From a494bd244e29ad042742a89b022624359462c5db Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Mon, 26 Aug 2024 23:59:21 +0200
Subject: [PATCH 01/69] Add initial setup

---
 .pre-commit-config.yaml            |   19 +
 poetry.lock                        | 1867 ----------------------------
 pyproject.toml                     |   52 -
 requirements/requirements.docs.txt |    2 +
 requirements/requirements.test.txt |    5 +
 requirements/requirements.txt      |    4 +
 setup.py                           |   66 +
 7 files changed, 96 insertions(+), 1919 deletions(-)
 create mode 100644 .pre-commit-config.yaml
 delete mode 100644 poetry.lock
 delete mode 100644 pyproject.toml
 create mode 100644 requirements/requirements.docs.txt
 create mode 100644 requirements/requirements.test.txt
 create mode 100644 requirements/requirements.txt
 create mode 100644 setup.py

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..ed3e8f3
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,19 @@
+repos:
+-   repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+    -   id: check-yaml
+    -   id: end-of-file-fixer
+    -   id: trailing-whitespace
+-   repo: https://github.com/psf/black
+    rev: 24.8.0
+    hooks:
+    -   id: black
+-   repo: https://github.com/pre-commit/mirrors-mypy
+    rev: v1.11.2
+    hooks:
+    -   id: mypy
+-   repo: https://github.com/PyCQA/flake8
+    rev: 7.1.1
+    hooks:
+    -   id: flake8
diff --git a/poetry.lock b/poetry.lock
deleted file mode 100644
index 732c059..0000000
--- a/poetry.lock
+++ /dev/null
@@ -1,1867 +0,0 @@
-# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand.
-
-[[package]]
-name = "babel"
-version = "2.13.1"
-description = "Internationalization utilities"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "Babel-2.13.1-py3-none-any.whl", hash = "sha256:7077a4984b02b6727ac10f1f7294484f737443d7e2e66c5e4380e41a3ae0b4ed"},
-    {file = "Babel-2.13.1.tar.gz", hash = "sha256:33e0952d7dd6374af8dbf6768cc4ddf3ccfefc244f9986d4074704f2fbd18900"},
-]
-
-[package.dependencies]
-pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""}
-
-[package.extras]
-dev = ["freezegun (>=1.0,<2.0)", "pytest (>=6.0)", "pytest-cov"]
-
-[[package]]
-name = "certifi"
-version = "2023.11.17"
-description = "Python package for providing Mozilla's CA Bundle."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "certifi-2023.11.17-py3-none-any.whl", hash = "sha256:e036ab49d5b79556f99cfc2d9320b34cfbe5be05c5871b51de9329f0603b0474"},
-    {file = "certifi-2023.11.17.tar.gz", hash = "sha256:9b469f3a900bf28dc19b8cfbf8019bf47f7fdd1a65a1d4ffb98fc14166beb4d1"},
-]
-
-[[package]]
-name = "charset-normalizer"
-version = "3.3.2"
-description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-optional = false
-python-versions = ">=3.7.0"
-files = [
-    {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-win32.whl", hash = "sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73"},
-    {file = "charset_normalizer-3.3.2-cp310-cp310-win_amd64.whl", hash = "sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:573f6eac48f4769d667c4442081b1794f52919e7edada77495aaed9236d13a96"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:549a3a73da901d5bc3ce8d24e0600d1fa85524c10287f6004fbab87672bf3e1e"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f27273b60488abe721a075bcca6d7f3964f9f6f067c8c4c605743023d7d3944f"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1ceae2f17a9c33cb48e3263960dc5fc8005351ee19db217e9b1bb15d28c02574"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:65f6f63034100ead094b8744b3b97965785388f308a64cf8d7c34f2f2e5be0c4"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:753f10e867343b4511128c6ed8c82f7bec3bd026875576dfd88483c5c73b2fd8"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4a78b2b446bd7c934f5dcedc588903fb2f5eec172f3d29e52a9096a43722adfc"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e537484df0d8f426ce2afb2d0f8e1c3d0b114b83f8850e5f2fbea0e797bd82ae"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:eb6904c354526e758fda7167b33005998fb68c46fbc10e013ca97f21ca5c8887"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:deb6be0ac38ece9ba87dea880e438f25ca3eddfac8b002a2ec3d9183a454e8ae"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4ab2fe47fae9e0f9dee8c04187ce5d09f48eabe611be8259444906793ab7cbce"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:80402cd6ee291dcb72644d6eac93785fe2c8b9cb30893c1af5b8fdd753b9d40f"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-win32.whl", hash = "sha256:7cd13a2e3ddeed6913a65e66e94b51d80a041145a026c27e6bb76c31a853c6ab"},
-    {file = "charset_normalizer-3.3.2-cp311-cp311-win_amd64.whl", hash = "sha256:663946639d296df6a2bb2aa51b60a2454ca1cb29835324c640dafb5ff2131a77"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:0b2b64d2bb6d3fb9112bafa732def486049e63de9618b5843bcdd081d8144cd8"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:ddbb2551d7e0102e7252db79ba445cdab71b26640817ab1e3e3648dad515003b"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:55086ee1064215781fff39a1af09518bc9255b50d6333f2e4c74ca09fac6a8f6"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f4a014bc36d3c57402e2977dada34f9c12300af536839dc38c0beab8878f38a"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a10af20b82360ab00827f916a6058451b723b4e65030c5a18577c8b2de5b3389"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:8d756e44e94489e49571086ef83b2bb8ce311e730092d2c34ca8f7d925cb20aa"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6ac7ffc7ad6d040517be39eb591cac5ff87416c2537df6ba3cba3bae290c0fed"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:7ed9e526742851e8d5cc9e6cf41427dfc6068d4f5a3bb03659444b4cabf6bc26"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:8bdb58ff7ba23002a4c5808d608e4e6c687175724f54a5dade5fa8c67b604e4d"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:6b3251890fff30ee142c44144871185dbe13b11bab478a88887a639655be1068"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b4a23f61ce87adf89be746c8a8974fe1c823c891d8f86eb218bb957c924bb143"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:efcb3f6676480691518c177e3b465bcddf57cea040302f9f4e6e191af91174d4"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-win32.whl", hash = "sha256:d965bba47ddeec8cd560687584e88cf699fd28f192ceb452d1d7ee807c5597b7"},
-    {file = "charset_normalizer-3.3.2-cp312-cp312-win_amd64.whl", hash = "sha256:96b02a3dc4381e5494fad39be677abcb5e6634bf7b4fa83a6dd3112607547001"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:95f2a5796329323b8f0512e09dbb7a1860c46a39da62ecb2324f116fa8fdc85c"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c002b4ffc0be611f0d9da932eb0f704fe2602a9a949d1f738e4c34c75b0863d5"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:a981a536974bbc7a512cf44ed14938cf01030a99e9b3a06dd59578882f06f985"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3287761bc4ee9e33561a7e058c72ac0938c4f57fe49a09eae428fd88aafe7bb6"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:42cb296636fcc8b0644486d15c12376cb9fa75443e00fb25de0b8602e64c1714"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0a55554a2fa0d408816b3b5cedf0045f4b8e1a6065aec45849de2d6f3f8e9786"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:c083af607d2515612056a31f0a8d9e0fcb5876b7bfc0abad3ecd275bc4ebc2d5"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:87d1351268731db79e0f8e745d92493ee2841c974128ef629dc518b937d9194c"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:bd8f7df7d12c2db9fab40bdd87a7c09b1530128315d047a086fa3ae3435cb3a8"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:c180f51afb394e165eafe4ac2936a14bee3eb10debc9d9e4db8958fe36afe711"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:8c622a5fe39a48f78944a87d4fb8a53ee07344641b0562c540d840748571b811"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-win32.whl", hash = "sha256:db364eca23f876da6f9e16c9da0df51aa4f104a972735574842618b8c6d999d4"},
-    {file = "charset_normalizer-3.3.2-cp37-cp37m-win_amd64.whl", hash = "sha256:86216b5cee4b06df986d214f664305142d9c76df9b6512be2738aa72a2048f99"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:6463effa3186ea09411d50efc7d85360b38d5f09b870c48e4600f63af490e56a"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:6c4caeef8fa63d06bd437cd4bdcf3ffefe6738fb1b25951440d80dc7df8c03ac"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:37e55c8e51c236f95b033f6fb391d7d7970ba5fe7ff453dad675e88cf303377a"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:fb69256e180cb6c8a894fee62b3afebae785babc1ee98b81cdf68bbca1987f33"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ae5f4161f18c61806f411a13b0310bea87f987c7d2ecdbdaad0e94eb2e404238"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b2b0a0c0517616b6869869f8c581d4eb2dd83a4d79e0ebcb7d373ef9956aeb0a"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:45485e01ff4d3630ec0d9617310448a8702f70e9c01906b0d0118bdf9d124cf2"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:eb00ed941194665c332bf8e078baf037d6c35d7c4f3102ea2d4f16ca94a26dc8"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:2127566c664442652f024c837091890cb1942c30937add288223dc895793f898"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:a50aebfa173e157099939b17f18600f72f84eed3049e743b68ad15bd69b6bf99"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:4d0d1650369165a14e14e1e47b372cfcb31d6ab44e6e33cb2d4e57265290044d"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:923c0c831b7cfcb071580d3f46c4baf50f174be571576556269530f4bbd79d04"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:06a81e93cd441c56a9b65d8e1d043daeb97a3d0856d177d5c90ba85acb3db087"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-win32.whl", hash = "sha256:6ef1d82a3af9d3eecdba2321dc1b3c238245d890843e040e41e470ffa64c3e25"},
-    {file = "charset_normalizer-3.3.2-cp38-cp38-win_amd64.whl", hash = "sha256:eb8821e09e916165e160797a6c17edda0679379a4be5c716c260e836e122f54b"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:c235ebd9baae02f1b77bcea61bce332cb4331dc3617d254df3323aa01ab47bd4"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:5b4c145409bef602a690e7cfad0a15a55c13320ff7a3ad7ca59c13bb8ba4d45d"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:68d1f8a9e9e37c1223b656399be5d6b448dea850bed7d0f87a8311f1ff3dabb0"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:22afcb9f253dac0696b5a4be4a1c0f8762f8239e21b99680099abd9b2b1b2269"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:e27ad930a842b4c5eb8ac0016b0a54f5aebbe679340c26101df33424142c143c"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:1f79682fbe303db92bc2b1136016a38a42e835d932bab5b3b1bfcfbf0640e519"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:122c7fa62b130ed55f8f285bfd56d5f4b4a5b503609d181f9ad85e55c89f4185"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:d0eccceffcb53201b5bfebb52600a5fb483a20b61da9dbc885f8b103cbe7598c"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:9f96df6923e21816da7e0ad3fd47dd8f94b2a5ce594e00677c0013018b813458"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:7f04c839ed0b6b98b1a7501a002144b76c18fb1c1850c8b98d458ac269e26ed2"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:34d1c8da1e78d2e001f363791c98a272bb734000fcef47a491c1e3b0505657a8"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ff8fa367d09b717b2a17a052544193ad76cd49979c805768879cb63d9ca50561"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-win32.whl", hash = "sha256:aed38f6e4fb3f5d6bf81bfa990a07806be9d83cf7bacef998ab1a9bd660a581f"},
-    {file = "charset_normalizer-3.3.2-cp39-cp39-win_amd64.whl", hash = "sha256:b01b88d45a6fcb69667cd6d2f7a9aeb4bf53760d7fc536bf679ec94fe9f3ff3d"},
-    {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
-]
-
-[[package]]
-name = "click"
-version = "8.1.7"
-description = "Composable command line interface toolkit"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
-    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "platform_system == \"Windows\""}
-
-[[package]]
-name = "colorama"
-version = "0.4.6"
-description = "Cross-platform colored terminal text."
-optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
-files = [
-    {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
-    {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
-]
-
-[[package]]
-name = "contourpy"
-version = "1.1.1"
-description = "Python library for calculating contours of 2D quadrilateral grids"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "contourpy-1.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:46e24f5412c948d81736509377e255f6040e94216bf1a9b5ea1eaa9d29f6ec1b"},
-    {file = "contourpy-1.1.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:0e48694d6a9c5a26ee85b10130c77a011a4fedf50a7279fa0bdaf44bafb4299d"},
-    {file = "contourpy-1.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a66045af6cf00e19d02191ab578a50cb93b2028c3eefed999793698e9ea768ae"},
-    {file = "contourpy-1.1.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:4ebf42695f75ee1a952f98ce9775c873e4971732a87334b099dde90b6af6a916"},
-    {file = "contourpy-1.1.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f6aec19457617ef468ff091669cca01fa7ea557b12b59a7908b9474bb9674cf0"},
-    {file = "contourpy-1.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:462c59914dc6d81e0b11f37e560b8a7c2dbab6aca4f38be31519d442d6cde1a1"},
-    {file = "contourpy-1.1.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:6d0a8efc258659edc5299f9ef32d8d81de8b53b45d67bf4bfa3067f31366764d"},
-    {file = "contourpy-1.1.1-cp310-cp310-win32.whl", hash = "sha256:d6ab42f223e58b7dac1bb0af32194a7b9311065583cc75ff59dcf301afd8a431"},
-    {file = "contourpy-1.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:549174b0713d49871c6dee90a4b499d3f12f5e5f69641cd23c50a4542e2ca1eb"},
-    {file = "contourpy-1.1.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:407d864db716a067cc696d61fa1ef6637fedf03606e8417fe2aeed20a061e6b2"},
-    {file = "contourpy-1.1.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:dfe80c017973e6a4c367e037cb31601044dd55e6bfacd57370674867d15a899b"},
-    {file = "contourpy-1.1.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e30aaf2b8a2bac57eb7e1650df1b3a4130e8d0c66fc2f861039d507a11760e1b"},
-    {file = "contourpy-1.1.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3de23ca4f381c3770dee6d10ead6fff524d540c0f662e763ad1530bde5112532"},
-    {file = "contourpy-1.1.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:566f0e41df06dfef2431defcfaa155f0acfa1ca4acbf8fd80895b1e7e2ada40e"},
-    {file = "contourpy-1.1.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b04c2f0adaf255bf756cf08ebef1be132d3c7a06fe6f9877d55640c5e60c72c5"},
-    {file = "contourpy-1.1.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:d0c188ae66b772d9d61d43c6030500344c13e3f73a00d1dc241da896f379bb62"},
-    {file = "contourpy-1.1.1-cp311-cp311-win32.whl", hash = "sha256:0683e1ae20dc038075d92e0e0148f09ffcefab120e57f6b4c9c0f477ec171f33"},
-    {file = "contourpy-1.1.1-cp311-cp311-win_amd64.whl", hash = "sha256:8636cd2fc5da0fb102a2504fa2c4bea3cbc149533b345d72cdf0e7a924decc45"},
-    {file = "contourpy-1.1.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:560f1d68a33e89c62da5da4077ba98137a5e4d3a271b29f2f195d0fba2adcb6a"},
-    {file = "contourpy-1.1.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:24216552104ae8f3b34120ef84825400b16eb6133af2e27a190fdc13529f023e"},
-    {file = "contourpy-1.1.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:56de98a2fb23025882a18b60c7f0ea2d2d70bbbcfcf878f9067234b1c4818442"},
-    {file = "contourpy-1.1.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:07d6f11dfaf80a84c97f1a5ba50d129d9303c5b4206f776e94037332e298dda8"},
-    {file = "contourpy-1.1.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:f1eaac5257a8f8a047248d60e8f9315c6cff58f7803971170d952555ef6344a7"},
-    {file = "contourpy-1.1.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:19557fa407e70f20bfaba7d55b4d97b14f9480856c4fb65812e8a05fe1c6f9bf"},
-    {file = "contourpy-1.1.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:081f3c0880712e40effc5f4c3b08feca6d064cb8cfbb372ca548105b86fd6c3d"},
-    {file = "contourpy-1.1.1-cp312-cp312-win32.whl", hash = "sha256:059c3d2a94b930f4dafe8105bcdc1b21de99b30b51b5bce74c753686de858cb6"},
-    {file = "contourpy-1.1.1-cp312-cp312-win_amd64.whl", hash = "sha256:f44d78b61740e4e8c71db1cf1fd56d9050a4747681c59ec1094750a658ceb970"},
-    {file = "contourpy-1.1.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:70e5a10f8093d228bb2b552beeb318b8928b8a94763ef03b858ef3612b29395d"},
-    {file = "contourpy-1.1.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:8394e652925a18ef0091115e3cc191fef350ab6dc3cc417f06da66bf98071ae9"},
-    {file = "contourpy-1.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c5bd5680f844c3ff0008523a71949a3ff5e4953eb7701b28760805bc9bcff217"},
-    {file = "contourpy-1.1.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:66544f853bfa85c0d07a68f6c648b2ec81dafd30f272565c37ab47a33b220684"},
-    {file = "contourpy-1.1.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0c02b75acfea5cab07585d25069207e478d12309557f90a61b5a3b4f77f46ce"},
-    {file = "contourpy-1.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41339b24471c58dc1499e56783fedc1afa4bb018bcd035cfb0ee2ad2a7501ef8"},
-    {file = "contourpy-1.1.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:f29fb0b3f1217dfe9362ec55440d0743fe868497359f2cf93293f4b2701b8251"},
-    {file = "contourpy-1.1.1-cp38-cp38-win32.whl", hash = "sha256:f9dc7f933975367251c1b34da882c4f0e0b2e24bb35dc906d2f598a40b72bfc7"},
-    {file = "contourpy-1.1.1-cp38-cp38-win_amd64.whl", hash = "sha256:498e53573e8b94b1caeb9e62d7c2d053c263ebb6aa259c81050766beb50ff8d9"},
-    {file = "contourpy-1.1.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:ba42e3810999a0ddd0439e6e5dbf6d034055cdc72b7c5c839f37a7c274cb4eba"},
-    {file = "contourpy-1.1.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6c06e4c6e234fcc65435223c7b2a90f286b7f1b2733058bdf1345d218cc59e34"},
-    {file = "contourpy-1.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca6fab080484e419528e98624fb5c4282148b847e3602dc8dbe0cb0669469887"},
-    {file = "contourpy-1.1.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:93df44ab351119d14cd1e6b52a5063d3336f0754b72736cc63db59307dabb718"},
-    {file = "contourpy-1.1.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:eafbef886566dc1047d7b3d4b14db0d5b7deb99638d8e1be4e23a7c7ac59ff0f"},
-    {file = "contourpy-1.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:efe0fab26d598e1ec07d72cf03eaeeba8e42b4ecf6b9ccb5a356fde60ff08b85"},
-    {file = "contourpy-1.1.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:f08e469821a5e4751c97fcd34bcb586bc243c39c2e39321822060ba902eac49e"},
-    {file = "contourpy-1.1.1-cp39-cp39-win32.whl", hash = "sha256:bfc8a5e9238232a45ebc5cb3bfee71f1167064c8d382cadd6076f0d51cff1da0"},
-    {file = "contourpy-1.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:c84fdf3da00c2827d634de4fcf17e3e067490c4aea82833625c4c8e6cdea0887"},
-    {file = "contourpy-1.1.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:229a25f68046c5cf8067d6d6351c8b99e40da11b04d8416bf8d2b1d75922521e"},
-    {file = "contourpy-1.1.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a10dab5ea1bd4401c9483450b5b0ba5416be799bbd50fc7a6cc5e2a15e03e8a3"},
-    {file = "contourpy-1.1.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:4f9147051cb8fdb29a51dc2482d792b3b23e50f8f57e3720ca2e3d438b7adf23"},
-    {file = "contourpy-1.1.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a75cc163a5f4531a256f2c523bd80db509a49fc23721b36dd1ef2f60ff41c3cb"},
-    {file = "contourpy-1.1.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b53d5769aa1f2d4ea407c65f2d1d08002952fac1d9e9d307aa2e1023554a163"},
-    {file = "contourpy-1.1.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:11b836b7dbfb74e049c302bbf74b4b8f6cb9d0b6ca1bf86cfa8ba144aedadd9c"},
-    {file = "contourpy-1.1.1.tar.gz", hash = "sha256:96ba37c2e24b7212a77da85004c38e7c4d155d3e72a45eeaf22c1f03f607e8ab"},
-]
-
-[package.dependencies]
-numpy = {version = ">=1.16,<2.0", markers = "python_version <= \"3.11\""}
-
-[package.extras]
-bokeh = ["bokeh", "selenium"]
-docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
-mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.4.1)", "types-Pillow"]
-test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
-test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
-
-[[package]]
-name = "cycler"
-version = "0.12.1"
-description = "Composable style cycles"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
-    {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
-]
-
-[package.extras]
-docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
-tests = ["pytest", "pytest-cov", "pytest-xdist"]
-
-[[package]]
-name = "exceptiongroup"
-version = "1.2.0"
-description = "Backport of PEP 654 (exception groups)"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "exceptiongroup-1.2.0-py3-none-any.whl", hash = "sha256:4bfd3996ac73b41e9b9628b04e079f193850720ea5945fc96a08633c66912f14"},
-    {file = "exceptiongroup-1.2.0.tar.gz", hash = "sha256:91f5c769735f051a4290d52edd0858999b57e5876e9f85937691bd4c9fa3ed68"},
-]
-
-[package.extras]
-test = ["pytest (>=6)"]
-
-[[package]]
-name = "filelock"
-version = "3.13.1"
-description = "A platform independent file lock."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"},
-    {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"},
-]
-
-[package.extras]
-docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"]
-testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"]
-typing = ["typing-extensions (>=4.8)"]
-
-[[package]]
-name = "fonttools"
-version = "4.45.1"
-description = "Tools to manipulate font files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "fonttools-4.45.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:45fa321c458ea29224067700954ec44493ae869b47e7c5485a350a149a19fb53"},
-    {file = "fonttools-4.45.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:0dc7617d96b1e668eea9250e1c1fe62d0c78c3f69573ce7e3332cc40e6d84356"},
-    {file = "fonttools-4.45.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:03ed3bda541e86725f6b4e1b94213f13ed1ae51a5a1f167028534cedea38c010"},
-    {file = "fonttools-4.45.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c4f4a5870e3b56788fb196da8cf30d0dfd51a76dc3b907861d018165f76ae4c2"},
-    {file = "fonttools-4.45.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:a3c11d9687479f01eddef729aa737abcdea0a44fdaffb62a930a18892f186c9b"},
-    {file = "fonttools-4.45.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:316cec50581e844c3ab69d7c82455b54c7cf18236b2f09e722faf665fbfcac58"},
-    {file = "fonttools-4.45.1-cp310-cp310-win32.whl", hash = "sha256:e2277cba9f0b525e30de2a9ad3cb4219aa4bc697230c1645666b0deee9f914f0"},
-    {file = "fonttools-4.45.1-cp310-cp310-win_amd64.whl", hash = "sha256:1b9e9ad2bcded9a1431afaa57c8d3c39143ac1f050862d66bddd863c515464a2"},
-    {file = "fonttools-4.45.1-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:ff6a698bdd435d24c379f6e8a54908cd9bb7dda23719084d56bf8c87709bf3bd"},
-    {file = "fonttools-4.45.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:2c980d60cd6ec1376206fe55013d166e5627ad0b149b5c81e74eaa913ab6134f"},
-    {file = "fonttools-4.45.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a12dee6523c02ca78aeedd0a5e12bfa9b7b29896350edd5241542897b072ae23"},
-    {file = "fonttools-4.45.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:37cd1ced6efb3dd6fe82e9f9bf92fd74ac58a5aefc284045f59ecd517a5fb9ab"},
-    {file = "fonttools-4.45.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e3d24248221bd7151dfff0d88b1b5da02dccd7134bd576ce8888199827bbaa19"},
-    {file = "fonttools-4.45.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:ba6c23591427844dfb0a13658f1718489de75de6a46b64234584c0d17573162d"},
-    {file = "fonttools-4.45.1-cp311-cp311-win32.whl", hash = "sha256:cebcddbe9351b67166292b4f71ffdbfcce01ba4b07d4267824eb46b277aeb19a"},
-    {file = "fonttools-4.45.1-cp311-cp311-win_amd64.whl", hash = "sha256:f22eb69996a0bd49f76bdefb30be54ce8dbb89a0d1246874d610f05c2aa2e69e"},
-    {file = "fonttools-4.45.1-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:794de93e83297db7b4943f2431e206d8b1ea69cb3ae14638a49cc50332bf0db8"},
-    {file = "fonttools-4.45.1-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:4ba17822a6681d06849078daaf6e03eccc9f467efe7c4c60280e28a78e8e5df9"},
-    {file = "fonttools-4.45.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e50f794d09df0675da8d9dbd7c66bfcab2f74a708343aabcad41936d26556891"},
-    {file = "fonttools-4.45.1-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8b07b857d4f9de3199a8c3d1b1bf2078c0f37447891ca1a8d9234106b9a27aff"},
-    {file = "fonttools-4.45.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:777ba42b94a27bb7fb2b4082522fccfd345667c32a56011e1c3e105979af5b79"},
-    {file = "fonttools-4.45.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:21e96b99878348c74aa58059b8578d7586f9519cbcdadacf56486737038aa043"},
-    {file = "fonttools-4.45.1-cp312-cp312-win32.whl", hash = "sha256:5cbf02cda8465b69769d07385f5d11e7bba19954e7787792f46fe679ec755ebb"},
-    {file = "fonttools-4.45.1-cp312-cp312-win_amd64.whl", hash = "sha256:800e354e0c3afaeb8d9552769773d02f228e98c37b8cb03041157c3d0687cffc"},
-    {file = "fonttools-4.45.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:6eb2c54f7a07c92108daabcf02caf31df97825738db02a28270633946bcda4d0"},
-    {file = "fonttools-4.45.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:43a3d267334109ff849c37cf3629476b5feb392ef1d2e464a167b83de8cd599c"},
-    {file = "fonttools-4.45.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8e1aefc2bf3c43e0f33f995f828a7bbeff4adc9393a7760b11456dbcf14388f6"},
-    {file = "fonttools-4.45.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1f53a19dcdd5737440839b8394eeebb35da9ec8109f7926cb6456639b5b58e47"},
-    {file = "fonttools-4.45.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:5a17706b9cc24b27721613fe5773d93331ab7f0ecaca9955aead89c6b843d3a7"},
-    {file = "fonttools-4.45.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:fb36e5f40191274a95938b40c0a1fa7f895e36935aea8709e1d6deff0b2d0d4f"},
-    {file = "fonttools-4.45.1-cp38-cp38-win32.whl", hash = "sha256:46eabddec12066829b8a1efe45ae552ba2f1796981ecf538d5f68284c354c589"},
-    {file = "fonttools-4.45.1-cp38-cp38-win_amd64.whl", hash = "sha256:b6de2f0fcd3302fb82f94801002cb473959e998c14c24ec28234adb674aed345"},
-    {file = "fonttools-4.45.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:392d0e3cc23daee910193625f7cf1b387aff9dd5b6f1a5f4a925680acb6dcbc2"},
-    {file = "fonttools-4.45.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:4b9544b1346d99848ac0e9b05b5d45ee703d7562fc4c9c48cf4b781de9632e57"},
-    {file = "fonttools-4.45.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8717db3e4895e4820ade64ea379187738827ee60748223cb0438ef044ee208c6"},
-    {file = "fonttools-4.45.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e29d5f298d616a93a4c5963682dc6cc8cc09f6d89cad2c29019fc5fb3b4d9472"},
-    {file = "fonttools-4.45.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:cb472905da3049960e80fc1cf808231880d79727a8410e156bf3e5063a1c574f"},
-    {file = "fonttools-4.45.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:ba299f1fbaa2a1e33210aaaf6fa816d4059e4d3cfe2ae9871368d4ab548c1c6a"},
-    {file = "fonttools-4.45.1-cp39-cp39-win32.whl", hash = "sha256:105099968b58a5b4cef6f3eb409db8ea8578b302a9d05e23fecba1b8b0177b5f"},
-    {file = "fonttools-4.45.1-cp39-cp39-win_amd64.whl", hash = "sha256:847f3f49dd3423e5a678c098e2ba92c7f4955d4aab3044f6a507b0bb0ecb07e0"},
-    {file = "fonttools-4.45.1-py3-none-any.whl", hash = "sha256:3bdd7dfca8f6c9f4779384064027e8477ad6a037d6a327b09381f43e0247c6f3"},
-    {file = "fonttools-4.45.1.tar.gz", hash = "sha256:6e441286d55fe7ec7c4fb36812bf914924813776ff514b744b510680fc2733f2"},
-]
-
-[package.extras]
-all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"]
-graphite = ["lz4 (>=1.7.4.2)"]
-interpolatable = ["munkres", "scipy"]
-lxml = ["lxml (>=4.0,<5)"]
-pathops = ["skia-pathops (>=0.5.0)"]
-plot = ["matplotlib"]
-repacker = ["uharfbuzz (>=0.23.0)"]
-symfont = ["sympy"]
-type1 = ["xattr"]
-ufo = ["fs (>=2.2.0,<3)"]
-unicode = ["unicodedata2 (>=15.1.0)"]
-woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"]
-
-[[package]]
-name = "fsspec"
-version = "2023.10.0"
-description = "File-system specification"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "fsspec-2023.10.0-py3-none-any.whl", hash = "sha256:346a8f024efeb749d2a5fca7ba8854474b1ff9af7c3faaf636a4548781136529"},
-    {file = "fsspec-2023.10.0.tar.gz", hash = "sha256:330c66757591df346ad3091a53bd907e15348c2ba17d63fd54f5c39c4457d2a5"},
-]
-
-[package.extras]
-abfs = ["adlfs"]
-adl = ["adlfs"]
-arrow = ["pyarrow (>=1)"]
-dask = ["dask", "distributed"]
-devel = ["pytest", "pytest-cov"]
-dropbox = ["dropbox", "dropboxdrivefs", "requests"]
-full = ["adlfs", "aiohttp (!=4.0.0a0,!=4.0.0a1)", "dask", "distributed", "dropbox", "dropboxdrivefs", "fusepy", "gcsfs", "libarchive-c", "ocifs", "panel", "paramiko", "pyarrow (>=1)", "pygit2", "requests", "s3fs", "smbprotocol", "tqdm"]
-fuse = ["fusepy"]
-gcs = ["gcsfs"]
-git = ["pygit2"]
-github = ["requests"]
-gs = ["gcsfs"]
-gui = ["panel"]
-hdfs = ["pyarrow (>=1)"]
-http = ["aiohttp (!=4.0.0a0,!=4.0.0a1)", "requests"]
-libarchive = ["libarchive-c"]
-oci = ["ocifs"]
-s3 = ["s3fs"]
-sftp = ["paramiko"]
-smb = ["smbprotocol"]
-ssh = ["paramiko"]
-tqdm = ["tqdm"]
-
-[[package]]
-name = "ghp-import"
-version = "2.1.0"
-description = "Copy your docs directly to the gh-pages branch."
-optional = false
-python-versions = "*"
-files = [
-    {file = "ghp-import-2.1.0.tar.gz", hash = "sha256:9c535c4c61193c2df8871222567d7fd7e5014d835f97dc7b7439069e2413d343"},
-    {file = "ghp_import-2.1.0-py3-none-any.whl", hash = "sha256:8337dd7b50877f163d4c0289bc1f1c7f127550241988d568c1db512c4324a619"},
-]
-
-[package.dependencies]
-python-dateutil = ">=2.8.1"
-
-[package.extras]
-dev = ["flake8", "markdown", "twine", "wheel"]
-
-[[package]]
-name = "griffe"
-version = "0.38.0"
-description = "Signatures for entire Python programs. Extract the structure, the frame, the skeleton of your project, to generate API documentation or find breaking changes in your API."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "griffe-0.38.0-py3-none-any.whl", hash = "sha256:6a5bc457320e8e199006aa5fbb03e162f5e21abe31aa6221f7a5c37ea0724c71"},
-    {file = "griffe-0.38.0.tar.gz", hash = "sha256:9b97487b583042b543d1e28196caee638ecd766c8c4c98135071806cb5333ac2"},
-]
-
-[package.dependencies]
-colorama = ">=0.4"
-
-[[package]]
-name = "huggingface-hub"
-version = "0.19.4"
-description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "huggingface_hub-0.19.4-py3-none-any.whl", hash = "sha256:dba013f779da16f14b606492828f3760600a1e1801432d09fe1c33e50b825bb5"},
-    {file = "huggingface_hub-0.19.4.tar.gz", hash = "sha256:176a4fc355a851c17550e7619488f383189727eab209534d7cef2114dae77b22"},
-]
-
-[package.dependencies]
-filelock = "*"
-fsspec = ">=2023.5.0"
-packaging = ">=20.9"
-pyyaml = ">=5.1"
-requests = "*"
-tqdm = ">=4.42.1"
-typing-extensions = ">=3.7.4.3"
-
-[package.extras]
-all = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
-cli = ["InquirerPy (==0.3.4)"]
-dev = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)"]
-docs = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "hf-doc-builder", "jedi", "mypy (==1.5.1)", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "ruff (>=0.1.3)", "soundfile", "types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)", "urllib3 (<2.0)", "watchdog"]
-fastai = ["fastai (>=2.4)", "fastcore (>=1.3.27)", "toml"]
-inference = ["aiohttp", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)"]
-quality = ["mypy (==1.5.1)", "ruff (>=0.1.3)"]
-tensorflow = ["graphviz", "pydot", "tensorflow"]
-testing = ["InquirerPy (==0.3.4)", "Jinja2", "Pillow", "aiohttp", "gradio", "jedi", "numpy", "pydantic (>1.1,<2.0)", "pydantic (>1.1,<3.0)", "pytest", "pytest-asyncio", "pytest-cov", "pytest-env", "pytest-vcr", "pytest-xdist", "soundfile", "urllib3 (<2.0)"]
-torch = ["torch"]
-typing = ["types-PyYAML", "types-requests", "types-simplejson", "types-toml", "types-tqdm", "types-urllib3", "typing-extensions (>=4.8.0)"]
-
-[[package]]
-name = "idna"
-version = "3.6"
-description = "Internationalized Domain Names in Applications (IDNA)"
-optional = false
-python-versions = ">=3.5"
-files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
-]
-
-[[package]]
-name = "importlib-metadata"
-version = "6.8.0"
-description = "Read metadata from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "importlib_metadata-6.8.0-py3-none-any.whl", hash = "sha256:3ebb78df84a805d7698245025b975d9d67053cd94c79245ba4b3eb694abe68bb"},
-    {file = "importlib_metadata-6.8.0.tar.gz", hash = "sha256:dbace7892d8c0c4ac1ad096662232f831d4e64f4c4545bd53016a3e9d4654743"},
-]
-
-[package.dependencies]
-zipp = ">=0.5"
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
-
-[[package]]
-name = "importlib-resources"
-version = "6.1.1"
-description = "Read resources from Python packages"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "importlib_resources-6.1.1-py3-none-any.whl", hash = "sha256:e8bf90d8213b486f428c9c39714b920041cb02c184686a3dee24905aaa8105d6"},
-    {file = "importlib_resources-6.1.1.tar.gz", hash = "sha256:3893a00122eafde6894c59914446a512f728a0c1a45f9bb9b63721b6bacf0b4a"},
-]
-
-[package.dependencies]
-zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-ruff", "zipp (>=3.17)"]
-
-[[package]]
-name = "iniconfig"
-version = "2.0.0"
-description = "brain-dead simple config-ini parsing"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"},
-    {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
-]
-
-[[package]]
-name = "jinja2"
-version = "3.1.2"
-description = "A very fast and expressive template engine."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "Jinja2-3.1.2-py3-none-any.whl", hash = "sha256:6088930bfe239f0e6710546ab9c19c9ef35e29792895fed6e6e31a023a182a61"},
-    {file = "Jinja2-3.1.2.tar.gz", hash = "sha256:31351a702a408a9e7595a8fc6150fc3f43bb6bf7e319770cbc0db9df9437e852"},
-]
-
-[package.dependencies]
-MarkupSafe = ">=2.0"
-
-[package.extras]
-i18n = ["Babel (>=2.7)"]
-
-[[package]]
-name = "kiwisolver"
-version = "1.4.5"
-description = "A fast implementation of the Cassowary constraint solver"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6ef7afcd2d281494c0a9101d5c571970708ad911d028137cd558f02b851c08b4"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:9eaa8b117dc8337728e834b9c6e2611f10c79e38f65157c4c38e9400286f5cb1"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:ec20916e7b4cbfb1f12380e46486ec4bcbaa91a9c448b97023fde0d5bbf9e4ff"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39b42c68602539407884cf70d6a480a469b93b81b7701378ba5e2328660c847a"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aa12042de0171fad672b6c59df69106d20d5596e4f87b5e8f76df757a7c399aa"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:2a40773c71d7ccdd3798f6489aaac9eee213d566850a9533f8d26332d626b82c"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:19df6e621f6d8b4b9c4d45f40a66839294ff2bb235e64d2178f7522d9170ac5b"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:83d78376d0d4fd884e2c114d0621624b73d2aba4e2788182d286309ebdeed770"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:e391b1f0a8a5a10ab3b9bb6afcfd74f2175f24f8975fb87ecae700d1503cdee0"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:852542f9481f4a62dbb5dd99e8ab7aedfeb8fb6342349a181d4036877410f525"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:59edc41b24031bc25108e210c0def6f6c2191210492a972d585a06ff246bb79b"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-win32.whl", hash = "sha256:a6aa6315319a052b4ee378aa171959c898a6183f15c1e541821c5c59beaa0238"},
-    {file = "kiwisolver-1.4.5-cp310-cp310-win_amd64.whl", hash = "sha256:d0ef46024e6a3d79c01ff13801cb19d0cad7fd859b15037aec74315540acc276"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:11863aa14a51fd6ec28688d76f1735f8f69ab1fabf388851a595d0721af042f5"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:8ab3919a9997ab7ef2fbbed0cc99bb28d3c13e6d4b1ad36e97e482558a91be90"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fcc700eadbbccbf6bc1bcb9dbe0786b4b1cb91ca0dcda336eef5c2beed37b797"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dfdd7c0b105af050eb3d64997809dc21da247cf44e63dc73ff0fd20b96be55a9"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76c6a5964640638cdeaa0c359382e5703e9293030fe730018ca06bc2010c4437"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bbea0db94288e29afcc4c28afbf3a7ccaf2d7e027489c449cf7e8f83c6346eb9"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ceec1a6bc6cab1d6ff5d06592a91a692f90ec7505d6463a88a52cc0eb58545da"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:040c1aebeda72197ef477a906782b5ab0d387642e93bda547336b8957c61022e"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f91de7223d4c7b793867797bacd1ee53bfe7359bd70d27b7b58a04efbb9436c8"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:faae4860798c31530dd184046a900e652c95513796ef51a12bc086710c2eec4d"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:b0157420efcb803e71d1b28e2c287518b8808b7cf1ab8af36718fd0a2c453eb0"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:06f54715b7737c2fecdbf140d1afb11a33d59508a47bf11bb38ecf21dc9ab79f"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:fdb7adb641a0d13bdcd4ef48e062363d8a9ad4a182ac7647ec88f695e719ae9f"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-win32.whl", hash = "sha256:bb86433b1cfe686da83ce32a9d3a8dd308e85c76b60896d58f082136f10bffac"},
-    {file = "kiwisolver-1.4.5-cp311-cp311-win_amd64.whl", hash = "sha256:6c08e1312a9cf1074d17b17728d3dfce2a5125b2d791527f33ffbe805200a355"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:32d5cf40c4f7c7b3ca500f8985eb3fb3a7dfc023215e876f207956b5ea26632a"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:f846c260f483d1fd217fe5ed7c173fb109efa6b1fc8381c8b7552c5781756192"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:5ff5cf3571589b6d13bfbfd6bcd7a3f659e42f96b5fd1c4830c4cf21d4f5ef45"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7269d9e5f1084a653d575c7ec012ff57f0c042258bf5db0954bf551c158466e7"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:da802a19d6e15dffe4b0c24b38b3af68e6c1a68e6e1d8f30148c83864f3881db"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3aba7311af82e335dd1e36ffff68aaca609ca6290c2cb6d821a39aa075d8e3ff"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:763773d53f07244148ccac5b084da5adb90bfaee39c197554f01b286cf869228"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2270953c0d8cdab5d422bee7d2007f043473f9d2999631c86a223c9db56cbd16"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:d099e745a512f7e3bbe7249ca835f4d357c586d78d79ae8f1dcd4d8adeb9bda9"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:74db36e14a7d1ce0986fa104f7d5637aea5c82ca6326ed0ec5694280942d1162"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:7e5bab140c309cb3a6ce373a9e71eb7e4873c70c2dda01df6820474f9889d6d4"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:0f114aa76dc1b8f636d077979c0ac22e7cd8f3493abbab152f20eb8d3cda71f3"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:88a2df29d4724b9237fc0c6eaf2a1adae0cdc0b3e9f4d8e7dc54b16812d2d81a"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-win32.whl", hash = "sha256:72d40b33e834371fd330fb1472ca19d9b8327acb79a5821d4008391db8e29f20"},
-    {file = "kiwisolver-1.4.5-cp312-cp312-win_amd64.whl", hash = "sha256:2c5674c4e74d939b9d91dda0fae10597ac7521768fec9e399c70a1f27e2ea2d9"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:3a2b053a0ab7a3960c98725cfb0bf5b48ba82f64ec95fe06f1d06c99b552e130"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3cd32d6c13807e5c66a7cbb79f90b553642f296ae4518a60d8d76243b0ad2898"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:59ec7b7c7e1a61061850d53aaf8e93db63dce0c936db1fda2658b70e4a1be709"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:da4cfb373035def307905d05041c1d06d8936452fe89d464743ae7fb8371078b"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2400873bccc260b6ae184b2b8a4fec0e4082d30648eadb7c3d9a13405d861e89"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:1b04139c4236a0f3aff534479b58f6f849a8b351e1314826c2d230849ed48985"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:4e66e81a5779b65ac21764c295087de82235597a2293d18d943f8e9e32746265"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:7931d8f1f67c4be9ba1dd9c451fb0eeca1a25b89e4d3f89e828fe12a519b782a"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:b3f7e75f3015df442238cca659f8baa5f42ce2a8582727981cbfa15fee0ee205"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:bbf1d63eef84b2e8c89011b7f2235b1e0bf7dacc11cac9431fc6468e99ac77fb"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:4c380469bd3f970ef677bf2bcba2b6b0b4d5c75e7a020fb863ef75084efad66f"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-win32.whl", hash = "sha256:9408acf3270c4b6baad483865191e3e582b638b1654a007c62e3efe96f09a9a3"},
-    {file = "kiwisolver-1.4.5-cp37-cp37m-win_amd64.whl", hash = "sha256:5b94529f9b2591b7af5f3e0e730a4e0a41ea174af35a4fd067775f9bdfeee01a"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:11c7de8f692fc99816e8ac50d1d1aef4f75126eefc33ac79aac02c099fd3db71"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:53abb58632235cd154176ced1ae8f0d29a6657aa1aa9decf50b899b755bc2b93"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:88b9f257ca61b838b6f8094a62418421f87ac2a1069f7e896c36a7d86b5d4c29"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3195782b26fc03aa9c6913d5bad5aeb864bdc372924c093b0f1cebad603dd712"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fc579bf0f502e54926519451b920e875f433aceb4624a3646b3252b5caa9e0b6"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5a580c91d686376f0f7c295357595c5a026e6cbc3d77b7c36e290201e7c11ecb"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:cfe6ab8da05c01ba6fbea630377b5da2cd9bcbc6338510116b01c1bc939a2c18"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:d2e5a98f0ec99beb3c10e13b387f8db39106d53993f498b295f0c914328b1333"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:a51a263952b1429e429ff236d2f5a21c5125437861baeed77f5e1cc2d2c7c6da"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:3edd2fa14e68c9be82c5b16689e8d63d89fe927e56debd6e1dbce7a26a17f81b"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:74d1b44c6cfc897df648cc9fdaa09bc3e7679926e6f96df05775d4fb3946571c"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:76d9289ed3f7501012e05abb8358bbb129149dbd173f1f57a1bf1c22d19ab7cc"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:92dea1ffe3714fa8eb6a314d2b3c773208d865a0e0d35e713ec54eea08a66250"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-win32.whl", hash = "sha256:5c90ae8c8d32e472be041e76f9d2f2dbff4d0b0be8bd4041770eddb18cf49a4e"},
-    {file = "kiwisolver-1.4.5-cp38-cp38-win_amd64.whl", hash = "sha256:c7940c1dc63eb37a67721b10d703247552416f719c4188c54e04334321351ced"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:9407b6a5f0d675e8a827ad8742e1d6b49d9c1a1da5d952a67d50ef5f4170b18d"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:15568384086b6df3c65353820a4473575dbad192e35010f622c6ce3eebd57af9"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:0dc9db8e79f0036e8173c466d21ef18e1befc02de8bf8aa8dc0813a6dc8a7046"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:cdc8a402aaee9a798b50d8b827d7ecf75edc5fb35ea0f91f213ff927c15f4ff0"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:6c3bd3cde54cafb87d74d8db50b909705c62b17c2099b8f2e25b461882e544ff"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:955e8513d07a283056b1396e9a57ceddbd272d9252c14f154d450d227606eb54"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:346f5343b9e3f00b8db8ba359350eb124b98c99efd0b408728ac6ebf38173958"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b9098e0049e88c6a24ff64545cdfc50807818ba6c1b739cae221bbbcbc58aad3"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:00bd361b903dc4bbf4eb165f24d1acbee754fce22ded24c3d56eec268658a5cf"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7b8b454bac16428b22560d0a1cf0a09875339cab69df61d7805bf48919415901"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:f1d072c2eb0ad60d4c183f3fb44ac6f73fb7a8f16a2694a91f988275cbf352f9"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:31a82d498054cac9f6d0b53d02bb85811185bcb477d4b60144f915f3b3126342"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:6512cb89e334e4700febbffaaa52761b65b4f5a3cf33f960213d5656cea36a77"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-win32.whl", hash = "sha256:9db8ea4c388fdb0f780fe91346fd438657ea602d58348753d9fb265ce1bca67f"},
-    {file = "kiwisolver-1.4.5-cp39-cp39-win_amd64.whl", hash = "sha256:59415f46a37f7f2efeec758353dd2eae1b07640d8ca0f0c42548ec4125492635"},
-    {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:5c7b3b3a728dc6faf3fc372ef24f21d1e3cee2ac3e9596691d746e5a536de920"},
-    {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:620ced262a86244e2be10a676b646f29c34537d0d9cc8eb26c08f53d98013390"},
-    {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:378a214a1e3bbf5ac4a8708304318b4f890da88c9e6a07699c4ae7174c09a68d"},
-    {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaf7be1207676ac608a50cd08f102f6742dbfc70e8d60c4db1c6897f62f71523"},
-    {file = "kiwisolver-1.4.5-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:ba55dce0a9b8ff59495ddd050a0225d58bd0983d09f87cfe2b6aec4f2c1234e4"},
-    {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:fd32ea360bcbb92d28933fc05ed09bffcb1704ba3fc7942e81db0fd4f81a7892"},
-    {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5e7139af55d1688f8b960ee9ad5adafc4ac17c1c473fe07133ac092310d76544"},
-    {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:dced8146011d2bc2e883f9bd68618b8247387f4bbec46d7392b3c3b032640126"},
-    {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c9bf3325c47b11b2e51bca0824ea217c7cd84491d8ac4eefd1e409705ef092bd"},
-    {file = "kiwisolver-1.4.5-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5794cf59533bc3f1b1c821f7206a3617999db9fbefc345360aafe2e067514929"},
-    {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:e368f200bbc2e4f905b8e71eb38b3c04333bddaa6a2464a6355487b02bb7fb09"},
-    {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e5d706eba36b4c4d5bc6c6377bb6568098765e990cfc21ee16d13963fab7b3e7"},
-    {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:85267bd1aa8880a9c88a8cb71e18d3d64d2751a790e6ca6c27b8ccc724bcd5ad"},
-    {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:210ef2c3a1f03272649aff1ef992df2e724748918c4bc2d5a90352849eb40bea"},
-    {file = "kiwisolver-1.4.5-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:11d011a7574eb3b82bcc9c1a1d35c1d7075677fdd15de527d91b46bd35e935ee"},
-    {file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
-]
-
-[[package]]
-name = "markdown"
-version = "3.5.1"
-description = "Python implementation of John Gruber's Markdown."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "Markdown-3.5.1-py3-none-any.whl", hash = "sha256:5874b47d4ee3f0b14d764324d2c94c03ea66bee56f2d929da9f2508d65e722dc"},
-    {file = "Markdown-3.5.1.tar.gz", hash = "sha256:b65d7beb248dc22f2e8a31fb706d93798093c308dc1aba295aedeb9d41a813bd"},
-]
-
-[package.dependencies]
-importlib-metadata = {version = ">=4.4", markers = "python_version < \"3.10\""}
-
-[package.extras]
-docs = ["mdx-gh-links (>=0.2)", "mkdocs (>=1.5)", "mkdocs-gen-files", "mkdocs-literate-nav", "mkdocs-nature (>=0.6)", "mkdocs-section-index", "mkdocstrings[python]"]
-testing = ["coverage", "pyyaml"]
-
-[[package]]
-name = "markupsafe"
-version = "2.1.3"
-description = "Safely add untrusted strings to HTML/XML markup."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "MarkupSafe-2.1.3-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:cd0f502fe016460680cd20aaa5a76d241d6f35a1c3350c474bac1273803893fa"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e09031c87a1e51556fdcb46e5bd4f59dfb743061cf93c4d6831bf894f125eb57"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:68e78619a61ecf91e76aa3e6e8e33fc4894a2bebe93410754bd28fce0a8a4f9f"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65c1a9bcdadc6c28eecee2c119465aebff8f7a584dd719facdd9e825ec61ab52"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:525808b8019e36eb524b8c68acdd63a37e75714eac50e988180b169d64480a00"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:962f82a3086483f5e5f64dbad880d31038b698494799b097bc59c2edf392fce6"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:aa7bd130efab1c280bed0f45501b7c8795f9fdbeb02e965371bbef3523627779"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:c9c804664ebe8f83a211cace637506669e7890fec1b4195b505c214e50dd4eb7"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-win32.whl", hash = "sha256:10bbfe99883db80bdbaff2dcf681dfc6533a614f700da1287707e8a5d78a8431"},
-    {file = "MarkupSafe-2.1.3-cp310-cp310-win_amd64.whl", hash = "sha256:1577735524cdad32f9f694208aa75e422adba74f1baee7551620e43a3141f559"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:ad9e82fb8f09ade1c3e1b996a6337afac2b8b9e365f926f5a61aacc71adc5b3c"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:3c0fae6c3be832a0a0473ac912810b2877c8cb9d76ca48de1ed31e1c68386575"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b076b6226fb84157e3f7c971a47ff3a679d837cf338547532ab866c57930dbee"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bfce63a9e7834b12b87c64d6b155fdd9b3b96191b6bd334bf37db7ff1fe457f2"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:338ae27d6b8745585f87218a3f23f1512dbf52c26c28e322dbe54bcede54ccb9"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:e4dd52d80b8c83fdce44e12478ad2e85c64ea965e75d66dbeafb0a3e77308fcc"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:df0be2b576a7abbf737b1575f048c23fb1d769f267ec4358296f31c2479db8f9"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5bbe06f8eeafd38e5d0a4894ffec89378b6c6a625ff57e3028921f8ff59318ac"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-win32.whl", hash = "sha256:dd15ff04ffd7e05ffcb7fe79f1b98041b8ea30ae9234aed2a9168b5797c3effb"},
-    {file = "MarkupSafe-2.1.3-cp311-cp311-win_amd64.whl", hash = "sha256:134da1eca9ec0ae528110ccc9e48041e0828d79f24121a1a146161103c76e686"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8e254ae696c88d98da6555f5ace2279cf7cd5b3f52be2b5cf97feafe883b58d2"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cb0932dc158471523c9637e807d9bfb93e06a95cbf010f1a38b98623b929ef2b"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9402b03f1a1b4dc4c19845e5c749e3ab82d5078d16a2a4c2cd2df62d57bb0707"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ca379055a47383d02a5400cb0d110cef0a776fc644cda797db0c5696cfd7e18e"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:b7ff0f54cb4ff66dd38bebd335a38e2c22c41a8ee45aa608efc890ac3e3931bc"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:c011a4149cfbcf9f03994ec2edffcb8b1dc2d2aede7ca243746df97a5d41ce48"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:56d9f2ecac662ca1611d183feb03a3fa4406469dafe241673d521dd5ae92a155"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-win32.whl", hash = "sha256:8758846a7e80910096950b67071243da3e5a20ed2546e6392603c096778d48e0"},
-    {file = "MarkupSafe-2.1.3-cp37-cp37m-win_amd64.whl", hash = "sha256:787003c0ddb00500e49a10f2844fac87aa6ce977b90b0feaaf9de23c22508b24"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:2ef12179d3a291be237280175b542c07a36e7f60718296278d8593d21ca937d4"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:2c1b19b3aaacc6e57b7e25710ff571c24d6c3613a45e905b1fde04d691b98ee0"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8afafd99945ead6e075b973fefa56379c5b5c53fd8937dad92c662da5d8fd5ee"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8c41976a29d078bb235fea9b2ecd3da465df42a562910f9022f1a03107bd02be"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d080e0a5eb2529460b30190fcfcc4199bd7f827663f858a226a81bc27beaa97e"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:69c0f17e9f5a7afdf2cc9fb2d1ce6aabdb3bafb7f38017c0b77862bcec2bbad8"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:504b320cd4b7eff6f968eddf81127112db685e81f7e36e75f9f84f0df46041c3"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:42de32b22b6b804f42c5d98be4f7e5e977ecdd9ee9b660fda1a3edf03b11792d"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-win32.whl", hash = "sha256:ceb01949af7121f9fc39f7d27f91be8546f3fb112c608bc4029aef0bab86a2a5"},
-    {file = "MarkupSafe-2.1.3-cp38-cp38-win_amd64.whl", hash = "sha256:1b40069d487e7edb2676d3fbdb2b0829ffa2cd63a2ec26c4938b2d34391b4ecc"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:8023faf4e01efadfa183e863fefde0046de576c6f14659e8782065bcece22198"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:6b2b56950d93e41f33b4223ead100ea0fe11f8e6ee5f641eb753ce4b77a7042b"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9dcdfd0eaf283af041973bff14a2e143b8bd64e069f4c383416ecd79a81aab58"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:05fb21170423db021895e1ea1e1f3ab3adb85d1c2333cbc2310f2a26bc77272e"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:282c2cb35b5b673bbcadb33a585408104df04f14b2d9b01d4c345a3b92861c2c"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:ab4a0df41e7c16a1392727727e7998a467472d0ad65f3ad5e6e765015df08636"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:7ef3cb2ebbf91e330e3bb937efada0edd9003683db6b57bb108c4001f37a02ea"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:0a4e4a1aff6c7ac4cd55792abf96c915634c2b97e3cc1c7129578aa68ebd754e"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-win32.whl", hash = "sha256:fec21693218efe39aa7f8599346e90c705afa52c5b31ae019b2e57e8f6542bb2"},
-    {file = "MarkupSafe-2.1.3-cp39-cp39-win_amd64.whl", hash = "sha256:3fd4abcb888d15a94f32b75d8fd18ee162ca0c064f35b11134be77050296d6ba"},
-    {file = "MarkupSafe-2.1.3.tar.gz", hash = "sha256:af598ed32d6ae86f1b747b82783958b1a4ab8f617b06fe68795c7f026abbdcad"},
-]
-
-[[package]]
-name = "matplotlib"
-version = "3.7.4"
-description = "Python plotting package"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "matplotlib-3.7.4-cp310-cp310-macosx_10_12_universal2.whl", hash = "sha256:b71079239bd866bf56df023e5146de159cb0c7294e508830901f4d79e2d89385"},
-    {file = "matplotlib-3.7.4-cp310-cp310-macosx_10_12_x86_64.whl", hash = "sha256:bf91a42f6274a64cb41189120b620c02e574535ff6671fa836cade7701b06fbd"},
-    {file = "matplotlib-3.7.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f757e8b42841d6add0cb69b42497667f0d25a404dcd50bd923ec9904e38414c4"},
-    {file = "matplotlib-3.7.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:e4dfee00aa4bd291e08bb9461831c26ce0da85ca9781bb8794f2025c6e925281"},
-    {file = "matplotlib-3.7.4-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3640f33632beb3993b698b1be9d1c262b742761d6101f3c27b87b2185d25c875"},
-    {file = "matplotlib-3.7.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ff539c4a17ecdf076ed808ee271ffae4a30dcb7e157b99ccae2c837262c07db6"},
-    {file = "matplotlib-3.7.4-cp310-cp310-win32.whl", hash = "sha256:24b8f28af3e766195c09b780b15aa9f6710192b415ae7866b9c03dee7ec86370"},
-    {file = "matplotlib-3.7.4-cp310-cp310-win_amd64.whl", hash = "sha256:3fa193286712c3b6c3cfa5fe8a6bb563f8c52cc750006c782296e0807ce5e799"},
-    {file = "matplotlib-3.7.4-cp311-cp311-macosx_10_12_universal2.whl", hash = "sha256:b167f54cb4654b210c9624ec7b54e2b3b8de68c93a14668937e7e53df60770ec"},
-    {file = "matplotlib-3.7.4-cp311-cp311-macosx_10_12_x86_64.whl", hash = "sha256:7dfe6821f1944cb35603ff22e21510941bbcce7ccf96095beffaac890d39ce77"},
-    {file = "matplotlib-3.7.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:3c557d9165320dff3c5f2bb99bfa0b6813d3e626423ff71c40d6bc23b83c3339"},
-    {file = "matplotlib-3.7.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:08372696b3bb45c563472a552a705bfa0942f0a8ffe084db8a4e8f9153fbdf9d"},
-    {file = "matplotlib-3.7.4-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:81e1a7ac818000e8ac3ca696c3fdc501bc2d3adc89005e7b4e22ee5e9d51de98"},
-    {file = "matplotlib-3.7.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:390920a3949906bc4b0216198d378f2a640c36c622e3584dd0c79a7c59ae9f50"},
-    {file = "matplotlib-3.7.4-cp311-cp311-win32.whl", hash = "sha256:62e094d8da26294634da9e7f1856beee3978752b1b530c8e1763d2faed60cc10"},
-    {file = "matplotlib-3.7.4-cp311-cp311-win_amd64.whl", hash = "sha256:f8fc2df756105784e650605e024d36dc2d048d68e5c1b26df97ee25d1bd41f9f"},
-    {file = "matplotlib-3.7.4-cp312-cp312-macosx_10_12_universal2.whl", hash = "sha256:568574756127791903604e315c11aef9f255151e4cfe20ec603a70f9dda8e259"},
-    {file = "matplotlib-3.7.4-cp312-cp312-macosx_10_12_x86_64.whl", hash = "sha256:7d479aac338195e2199a8cfc03c4f2f55914e6a120177edae79e0340a6406457"},
-    {file = "matplotlib-3.7.4-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:32183d4be84189a4c52b4b8861434d427d9118db2cec32986f98ed6c02dcfbb6"},
-    {file = "matplotlib-3.7.4-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0037d066cca1f4bda626c507cddeb6f7da8283bc6a214da2db13ff2162933c52"},
-    {file = "matplotlib-3.7.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:44856632ebce88abd8efdc0a0dceec600418dcac06b72ae77af0019d260aa243"},
-    {file = "matplotlib-3.7.4-cp312-cp312-win_amd64.whl", hash = "sha256:632fc938c22117d4241411191cfb88ac264a4c0a9ac702244641ddf30f0d739c"},
-    {file = "matplotlib-3.7.4-cp38-cp38-macosx_10_12_universal2.whl", hash = "sha256:ce163be048613b9d1962273708cc97e09ca05d37312e670d166cf332b80bbaff"},
-    {file = "matplotlib-3.7.4-cp38-cp38-macosx_10_12_x86_64.whl", hash = "sha256:e680f49bb8052ba3b2698e370155d2b4afb49f9af1cc611a26579d5981e2852a"},
-    {file = "matplotlib-3.7.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:0604880e4327114054199108b7390f987f4f40ee5ce728985836889e11a780ba"},
-    {file = "matplotlib-3.7.4-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:1e6abcde6fc52475f9d6a12b9f1792aee171ce7818ef6df5d61cb0b82816e6e8"},
-    {file = "matplotlib-3.7.4-cp38-cp38-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:f59a70e2ec3212033ef6633ed07682da03f5249379722512a3a2a26a7d9a738e"},
-    {file = "matplotlib-3.7.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7a9981b2a2dd9da06eca4ab5855d09b54b8ce7377c3e0e3957767b83219d652d"},
-    {file = "matplotlib-3.7.4-cp38-cp38-win32.whl", hash = "sha256:83859ac26839660ecd164ee8311272074250b915ac300f9b2eccc84410f8953b"},
-    {file = "matplotlib-3.7.4-cp38-cp38-win_amd64.whl", hash = "sha256:7a7709796ac59fe8debde68272388be6ed449c8971362eb5b60d280eac8dadde"},
-    {file = "matplotlib-3.7.4-cp39-cp39-macosx_10_12_universal2.whl", hash = "sha256:b1d70bc1ea1bf110bec64f4578de3e14947909a8887df4c1fd44492eca487955"},
-    {file = "matplotlib-3.7.4-cp39-cp39-macosx_10_12_x86_64.whl", hash = "sha256:c83f49e795a5de6c168876eea723f5b88355202f9603c55977f5356213aa8280"},
-    {file = "matplotlib-3.7.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:5c9133f230945fe10652eb33e43642e933896194ef6a4f8d5e79bb722bdb2000"},
-    {file = "matplotlib-3.7.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:798ff59022eeb276380ce9a73ba35d13c3d1499ab9b73d194fd07f1b0a41c304"},
-    {file = "matplotlib-3.7.4-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1707b20b25e90538c2ce8d4409e30f0ef1df4017cc65ad0439633492a973635b"},
-    {file = "matplotlib-3.7.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8e6227ca8492baeef873cdd8e169a318efb5c3a25ce94e69727e7f964995b0b1"},
-    {file = "matplotlib-3.7.4-cp39-cp39-win32.whl", hash = "sha256:5661c8639aded7d1bbf781373a359011cb1dd09199dee49043e9e68dd16f07ba"},
-    {file = "matplotlib-3.7.4-cp39-cp39-win_amd64.whl", hash = "sha256:55eec941a4743f0bd3e5b8ee180e36b7ea8e62f867bf2613937c9f01b9ac06a2"},
-    {file = "matplotlib-3.7.4-pp38-pypy38_pp73-macosx_10_12_x86_64.whl", hash = "sha256:ab16868714e5cc90ec8f7ff5d83d23bcd6559224d8e9cb5227c9f58748889fe8"},
-    {file = "matplotlib-3.7.4-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0c698b33f9a3f0b127a8e614c8fb4087563bb3caa9c9d95298722fa2400cdd3f"},
-    {file = "matplotlib-3.7.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:be3493bbcb4d255cb71de1f9050ac71682fce21a56089eadbcc8e21784cb12ee"},
-    {file = "matplotlib-3.7.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:f8c725d1dd2901b2e7ec6cd64165e00da2978cc23d4143cb9ef745bec88e6b04"},
-    {file = "matplotlib-3.7.4-pp39-pypy39_pp73-macosx_10_12_x86_64.whl", hash = "sha256:286332f8f45f8ffde2d2119b9fdd42153dccd5025fa9f451b4a3b5c086e26da5"},
-    {file = "matplotlib-3.7.4-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:116ef0b43aa00ff69260b4cce39c571e4b8c6f893795b708303fa27d9b9d7548"},
-    {file = "matplotlib-3.7.4-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c90590d4b46458677d80bc3218f3f1ac11fc122baa9134e0cb5b3e8fc3714052"},
-    {file = "matplotlib-3.7.4-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:de7c07069687be64fd9d119da3122ba13a8d399eccd3f844815f0dc78a870b2c"},
-    {file = "matplotlib-3.7.4.tar.gz", hash = "sha256:7cd4fef8187d1dd0d9dcfdbaa06ac326d396fb8c71c647129f0bf56835d77026"},
-]
-
-[package.dependencies]
-contourpy = ">=1.0.1"
-cycler = ">=0.10"
-fonttools = ">=4.22.0"
-importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""}
-kiwisolver = ">=1.0.1"
-numpy = ">=1.20,<2"
-packaging = ">=20.0"
-pillow = ">=6.2.0"
-pyparsing = ">=2.3.1"
-python-dateutil = ">=2.7"
-
-[[package]]
-name = "mergedeep"
-version = "1.3.4"
-description = "A deep merge function for 🐍."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "mergedeep-1.3.4-py3-none-any.whl", hash = "sha256:70775750742b25c0d8f36c55aed03d24c3384d17c951b3175d898bd778ef0307"},
-    {file = "mergedeep-1.3.4.tar.gz", hash = "sha256:0096d52e9dad9939c3d975a774666af186eda617e6ca84df4c94dec30004f2a8"},
-]
-
-[[package]]
-name = "mkdocs"
-version = "1.5.3"
-description = "Project documentation with Markdown."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "mkdocs-1.5.3-py3-none-any.whl", hash = "sha256:3b3a78e736b31158d64dbb2f8ba29bd46a379d0c6e324c2246c3bc3d2189cfc1"},
-    {file = "mkdocs-1.5.3.tar.gz", hash = "sha256:eb7c99214dcb945313ba30426c2451b735992c73c2e10838f76d09e39ff4d0e2"},
-]
-
-[package.dependencies]
-click = ">=7.0"
-colorama = {version = ">=0.4", markers = "platform_system == \"Windows\""}
-ghp-import = ">=1.0"
-importlib-metadata = {version = ">=4.3", markers = "python_version < \"3.10\""}
-jinja2 = ">=2.11.1"
-markdown = ">=3.2.1"
-markupsafe = ">=2.0.1"
-mergedeep = ">=1.3.4"
-packaging = ">=20.5"
-pathspec = ">=0.11.1"
-platformdirs = ">=2.2.0"
-pyyaml = ">=5.1"
-pyyaml-env-tag = ">=0.1"
-watchdog = ">=2.0"
-
-[package.extras]
-i18n = ["babel (>=2.9.0)"]
-min-versions = ["babel (==2.9.0)", "click (==7.0)", "colorama (==0.4)", "ghp-import (==1.0)", "importlib-metadata (==4.3)", "jinja2 (==2.11.1)", "markdown (==3.2.1)", "markupsafe (==2.0.1)", "mergedeep (==1.3.4)", "packaging (==20.5)", "pathspec (==0.11.1)", "platformdirs (==2.2.0)", "pyyaml (==5.1)", "pyyaml-env-tag (==0.1)", "typing-extensions (==3.10)", "watchdog (==2.0)"]
-
-[[package]]
-name = "mkdocs-autorefs"
-version = "0.5.0"
-description = "Automatically link across pages in MkDocs."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "mkdocs_autorefs-0.5.0-py3-none-any.whl", hash = "sha256:7930fcb8ac1249f10e683967aeaddc0af49d90702af111a5e390e8b20b3d97ff"},
-    {file = "mkdocs_autorefs-0.5.0.tar.gz", hash = "sha256:9a5054a94c08d28855cfab967ada10ed5be76e2bfad642302a610b252c3274c0"},
-]
-
-[package.dependencies]
-Markdown = ">=3.3"
-mkdocs = ">=1.1"
-
-[[package]]
-name = "mkdocs-material"
-version = "9.4.14"
-description = "Documentation that simply works"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "mkdocs_material-9.4.14-py3-none-any.whl", hash = "sha256:dbc78a4fea97b74319a6aa9a2f0be575a6028be6958f813ba367188f7b8428f6"},
-    {file = "mkdocs_material-9.4.14.tar.gz", hash = "sha256:a511d3ff48fa8718b033e7e37d17abd9cc1de0fdf0244a625ca2ae2387e2416d"},
-]
-
-[package.dependencies]
-babel = ">=2.10,<3.0"
-colorama = ">=0.4,<1.0"
-jinja2 = ">=3.0,<4.0"
-markdown = ">=3.2,<4.0"
-mkdocs = ">=1.5.3,<2.0"
-mkdocs-material-extensions = ">=1.3,<2.0"
-paginate = ">=0.5,<1.0"
-pygments = ">=2.16,<3.0"
-pymdown-extensions = ">=10.2,<11.0"
-regex = ">=2022.4"
-requests = ">=2.26,<3.0"
-
-[package.extras]
-git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
-imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=9.4,<10.0)"]
-recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
-
-[[package]]
-name = "mkdocs-material-extensions"
-version = "1.3.1"
-description = "Extension pack for Python Markdown and MkDocs Material."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "mkdocs_material_extensions-1.3.1-py3-none-any.whl", hash = "sha256:adff8b62700b25cb77b53358dad940f3ef973dd6db797907c49e3c2ef3ab4e31"},
-    {file = "mkdocs_material_extensions-1.3.1.tar.gz", hash = "sha256:10c9511cea88f568257f960358a467d12b970e1f7b2c0e5fb2bb48cab1928443"},
-]
-
-[[package]]
-name = "mkdocstrings"
-version = "0.24.0"
-description = "Automatic documentation from sources, for MkDocs."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "mkdocstrings-0.24.0-py3-none-any.whl", hash = "sha256:f4908560c10f587326d8f5165d1908817b2e280bbf707607f601c996366a2264"},
-    {file = "mkdocstrings-0.24.0.tar.gz", hash = "sha256:222b1165be41257b494a9d29b14135d2b7ca43f38161d5b10caae03b87bd4f7e"},
-]
-
-[package.dependencies]
-click = ">=7.0"
-importlib-metadata = {version = ">=4.6", markers = "python_version < \"3.10\""}
-Jinja2 = ">=2.11.1"
-Markdown = ">=3.3"
-MarkupSafe = ">=1.1"
-mkdocs = ">=1.4"
-mkdocs-autorefs = ">=0.3.1"
-mkdocstrings-python = {version = ">=0.5.2", optional = true, markers = "extra == \"python\""}
-platformdirs = ">=2.2.0"
-pymdown-extensions = ">=6.3"
-typing-extensions = {version = ">=4.1", markers = "python_version < \"3.10\""}
-
-[package.extras]
-crystal = ["mkdocstrings-crystal (>=0.3.4)"]
-python = ["mkdocstrings-python (>=0.5.2)"]
-python-legacy = ["mkdocstrings-python-legacy (>=0.2.1)"]
-
-[[package]]
-name = "mkdocstrings-python"
-version = "1.7.5"
-description = "A Python handler for mkdocstrings."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "mkdocstrings_python-1.7.5-py3-none-any.whl", hash = "sha256:5f6246026353f0c0785135db70c3fe9a5d9318990fc7ceb11d62097b8ffdd704"},
-    {file = "mkdocstrings_python-1.7.5.tar.gz", hash = "sha256:c7d143728257dbf1aa550446555a554b760dcd40a763f077189d298502b800be"},
-]
-
-[package.dependencies]
-griffe = ">=0.37"
-mkdocstrings = ">=0.20"
-
-[[package]]
-name = "numpy"
-version = "1.24.4"
-description = "Fundamental package for array computing in Python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "numpy-1.24.4-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0bfb52d2169d58c1cdb8cc1f16989101639b34c7d3ce60ed70b19c63eba0b64"},
-    {file = "numpy-1.24.4-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:ed094d4f0c177b1b8e7aa9cba7d6ceed51c0e569a5318ac0ca9a090680a6a1b1"},
-    {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:79fc682a374c4a8ed08b331bef9c5f582585d1048fa6d80bc6c35bc384eee9b4"},
-    {file = "numpy-1.24.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ffe43c74893dbf38c2b0a1f5428760a1a9c98285553c89e12d70a96a7f3a4d6"},
-    {file = "numpy-1.24.4-cp310-cp310-win32.whl", hash = "sha256:4c21decb6ea94057331e111a5bed9a79d335658c27ce2adb580fb4d54f2ad9bc"},
-    {file = "numpy-1.24.4-cp310-cp310-win_amd64.whl", hash = "sha256:b4bea75e47d9586d31e892a7401f76e909712a0fd510f58f5337bea9572c571e"},
-    {file = "numpy-1.24.4-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:f136bab9c2cfd8da131132c2cf6cc27331dd6fae65f95f69dcd4ae3c3639c810"},
-    {file = "numpy-1.24.4-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2926dac25b313635e4d6cf4dc4e51c8c0ebfed60b801c799ffc4c32bf3d1254"},
-    {file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:222e40d0e2548690405b0b3c7b21d1169117391c2e82c378467ef9ab4c8f0da7"},
-    {file = "numpy-1.24.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7215847ce88a85ce39baf9e89070cb860c98fdddacbaa6c0da3ffb31b3350bd5"},
-    {file = "numpy-1.24.4-cp311-cp311-win32.whl", hash = "sha256:4979217d7de511a8d57f4b4b5b2b965f707768440c17cb70fbf254c4b225238d"},
-    {file = "numpy-1.24.4-cp311-cp311-win_amd64.whl", hash = "sha256:b7b1fc9864d7d39e28f41d089bfd6353cb5f27ecd9905348c24187a768c79694"},
-    {file = "numpy-1.24.4-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1452241c290f3e2a312c137a9999cdbf63f78864d63c79039bda65ee86943f61"},
-    {file = "numpy-1.24.4-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:04640dab83f7c6c85abf9cd729c5b65f1ebd0ccf9de90b270cd61935eef0197f"},
-    {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5425b114831d1e77e4b5d812b69d11d962e104095a5b9c3b641a218abcc050e"},
-    {file = "numpy-1.24.4-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd80e219fd4c71fc3699fc1dadac5dcf4fd882bfc6f7ec53d30fa197b8ee22dc"},
-    {file = "numpy-1.24.4-cp38-cp38-win32.whl", hash = "sha256:4602244f345453db537be5314d3983dbf5834a9701b7723ec28923e2889e0bb2"},
-    {file = "numpy-1.24.4-cp38-cp38-win_amd64.whl", hash = "sha256:692f2e0f55794943c5bfff12b3f56f99af76f902fc47487bdfe97856de51a706"},
-    {file = "numpy-1.24.4-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2541312fbf09977f3b3ad449c4e5f4bb55d0dbf79226d7724211acc905049400"},
-    {file = "numpy-1.24.4-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:9667575fb6d13c95f1b36aca12c5ee3356bf001b714fc354eb5465ce1609e62f"},
-    {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f3a86ed21e4f87050382c7bc96571755193c4c1392490744ac73d660e8f564a9"},
-    {file = "numpy-1.24.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d11efb4dbecbdf22508d55e48d9c8384db795e1b7b51ea735289ff96613ff74d"},
-    {file = "numpy-1.24.4-cp39-cp39-win32.whl", hash = "sha256:6620c0acd41dbcb368610bb2f4d83145674040025e5536954782467100aa8835"},
-    {file = "numpy-1.24.4-cp39-cp39-win_amd64.whl", hash = "sha256:befe2bf740fd8373cf56149a5c23a0f601e82869598d41f8e188a0e9869926f8"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:31f13e25b4e304632a4619d0e0777662c2ffea99fcae2029556b17d8ff958aef"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:95f7ac6540e95bc440ad77f56e520da5bf877f87dca58bd095288dce8940532a"},
-    {file = "numpy-1.24.4-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:e98f220aa76ca2a977fe435f5b04d7b3470c0a2e6312907b37ba6068f26787f2"},
-    {file = "numpy-1.24.4.tar.gz", hash = "sha256:80f5e3a4e498641401868df4208b74581206afbee7cf7b8329daae82676d9463"},
-]
-
-[[package]]
-name = "opencv-python-headless"
-version = "4.8.1.78"
-description = "Wrapper package for OpenCV python bindings."
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "opencv-python-headless-4.8.1.78.tar.gz", hash = "sha256:bc7197b42352f6f865c302a49140b889ec7cd957dd697e2d7fc016ad0d3f28f1"},
-    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:f3a33f644249f9ce1c913eac580e4b3ef4ce7cab0a71900274708959c2feb5e3"},
-    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:2c7d45721df9801c4dcd34683a15caa0e30f38b185263fec04a6eb274bc720f0"},
-    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b6bd6e1132b6f5dcb3a5bfe30fc4d341a7bfb26134da349a06c9255288ded94"},
-    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:58e70d2f0915fe23e02c6e405588276c9397844a47d38b9c87fac5f7f9ba2dcc"},
-    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-win32.whl", hash = "sha256:382f8c7a6a14f80091284eecedd52cee4812231ee0eff1118592197b538d9252"},
-    {file = "opencv_python_headless-4.8.1.78-cp37-abi3-win_amd64.whl", hash = "sha256:0a0f1e9f836f7d5bad1dd164694944c8761711cbdf4b36ebbd4815a8ef731079"},
-]
-
-[package.dependencies]
-numpy = [
-    {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\""},
-    {version = ">=1.21.2", markers = "python_version >= \"3.10\""},
-    {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
-    {version = ">=1.19.3", markers = "python_version >= \"3.6\" and platform_system == \"Linux\" and platform_machine == \"aarch64\" or python_version >= \"3.9\""},
-    {version = ">=1.17.0", markers = "python_version >= \"3.7\""},
-    {version = ">=1.17.3", markers = "python_version >= \"3.8\""},
-]
-
-[[package]]
-name = "packaging"
-version = "23.2"
-description = "Core utilities for Python packages"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "packaging-23.2-py3-none-any.whl", hash = "sha256:8c491190033a9af7e1d931d0b5dacc2ef47509b34dd0de67ed209b5203fc88c7"},
-    {file = "packaging-23.2.tar.gz", hash = "sha256:048fb0e9405036518eaaf48a55953c750c11e1a1b68e0dd1a9d62ed0c092cfc5"},
-]
-
-[[package]]
-name = "paginate"
-version = "0.5.6"
-description = "Divides large result sets into pages for easier browsing"
-optional = false
-python-versions = "*"
-files = [
-    {file = "paginate-0.5.6.tar.gz", hash = "sha256:5e6007b6a9398177a7e1648d04fdd9f8c9766a1a945bceac82f1929e8c78af2d"},
-]
-
-[[package]]
-name = "pathspec"
-version = "0.11.2"
-description = "Utility library for gitignore style pattern matching of file paths."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "pathspec-0.11.2-py3-none-any.whl", hash = "sha256:1d6ed233af05e679efb96b1851550ea95bbb64b7c490b0f5aa52996c11e92a20"},
-    {file = "pathspec-0.11.2.tar.gz", hash = "sha256:e0d8d0ac2f12da61956eb2306b69f9469b42f4deb0f3cb6ed47b9cce9996ced3"},
-]
-
-[[package]]
-name = "pillow"
-version = "10.1.0"
-description = "Python Imaging Library (Fork)"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "Pillow-10.1.0-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:1ab05f3db77e98f93964697c8efc49c7954b08dd61cff526b7f2531a22410106"},
-    {file = "Pillow-10.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:6932a7652464746fcb484f7fc3618e6503d2066d853f68a4bd97193a3996e273"},
-    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a5f63b5a68daedc54c7c3464508d8c12075e56dcfbd42f8c1bf40169061ae666"},
-    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c0949b55eb607898e28eaccb525ab104b2d86542a85c74baf3a6dc24002edec2"},
-    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_28_aarch64.whl", hash = "sha256:ae88931f93214777c7a3aa0a8f92a683f83ecde27f65a45f95f22d289a69e593"},
-    {file = "Pillow-10.1.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:b0eb01ca85b2361b09480784a7931fc648ed8b7836f01fb9241141b968feb1db"},
-    {file = "Pillow-10.1.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d27b5997bdd2eb9fb199982bb7eb6164db0426904020dc38c10203187ae2ff2f"},
-    {file = "Pillow-10.1.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:7df5608bc38bd37ef585ae9c38c9cd46d7c81498f086915b0f97255ea60c2818"},
-    {file = "Pillow-10.1.0-cp310-cp310-win_amd64.whl", hash = "sha256:41f67248d92a5e0a2076d3517d8d4b1e41a97e2df10eb8f93106c89107f38b57"},
-    {file = "Pillow-10.1.0-cp311-cp311-macosx_10_10_x86_64.whl", hash = "sha256:1fb29c07478e6c06a46b867e43b0bcdb241b44cc52be9bc25ce5944eed4648e7"},
-    {file = "Pillow-10.1.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:2cdc65a46e74514ce742c2013cd4a2d12e8553e3a2563c64879f7c7e4d28bce7"},
-    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50d08cd0a2ecd2a8657bd3d82c71efd5a58edb04d9308185d66c3a5a5bed9610"},
-    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:062a1610e3bc258bff2328ec43f34244fcec972ee0717200cb1425214fe5b839"},
-    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:61f1a9d247317fa08a308daaa8ee7b3f760ab1809ca2da14ecc88ae4257d6172"},
-    {file = "Pillow-10.1.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:a646e48de237d860c36e0db37ecaecaa3619e6f3e9d5319e527ccbc8151df061"},
-    {file = "Pillow-10.1.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:47e5bf85b80abc03be7455c95b6d6e4896a62f6541c1f2ce77a7d2bb832af262"},
-    {file = "Pillow-10.1.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:a92386125e9ee90381c3369f57a2a50fa9e6aa8b1cf1d9c4b200d41a7dd8e992"},
-    {file = "Pillow-10.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:0f7c276c05a9767e877a0b4c5050c8bee6a6d960d7f0c11ebda6b99746068c2a"},
-    {file = "Pillow-10.1.0-cp312-cp312-macosx_10_10_x86_64.whl", hash = "sha256:a89b8312d51715b510a4fe9fc13686283f376cfd5abca8cd1c65e4c76e21081b"},
-    {file = "Pillow-10.1.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:00f438bb841382b15d7deb9a05cc946ee0f2c352653c7aa659e75e592f6fa17d"},
-    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3d929a19f5469b3f4df33a3df2983db070ebb2088a1e145e18facbc28cae5b27"},
-    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9a92109192b360634a4489c0c756364c0c3a2992906752165ecb50544c251312"},
-    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:0248f86b3ea061e67817c47ecbe82c23f9dd5d5226200eb9090b3873d3ca32de"},
-    {file = "Pillow-10.1.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:9882a7451c680c12f232a422730f986a1fcd808da0fd428f08b671237237d651"},
-    {file = "Pillow-10.1.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:1c3ac5423c8c1da5928aa12c6e258921956757d976405e9467c5f39d1d577a4b"},
-    {file = "Pillow-10.1.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:806abdd8249ba3953c33742506fe414880bad78ac25cc9a9b1c6ae97bedd573f"},
-    {file = "Pillow-10.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:eaed6977fa73408b7b8a24e8b14e59e1668cfc0f4c40193ea7ced8e210adf996"},
-    {file = "Pillow-10.1.0-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:fe1e26e1ffc38be097f0ba1d0d07fcade2bcfd1d023cda5b29935ae8052bd793"},
-    {file = "Pillow-10.1.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:7a7e3daa202beb61821c06d2517428e8e7c1aab08943e92ec9e5755c2fc9ba5e"},
-    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:24fadc71218ad2b8ffe437b54876c9382b4a29e030a05a9879f615091f42ffc2"},
-    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fa1d323703cfdac2036af05191b969b910d8f115cf53093125e4058f62012c9a"},
-    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_28_aarch64.whl", hash = "sha256:912e3812a1dbbc834da2b32299b124b5ddcb664ed354916fd1ed6f193f0e2d01"},
-    {file = "Pillow-10.1.0-cp38-cp38-manylinux_2_28_x86_64.whl", hash = "sha256:7dbaa3c7de82ef37e7708521be41db5565004258ca76945ad74a8e998c30af8d"},
-    {file = "Pillow-10.1.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:9d7bc666bd8c5a4225e7ac71f2f9d12466ec555e89092728ea0f5c0c2422ea80"},
-    {file = "Pillow-10.1.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:baada14941c83079bf84c037e2d8b7506ce201e92e3d2fa0d1303507a8538212"},
-    {file = "Pillow-10.1.0-cp38-cp38-win_amd64.whl", hash = "sha256:2ef6721c97894a7aa77723740a09547197533146fba8355e86d6d9a4a1056b14"},
-    {file = "Pillow-10.1.0-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:0a026c188be3b443916179f5d04548092e253beb0c3e2ee0a4e2cdad72f66099"},
-    {file = "Pillow-10.1.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:04f6f6149f266a100374ca3cc368b67fb27c4af9f1cc8cb6306d849dcdf12616"},
-    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb40c011447712d2e19cc261c82655f75f32cb724788df315ed992a4d65696bb"},
-    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1a8413794b4ad9719346cd9306118450b7b00d9a15846451549314a58ac42219"},
-    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_28_aarch64.whl", hash = "sha256:c9aeea7b63edb7884b031a35305629a7593272b54f429a9869a4f63a1bf04c34"},
-    {file = "Pillow-10.1.0-cp39-cp39-manylinux_2_28_x86_64.whl", hash = "sha256:b4005fee46ed9be0b8fb42be0c20e79411533d1fd58edabebc0dd24626882cfd"},
-    {file = "Pillow-10.1.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:4d0152565c6aa6ebbfb1e5d8624140a440f2b99bf7afaafbdbf6430426497f28"},
-    {file = "Pillow-10.1.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:d921bc90b1defa55c9917ca6b6b71430e4286fc9e44c55ead78ca1a9f9eba5f2"},
-    {file = "Pillow-10.1.0-cp39-cp39-win_amd64.whl", hash = "sha256:cfe96560c6ce2f4c07d6647af2d0f3c54cc33289894ebd88cfbb3bcd5391e256"},
-    {file = "Pillow-10.1.0-pp310-pypy310_pp73-macosx_10_10_x86_64.whl", hash = "sha256:937bdc5a7f5343d1c97dc98149a0be7eb9704e937fe3dc7140e229ae4fc572a7"},
-    {file = "Pillow-10.1.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b1c25762197144e211efb5f4e8ad656f36c8d214d390585d1d21281f46d556ba"},
-    {file = "Pillow-10.1.0-pp310-pypy310_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:afc8eef765d948543a4775f00b7b8c079b3321d6b675dde0d02afa2ee23000b4"},
-    {file = "Pillow-10.1.0-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:883f216eac8712b83a63f41b76ddfb7b2afab1b74abbb413c5df6680f071a6b9"},
-    {file = "Pillow-10.1.0-pp39-pypy39_pp73-macosx_10_10_x86_64.whl", hash = "sha256:b920e4d028f6442bea9a75b7491c063f0b9a3972520731ed26c83e254302eb1e"},
-    {file = "Pillow-10.1.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1c41d960babf951e01a49c9746f92c5a7e0d939d1652d7ba30f6b3090f27e412"},
-    {file = "Pillow-10.1.0-pp39-pypy39_pp73-manylinux_2_28_x86_64.whl", hash = "sha256:1fafabe50a6977ac70dfe829b2d5735fd54e190ab55259ec8aea4aaea412fa0b"},
-    {file = "Pillow-10.1.0-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:3b834f4b16173e5b92ab6566f0473bfb09f939ba14b23b8da1f54fa63e4b623f"},
-    {file = "Pillow-10.1.0.tar.gz", hash = "sha256:e6bf8de6c36ed96c86ea3b6e1d5273c53f46ef518a062464cd7ef5dd2cf92e38"},
-]
-
-[package.extras]
-docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"]
-tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
-
-[[package]]
-name = "platformdirs"
-version = "4.0.0"
-description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "platformdirs-4.0.0-py3-none-any.whl", hash = "sha256:118c954d7e949b35437270383a3f2531e99dd93cf7ce4dc8340d3356d30f173b"},
-    {file = "platformdirs-4.0.0.tar.gz", hash = "sha256:cb633b2bcf10c51af60beb0ab06d2f1d69064b43abf4c185ca6b28865f3f9731"},
-]
-
-[package.extras]
-docs = ["furo (>=2023.7.26)", "proselint (>=0.13)", "sphinx (>=7.1.1)", "sphinx-autodoc-typehints (>=1.24)"]
-test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)"]
-
-[[package]]
-name = "pluggy"
-version = "1.3.0"
-description = "plugin and hook calling mechanisms for python"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pluggy-1.3.0-py3-none-any.whl", hash = "sha256:d89c696a773f8bd377d18e5ecda92b7a3793cbe66c87060a6fb58c7b6e1061f7"},
-    {file = "pluggy-1.3.0.tar.gz", hash = "sha256:cf61ae8f126ac6f7c451172cf30e3e43d3ca77615509771b3a984a0730651e12"},
-]
-
-[package.extras]
-dev = ["pre-commit", "tox"]
-testing = ["pytest", "pytest-benchmark"]
-
-[[package]]
-name = "pygments"
-version = "2.17.2"
-description = "Pygments is a syntax highlighting package written in Python."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "pygments-2.17.2-py3-none-any.whl", hash = "sha256:b27c2826c47d0f3219f29554824c30c5e8945175d888647acd804ddd04af846c"},
-    {file = "pygments-2.17.2.tar.gz", hash = "sha256:da46cec9fd2de5be3a8a784f434e4c4ab670b4ff54d605c4c2717e9d49c4c367"},
-]
-
-[package.extras]
-plugins = ["importlib-metadata"]
-windows-terminal = ["colorama (>=0.4.6)"]
-
-[[package]]
-name = "pymdown-extensions"
-version = "10.5"
-description = "Extension pack for Python Markdown."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "pymdown_extensions-10.5-py3-none-any.whl", hash = "sha256:1f0ca8bb5beff091315f793ee17683bc1390731f6ac4c5eb01e27464b80fe879"},
-    {file = "pymdown_extensions-10.5.tar.gz", hash = "sha256:1b60f1e462adbec5a1ed79dac91f666c9c0d241fa294de1989f29d20096cfd0b"},
-]
-
-[package.dependencies]
-markdown = ">=3.5"
-pyyaml = "*"
-
-[package.extras]
-extra = ["pygments (>=2.12)"]
-
-[[package]]
-name = "pyparsing"
-version = "3.1.1"
-description = "pyparsing module - Classes and methods to define and execute parsing grammars"
-optional = false
-python-versions = ">=3.6.8"
-files = [
-    {file = "pyparsing-3.1.1-py3-none-any.whl", hash = "sha256:32c7c0b711493c72ff18a981d24f28aaf9c1fb7ed5e9667c9e84e3db623bdbfb"},
-    {file = "pyparsing-3.1.1.tar.gz", hash = "sha256:ede28a1a32462f5a9705e07aea48001a08f7cf81a021585011deba701581a0db"},
-]
-
-[package.extras]
-diagrams = ["jinja2", "railroad-diagrams"]
-
-[[package]]
-name = "pytest"
-version = "7.4.3"
-description = "pytest: simple powerful testing with Python"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "pytest-7.4.3-py3-none-any.whl", hash = "sha256:0d009c083ea859a71b76adf7c1d502e4bc170b80a8ef002da5806527b9591fac"},
-    {file = "pytest-7.4.3.tar.gz", hash = "sha256:d989d136982de4e3b29dabcc838ad581c64e8ed52c11fbe86ddebd9da0818cd5"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "sys_platform == \"win32\""}
-exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
-iniconfig = "*"
-packaging = "*"
-pluggy = ">=0.12,<2.0"
-tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
-
-[package.extras]
-testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "setuptools", "xmlschema"]
-
-[[package]]
-name = "python-dateutil"
-version = "2.8.2"
-description = "Extensions to the standard Python datetime module"
-optional = false
-python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-files = [
-    {file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
-    {file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
-]
-
-[package.dependencies]
-six = ">=1.5"
-
-[[package]]
-name = "pytz"
-version = "2023.3.post1"
-description = "World timezone definitions, modern and historical"
-optional = false
-python-versions = "*"
-files = [
-    {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
-    {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
-]
-
-[[package]]
-name = "pyyaml"
-version = "6.0.1"
-description = "YAML parser and emitter for Python"
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "PyYAML-6.0.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d858aa552c999bc8a8d57426ed01e40bef403cd8ccdd0fc5f6f04a00414cac2a"},
-    {file = "PyYAML-6.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fd66fc5d0da6d9815ba2cebeb4205f95818ff4b79c3ebe268e75d961704af52f"},
-    {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"},
-    {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"},
-    {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"},
-    {file = "PyYAML-6.0.1-cp310-cp310-win32.whl", hash = "sha256:bd4af7373a854424dabd882decdc5579653d7868b8fb26dc7d0e99f823aa5924"},
-    {file = "PyYAML-6.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:fd1592b3fdf65fff2ad0004b5e363300ef59ced41c2e6b3a99d4089fa8c5435d"},
-    {file = "PyYAML-6.0.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6965a7bc3cf88e5a1c3bd2e0b5c22f8d677dc88a455344035f03399034eb3007"},
-    {file = "PyYAML-6.0.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f003ed9ad21d6a4713f0a9b5a7a0a79e08dd0f221aff4525a2be4c346ee60aab"},
-    {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:42f8152b8dbc4fe7d96729ec2b99c7097d656dc1213a3229ca5383f973a5ed6d"},
-    {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:062582fca9fabdd2c8b54a3ef1c978d786e0f6b3a1510e0ac93ef59e0ddae2bc"},
-    {file = "PyYAML-6.0.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d2b04aac4d386b172d5b9692e2d2da8de7bfb6c387fa4f801fbf6fb2e6ba4673"},
-    {file = "PyYAML-6.0.1-cp311-cp311-win32.whl", hash = "sha256:1635fd110e8d85d55237ab316b5b011de701ea0f29d07611174a1b42f1444741"},
-    {file = "PyYAML-6.0.1-cp311-cp311-win_amd64.whl", hash = "sha256:bf07ee2fef7014951eeb99f56f39c9bb4af143d8aa3c21b1677805985307da34"},
-    {file = "PyYAML-6.0.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:50550eb667afee136e9a77d6dc71ae76a44df8b3e51e41b77f6de2932bfe0f47"},
-    {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1fe35611261b29bd1de0070f0b2f47cb6ff71fa6595c077e42bd0c419fa27b98"},
-    {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:704219a11b772aea0d8ecd7058d0082713c3562b4e271b849ad7dc4a5c90c13c"},
-    {file = "PyYAML-6.0.1-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:afd7e57eddb1a54f0f1a974bc4391af8bcce0b444685d936840f125cf046d5bd"},
-    {file = "PyYAML-6.0.1-cp36-cp36m-win32.whl", hash = "sha256:fca0e3a251908a499833aa292323f32437106001d436eca0e6e7833256674585"},
-    {file = "PyYAML-6.0.1-cp36-cp36m-win_amd64.whl", hash = "sha256:f22ac1c3cac4dbc50079e965eba2c1058622631e526bd9afd45fedd49ba781fa"},
-    {file = "PyYAML-6.0.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b1275ad35a5d18c62a7220633c913e1b42d44b46ee12554e5fd39c70a243d6a3"},
-    {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:18aeb1bf9a78867dc38b259769503436b7c72f7a1f1f4c93ff9a17de54319b27"},
-    {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:596106435fa6ad000c2991a98fa58eeb8656ef2325d7e158344fb33864ed87e3"},
-    {file = "PyYAML-6.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:baa90d3f661d43131ca170712d903e6295d1f7a0f595074f151c0aed377c9b9c"},
-    {file = "PyYAML-6.0.1-cp37-cp37m-win32.whl", hash = "sha256:9046c58c4395dff28dd494285c82ba00b546adfc7ef001486fbf0324bc174fba"},
-    {file = "PyYAML-6.0.1-cp37-cp37m-win_amd64.whl", hash = "sha256:4fb147e7a67ef577a588a0e2c17b6db51dda102c71de36f8549b6816a96e1867"},
-    {file = "PyYAML-6.0.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:1d4c7e777c441b20e32f52bd377e0c409713e8bb1386e1099c2415f26e479595"},
-    {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd17c15d3bb3fa06978b4e8958dcdc6e0174ccea823003a106c7d4d7899ac5"},
-    {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:28c119d996beec18c05208a8bd78cbe4007878c6dd15091efb73a30e90539696"},
-    {file = "PyYAML-6.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7e07cbde391ba96ab58e532ff4803f79c4129397514e1413a7dc761ccd755735"},
-    {file = "PyYAML-6.0.1-cp38-cp38-win32.whl", hash = "sha256:184c5108a2aca3c5b3d3bf9395d50893a7ab82a38004c8f61c258d4428e80206"},
-    {file = "PyYAML-6.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:1e2722cc9fbb45d9b87631ac70924c11d3a401b2d7f410cc0e3bbf249f2dca62"},
-    {file = "PyYAML-6.0.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:9eb6caa9a297fc2c2fb8862bc5370d0303ddba53ba97e71f08023b6cd73d16a8"},
-    {file = "PyYAML-6.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c8098ddcc2a85b61647b2590f825f3db38891662cfc2fc776415143f599bb859"},
-    {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5773183b6446b2c99bb77e77595dd486303b4faab2b086e7b17bc6bef28865f6"},
-    {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b786eecbdf8499b9ca1d697215862083bd6d2a99965554781d0d8d1ad31e13a0"},
-    {file = "PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c"},
-    {file = "PyYAML-6.0.1-cp39-cp39-win32.whl", hash = "sha256:faca3bdcf85b2fc05d06ff3fbc1f83e1391b3e724afa3feba7d13eeab355484c"},
-    {file = "PyYAML-6.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:510c9deebc5c0225e8c96813043e62b680ba2f9c50a08d3724c7f28a747d1486"},
-    {file = "PyYAML-6.0.1.tar.gz", hash = "sha256:bfdf460b1736c775f2ba9f6a92bca30bc2095067b8a9d77876d1fad6cc3b4a43"},
-]
-
-[[package]]
-name = "pyyaml-env-tag"
-version = "0.1"
-description = "A custom YAML tag for referencing environment variables in YAML files. "
-optional = false
-python-versions = ">=3.6"
-files = [
-    {file = "pyyaml_env_tag-0.1-py3-none-any.whl", hash = "sha256:af31106dec8a4d68c60207c1886031cbf839b68aa7abccdb19868200532c2069"},
-    {file = "pyyaml_env_tag-0.1.tar.gz", hash = "sha256:70092675bda14fdec33b31ba77e7543de9ddc88f2e5b99160396572d11525bdb"},
-]
-
-[package.dependencies]
-pyyaml = "*"
-
-[[package]]
-name = "regex"
-version = "2023.10.3"
-description = "Alternative regular expression module, to replace re."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "regex-2023.10.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:4c34d4f73ea738223a094d8e0ffd6d2c1a1b4c175da34d6b0de3d8d69bee6bcc"},
-    {file = "regex-2023.10.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a8f4e49fc3ce020f65411432183e6775f24e02dff617281094ba6ab079ef0915"},
-    {file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4cd1bccf99d3ef1ab6ba835308ad85be040e6a11b0977ef7ea8c8005f01a3c29"},
-    {file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:81dce2ddc9f6e8f543d94b05d56e70d03a0774d32f6cca53e978dc01e4fc75b8"},
-    {file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9c6b4d23c04831e3ab61717a707a5d763b300213db49ca680edf8bf13ab5d91b"},
-    {file = "regex-2023.10.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c15ad0aee158a15e17e0495e1e18741573d04eb6da06d8b84af726cfc1ed02ee"},
-    {file = "regex-2023.10.3-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6239d4e2e0b52c8bd38c51b760cd870069f0bdf99700a62cd509d7a031749a55"},
-    {file = "regex-2023.10.3-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:4a8bf76e3182797c6b1afa5b822d1d5802ff30284abe4599e1247be4fd6b03be"},
-    {file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:d9c727bbcf0065cbb20f39d2b4f932f8fa1631c3e01fcedc979bd4f51fe051c5"},
-    {file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:3ccf2716add72f80714b9a63899b67fa711b654be3fcdd34fa391d2d274ce767"},
-    {file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_ppc64le.whl", hash = "sha256:107ac60d1bfdc3edb53be75e2a52aff7481b92817cfdddd9b4519ccf0e54a6ff"},
-    {file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_s390x.whl", hash = "sha256:00ba3c9818e33f1fa974693fb55d24cdc8ebafcb2e4207680669d8f8d7cca79a"},
-    {file = "regex-2023.10.3-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:f0a47efb1dbef13af9c9a54a94a0b814902e547b7f21acb29434504d18f36e3a"},
-    {file = "regex-2023.10.3-cp310-cp310-win32.whl", hash = "sha256:36362386b813fa6c9146da6149a001b7bd063dabc4d49522a1f7aa65b725c7ec"},
-    {file = "regex-2023.10.3-cp310-cp310-win_amd64.whl", hash = "sha256:c65a3b5330b54103e7d21cac3f6bf3900d46f6d50138d73343d9e5b2900b2353"},
-    {file = "regex-2023.10.3-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:90a79bce019c442604662d17bf69df99090e24cdc6ad95b18b6725c2988a490e"},
-    {file = "regex-2023.10.3-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c7964c2183c3e6cce3f497e3a9f49d182e969f2dc3aeeadfa18945ff7bdd7051"},
-    {file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4ef80829117a8061f974b2fda8ec799717242353bff55f8a29411794d635d964"},
-    {file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:5addc9d0209a9afca5fc070f93b726bf7003bd63a427f65ef797a931782e7edc"},
-    {file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c148bec483cc4b421562b4bcedb8e28a3b84fcc8f0aa4418e10898f3c2c0eb9b"},
-    {file = "regex-2023.10.3-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8d1f21af4c1539051049796a0f50aa342f9a27cde57318f2fc41ed50b0dbc4ac"},
-    {file = "regex-2023.10.3-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0b9ac09853b2a3e0d0082104036579809679e7715671cfbf89d83c1cb2a30f58"},
-    {file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:ebedc192abbc7fd13c5ee800e83a6df252bec691eb2c4bedc9f8b2e2903f5e2a"},
-    {file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:d8a993c0a0ffd5f2d3bda23d0cd75e7086736f8f8268de8a82fbc4bd0ac6791e"},
-    {file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_ppc64le.whl", hash = "sha256:be6b7b8d42d3090b6c80793524fa66c57ad7ee3fe9722b258aec6d0672543fd0"},
-    {file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_s390x.whl", hash = "sha256:4023e2efc35a30e66e938de5aef42b520c20e7eda7bb5fb12c35e5d09a4c43f6"},
-    {file = "regex-2023.10.3-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:0d47840dc05e0ba04fe2e26f15126de7c755496d5a8aae4a08bda4dd8d646c54"},
-    {file = "regex-2023.10.3-cp311-cp311-win32.whl", hash = "sha256:9145f092b5d1977ec8c0ab46e7b3381b2fd069957b9862a43bd383e5c01d18c2"},
-    {file = "regex-2023.10.3-cp311-cp311-win_amd64.whl", hash = "sha256:b6104f9a46bd8743e4f738afef69b153c4b8b592d35ae46db07fc28ae3d5fb7c"},
-    {file = "regex-2023.10.3-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bff507ae210371d4b1fe316d03433ac099f184d570a1a611e541923f78f05037"},
-    {file = "regex-2023.10.3-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:be5e22bbb67924dea15039c3282fa4cc6cdfbe0cbbd1c0515f9223186fc2ec5f"},
-    {file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4a992f702c9be9c72fa46f01ca6e18d131906a7180950958f766c2aa294d4b41"},
-    {file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7434a61b158be563c1362d9071358f8ab91b8d928728cd2882af060481244c9e"},
-    {file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c2169b2dcabf4e608416f7f9468737583ce5f0a6e8677c4efbf795ce81109d7c"},
-    {file = "regex-2023.10.3-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a9e908ef5889cda4de038892b9accc36d33d72fb3e12c747e2799a0e806ec841"},
-    {file = "regex-2023.10.3-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:12bd4bc2c632742c7ce20db48e0d99afdc05e03f0b4c1af90542e05b809a03d9"},
-    {file = "regex-2023.10.3-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:bc72c231f5449d86d6c7d9cc7cd819b6eb30134bb770b8cfdc0765e48ef9c420"},
-    {file = "regex-2023.10.3-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:bce8814b076f0ce5766dc87d5a056b0e9437b8e0cd351b9a6c4e1134a7dfbda9"},
-    {file = "regex-2023.10.3-cp312-cp312-musllinux_1_1_ppc64le.whl", hash = "sha256:ba7cd6dc4d585ea544c1412019921570ebd8a597fabf475acc4528210d7c4a6f"},
-    {file = "regex-2023.10.3-cp312-cp312-musllinux_1_1_s390x.whl", hash = "sha256:b0c7d2f698e83f15228ba41c135501cfe7d5740181d5903e250e47f617eb4292"},
-    {file = "regex-2023.10.3-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:5a8f91c64f390ecee09ff793319f30a0f32492e99f5dc1c72bc361f23ccd0a9a"},
-    {file = "regex-2023.10.3-cp312-cp312-win32.whl", hash = "sha256:ad08a69728ff3c79866d729b095872afe1e0557251da4abb2c5faff15a91d19a"},
-    {file = "regex-2023.10.3-cp312-cp312-win_amd64.whl", hash = "sha256:39cdf8d141d6d44e8d5a12a8569d5a227f645c87df4f92179bd06e2e2705e76b"},
-    {file = "regex-2023.10.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:4a3ee019a9befe84fa3e917a2dd378807e423d013377a884c1970a3c2792d293"},
-    {file = "regex-2023.10.3-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:76066d7ff61ba6bf3cb5efe2428fc82aac91802844c022d849a1f0f53820502d"},
-    {file = "regex-2023.10.3-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:bfe50b61bab1b1ec260fa7cd91106fa9fece57e6beba05630afe27c71259c59b"},
-    {file = "regex-2023.10.3-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9fd88f373cb71e6b59b7fa597e47e518282455c2734fd4306a05ca219a1991b0"},
-    {file = "regex-2023.10.3-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b3ab05a182c7937fb374f7e946f04fb23a0c0699c0450e9fb02ef567412d2fa3"},
-    {file = "regex-2023.10.3-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dac37cf08fcf2094159922edc7a2784cfcc5c70f8354469f79ed085f0328ebdf"},
-    {file = "regex-2023.10.3-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:e54ddd0bb8fb626aa1f9ba7b36629564544954fff9669b15da3610c22b9a0991"},
-    {file = "regex-2023.10.3-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:3367007ad1951fde612bf65b0dffc8fd681a4ab98ac86957d16491400d661302"},
-    {file = "regex-2023.10.3-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:16f8740eb6dbacc7113e3097b0a36065a02e37b47c936b551805d40340fb9971"},
-    {file = "regex-2023.10.3-cp37-cp37m-musllinux_1_1_ppc64le.whl", hash = "sha256:f4f2ca6df64cbdd27f27b34f35adb640b5d2d77264228554e68deda54456eb11"},
-    {file = "regex-2023.10.3-cp37-cp37m-musllinux_1_1_s390x.whl", hash = "sha256:39807cbcbe406efca2a233884e169d056c35aa7e9f343d4e78665246a332f597"},
-    {file = "regex-2023.10.3-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:7eece6fbd3eae4a92d7c748ae825cbc1ee41a89bb1c3db05b5578ed3cfcfd7cb"},
-    {file = "regex-2023.10.3-cp37-cp37m-win32.whl", hash = "sha256:ce615c92d90df8373d9e13acddd154152645c0dc060871abf6bd43809673d20a"},
-    {file = "regex-2023.10.3-cp37-cp37m-win_amd64.whl", hash = "sha256:0f649fa32fe734c4abdfd4edbb8381c74abf5f34bc0b3271ce687b23729299ed"},
-    {file = "regex-2023.10.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:9b98b7681a9437262947f41c7fac567c7e1f6eddd94b0483596d320092004533"},
-    {file = "regex-2023.10.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:91dc1d531f80c862441d7b66c4505cd6ea9d312f01fb2f4654f40c6fdf5cc37a"},
-    {file = "regex-2023.10.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:82fcc1f1cc3ff1ab8a57ba619b149b907072e750815c5ba63e7aa2e1163384a4"},
-    {file = "regex-2023.10.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:7979b834ec7a33aafae34a90aad9f914c41fd6eaa8474e66953f3f6f7cbd4368"},
-    {file = "regex-2023.10.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:ef71561f82a89af6cfcbee47f0fabfdb6e63788a9258e913955d89fdd96902ab"},
-    {file = "regex-2023.10.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:dd829712de97753367153ed84f2de752b86cd1f7a88b55a3a775eb52eafe8a94"},
-    {file = "regex-2023.10.3-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:00e871d83a45eee2f8688d7e6849609c2ca2a04a6d48fba3dff4deef35d14f07"},
-    {file = "regex-2023.10.3-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:706e7b739fdd17cb89e1fbf712d9dc21311fc2333f6d435eac2d4ee81985098c"},
-    {file = "regex-2023.10.3-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:cc3f1c053b73f20c7ad88b0d1d23be7e7b3901229ce89f5000a8399746a6e039"},
-    {file = "regex-2023.10.3-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:6f85739e80d13644b981a88f529d79c5bdf646b460ba190bffcaf6d57b2a9863"},
-    {file = "regex-2023.10.3-cp38-cp38-musllinux_1_1_ppc64le.whl", hash = "sha256:741ba2f511cc9626b7561a440f87d658aabb3d6b744a86a3c025f866b4d19e7f"},
-    {file = "regex-2023.10.3-cp38-cp38-musllinux_1_1_s390x.whl", hash = "sha256:e77c90ab5997e85901da85131fd36acd0ed2221368199b65f0d11bca44549711"},
-    {file = "regex-2023.10.3-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:979c24cbefaf2420c4e377ecd1f165ea08cc3d1fbb44bdc51bccbbf7c66a2cb4"},
-    {file = "regex-2023.10.3-cp38-cp38-win32.whl", hash = "sha256:58837f9d221744d4c92d2cf7201c6acd19623b50c643b56992cbd2b745485d3d"},
-    {file = "regex-2023.10.3-cp38-cp38-win_amd64.whl", hash = "sha256:c55853684fe08d4897c37dfc5faeff70607a5f1806c8be148f1695be4a63414b"},
-    {file = "regex-2023.10.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:2c54e23836650bdf2c18222c87f6f840d4943944146ca479858404fedeb9f9af"},
-    {file = "regex-2023.10.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:69c0771ca5653c7d4b65203cbfc5e66db9375f1078689459fe196fe08b7b4930"},
-    {file = "regex-2023.10.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:6ac965a998e1388e6ff2e9781f499ad1eaa41e962a40d11c7823c9952c77123e"},
-    {file = "regex-2023.10.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1c0e8fae5b27caa34177bdfa5a960c46ff2f78ee2d45c6db15ae3f64ecadde14"},
-    {file = "regex-2023.10.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6c56c3d47da04f921b73ff9415fbaa939f684d47293f071aa9cbb13c94afc17d"},
-    {file = "regex-2023.10.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7ef1e014eed78ab650bef9a6a9cbe50b052c0aebe553fb2881e0453717573f52"},
-    {file = "regex-2023.10.3-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:d29338556a59423d9ff7b6eb0cb89ead2b0875e08fe522f3e068b955c3e7b59b"},
-    {file = "regex-2023.10.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:9c6d0ced3c06d0f183b73d3c5920727268d2201aa0fe6d55c60d68c792ff3588"},
-    {file = "regex-2023.10.3-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:994645a46c6a740ee8ce8df7911d4aee458d9b1bc5639bc968226763d07f00fa"},
-    {file = "regex-2023.10.3-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:66e2fe786ef28da2b28e222c89502b2af984858091675044d93cb50e6f46d7af"},
-    {file = "regex-2023.10.3-cp39-cp39-musllinux_1_1_ppc64le.whl", hash = "sha256:11175910f62b2b8c055f2b089e0fedd694fe2be3941b3e2633653bc51064c528"},
-    {file = "regex-2023.10.3-cp39-cp39-musllinux_1_1_s390x.whl", hash = "sha256:06e9abc0e4c9ab4779c74ad99c3fc10d3967d03114449acc2c2762ad4472b8ca"},
-    {file = "regex-2023.10.3-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:fb02e4257376ae25c6dd95a5aec377f9b18c09be6ebdefa7ad209b9137b73d48"},
-    {file = "regex-2023.10.3-cp39-cp39-win32.whl", hash = "sha256:3b2c3502603fab52d7619b882c25a6850b766ebd1b18de3df23b2f939360e1bd"},
-    {file = "regex-2023.10.3-cp39-cp39-win_amd64.whl", hash = "sha256:adbccd17dcaff65704c856bd29951c58a1bd4b2b0f8ad6b826dbd543fe740988"},
-    {file = "regex-2023.10.3.tar.gz", hash = "sha256:3fef4f844d2290ee0ba57addcec17eec9e3df73f10a2748485dfd6a3a188cc0f"},
-]
-
-[[package]]
-name = "requests"
-version = "2.31.0"
-description = "Python HTTP for Humans."
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "requests-2.31.0-py3-none-any.whl", hash = "sha256:58cd2187c01e70e6e26505bca751777aa9f2ee0b7f4300988b709f44e013003f"},
-    {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
-]
-
-[package.dependencies]
-certifi = ">=2017.4.17"
-charset-normalizer = ">=2,<4"
-idna = ">=2.5,<4"
-urllib3 = ">=1.21.1,<3"
-
-[package.extras]
-socks = ["PySocks (>=1.5.6,!=1.5.7)"]
-use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
-
-[[package]]
-name = "safetensors"
-version = "0.4.1"
-description = ""
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "safetensors-0.4.1-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:cba01c6b76e01ec453933b3b3c0157c59b52881c83eaa0f7666244e71aa75fd1"},
-    {file = "safetensors-0.4.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:7a8f6f679d97ea0135c7935c202feefbd042c149aa70ee759855e890c01c7814"},
-    {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bbc2ce1f5ae5143a7fb72b71fa71db6a42b4f6cf912aa3acdc6b914084778e68"},
-    {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:2d87d993eaefe6611a9c241a8bd364a5f1ffed5771c74840363a6c4ed8d868f6"},
-    {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:097e9af2efa8778cd2f0cba451784253e62fa7cc9fc73c0744d27212f7294e25"},
-    {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d10a9f7bae608ccfdc009351f01dc3d8535ff57f9488a58a4c38e45bf954fe93"},
-    {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:270b99885ec14abfd56c1d7f28ada81740a9220b4bae960c3de1c6fe84af9e4d"},
-    {file = "safetensors-0.4.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:285b52a481e7ba93e29ad4ec5841ef2c4479ef0a6c633c4e2629e0508453577b"},
-    {file = "safetensors-0.4.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:c3c9f0ca510e0de95abd6424789dcbc879942a3a4e29b0dfa99d9427bf1da75c"},
-    {file = "safetensors-0.4.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:88b4653059c903015284a9722f9a46838c654257173b279c8f6f46dbe80b612d"},
-    {file = "safetensors-0.4.1-cp310-none-win32.whl", hash = "sha256:2fe6926110e3d425c4b684a4379b7796fdc26ad7d16922ea1696c8e6ea7e920f"},
-    {file = "safetensors-0.4.1-cp310-none-win_amd64.whl", hash = "sha256:a79e16222106b2f5edbca1b8185661477d8971b659a3c814cc6f15181a9b34c8"},
-    {file = "safetensors-0.4.1-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:d93321eea0dd7e81b283e47a1d20dee6069165cc158286316d0d06d340de8fe8"},
-    {file = "safetensors-0.4.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:8ff8e41c8037db17de0ea2a23bc684f43eaf623be7d34906fe1ac10985b8365e"},
-    {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:39d36f1d88468a87c437a1bc27c502e71b6ca44c385a9117a9f9ba03a75cc9c6"},
-    {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7ef010e9afcb4057fb6be3d0a0cfa07aac04fe97ef73fe4a23138d8522ba7c17"},
-    {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:b287304f2b2220d51ccb51fd857761e78bcffbeabe7b0238f8dc36f2edfd9542"},
-    {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e09000b2599e1836314430f81a3884c66a5cbabdff5d9f175b5d560d4de38d78"},
-    {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e9c80ce0001efa16066358d2dd77993adc25f5a6c61850e4ad096a2232930bce"},
-    {file = "safetensors-0.4.1-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:413e1f6ac248f7d1b755199a06635e70c3515493d3b41ba46063dec33aa2ebb7"},
-    {file = "safetensors-0.4.1-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:d3ac139377cfe71ba04573f1cda66e663b7c3e95be850e9e6c2dd4b5984bd513"},
-    {file = "safetensors-0.4.1-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:04157d008385bea66d12fe90844a80d4a76dc25ec5230b5bd9a630496d1b7c03"},
-    {file = "safetensors-0.4.1-cp311-none-win32.whl", hash = "sha256:5f25297148ec665f0deb8bd67e9564634d8d6841041ab5393ccfe203379ea88b"},
-    {file = "safetensors-0.4.1-cp311-none-win_amd64.whl", hash = "sha256:b2f8877990a72ff595507b80f4b69036a9a1986a641f8681adf3425d97d3d2a5"},
-    {file = "safetensors-0.4.1-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:eb2c1da1cc39509d1a55620a5f4d14f8911c47a89c926a96e6f4876e864375a3"},
-    {file = "safetensors-0.4.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:303d2c0415cf15a28f8d7f17379ea3c34c2b466119118a34edd9965983a1a8a6"},
-    {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bb4cb3e37a9b961ddd68e873b29fe9ab4a081e3703412e34aedd2b7a8e9cafd9"},
-    {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:ae5497adc68669db2fed7cb2dad81e6a6106e79c9a132da3efdb6af1db1014fa"},
-    {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:3b30abd0cddfe959d1daedf92edcd1b445521ebf7ddefc20860ed01486b33c90"},
-    {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d784a98c492c751f228a4a894c3b8a092ff08b24e73b5568938c28b8c0e8f8df"},
-    {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e57a5ab08b0ec7a7caf30d2ac79bb30c89168431aca4f8854464bb9461686925"},
-    {file = "safetensors-0.4.1-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:edcf3121890b5f0616aa5a54683b1a5d2332037b970e507d6bb7841a3a596556"},
-    {file = "safetensors-0.4.1-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:fdb58dee173ef33634c3016c459d671ca12d11e6acf9db008261cbe58107e579"},
-    {file = "safetensors-0.4.1-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:780dc21eb3fd32ddd0e8c904bdb0290f2454f4ac21ae71e94f9ce72db1900a5a"},
-    {file = "safetensors-0.4.1-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:48901bd540f8a3c1791314bc5c8a170927bf7f6acddb75bf0a263d081a3637d4"},
-    {file = "safetensors-0.4.1-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:3b0b7b2d5976fbed8a05e2bbdce5816a59e6902e9e7c7e07dc723637ed539787"},
-    {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8f69903ff49cb30b9227fb5d029bea276ea20d04b06803877a420c5b1b74c689"},
-    {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:0ddd050e01f3e843aa8c1c27bf68675b8a08e385d0045487af4d70418c3cb356"},
-    {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:9a82bc2bd7a9a0e08239bdd6d7774d64121f136add93dfa344a2f1a6d7ef35fa"},
-    {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:6ace9e66a40f98a216ad661245782483cf79cf56eb2b112650bb904b0baa9db5"},
-    {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:82cbb8f4d022f2e94498cbefca900698b8ded3d4f85212f47da614001ff06652"},
-    {file = "safetensors-0.4.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:791edc10a3c359a2f5f52d5cddab0df8a45107d91027d86c3d44e57162e5d934"},
-    {file = "safetensors-0.4.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:83c2cfbe8c6304f0891e7bb378d56f66d2148972eeb5f747cd8a2246886f0d8c"},
-    {file = "safetensors-0.4.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:04dd14f53f5500eb4c4149674216ba1000670efbcf4b1b5c2643eb244e7882ea"},
-    {file = "safetensors-0.4.1-cp37-none-win32.whl", hash = "sha256:d5b3defa74f3723a388bfde2f5d488742bc4879682bd93267c09a3bcdf8f869b"},
-    {file = "safetensors-0.4.1-cp37-none-win_amd64.whl", hash = "sha256:25a043cbb59d4f75e9dd87fdf5c009dd8830105a2c57ace49b72167dd9808111"},
-    {file = "safetensors-0.4.1-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:3f6a520af7f2717c5ecba112041f2c8af1ca6480b97bf957aba81ed9642e654c"},
-    {file = "safetensors-0.4.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:c3807ac3b16288dffebb3474b555b56fe466baa677dfc16290dcd02dca1ab228"},
-    {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8b58ba13a9e82b4bc3fc221914f6ef237fe6c2adb13cede3ace64d1aacf49610"},
-    {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:dac4bb42f8679aadc59bd91a4c5a1784a758ad49d0912995945cd674089f628e"},
-    {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:911b48dc09e321a194def3a7431662ff4f03646832f3a8915bbf0f449b8a5fcb"},
-    {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:82571d20288c975c1b30b08deb9b1c3550f36b31191e1e81fae87669a92217d0"},
-    {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:da52ee0dc8ba03348ffceab767bd8230842fdf78f8a996e2a16445747143a778"},
-    {file = "safetensors-0.4.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2536b11ce665834201072e9397404170f93f3be10cca9995b909f023a04501ee"},
-    {file = "safetensors-0.4.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:998fbac99ca956c3a09fe07cc0b35fac26a521fa8865a690686d889f0ff4e4a6"},
-    {file = "safetensors-0.4.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:845be0aafabf2a60c2d482d4e93023fecffe5e5443d801d7a7741bae9de41233"},
-    {file = "safetensors-0.4.1-cp38-none-win32.whl", hash = "sha256:ce7a28bc8af685a69d7e869d09d3e180a275e3281e29cf5f1c7319e231932cc7"},
-    {file = "safetensors-0.4.1-cp38-none-win_amd64.whl", hash = "sha256:e056fb9e22d118cc546107f97dc28b449d88274207dd28872bd668c86216e4f6"},
-    {file = "safetensors-0.4.1-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:bdc0d039e44a727824639824090bd8869535f729878fa248addd3dc01db30eae"},
-    {file = "safetensors-0.4.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:3c1b1d510c7aba71504ece87bf393ea82638df56303e371e5e2cf09d18977dd7"},
-    {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0bd0afd95c1e497f520e680ea01e0397c0868a3a3030e128438cf6e9e3fcd671"},
-    {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:f603bdd8deac6726d39f41688ed353c532dd53935234405d79e9eb53f152fbfb"},
-    {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:d8a85e3e47e0d4eebfaf9a58b40aa94f977a56050cb5598ad5396a9ee7c087c6"},
-    {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e0ccb5aa0f3be2727117e5631200fbb3a5b3a2b3757545a92647d6dd8be6658f"},
-    {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d784938534e255473155e4d9f276ee69eb85455b6af1292172c731409bf9adee"},
-    {file = "safetensors-0.4.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a257de175c254d39ccd6a21341cd62eb7373b05c1e618a78096a56a857e0c316"},
-    {file = "safetensors-0.4.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:6fd80f7794554091836d4d613d33a7d006e2b8d6ba014d06f97cebdfda744f64"},
-    {file = "safetensors-0.4.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:35803201d980efcf964b75a0a2aee97fe5e9ecc5f3ad676b38fafdfe98e0620d"},
-    {file = "safetensors-0.4.1-cp39-none-win32.whl", hash = "sha256:7ff8a36e0396776d3ed9a106fc9a9d7c55d4439ca9a056a24bf66d343041d3e6"},
-    {file = "safetensors-0.4.1-cp39-none-win_amd64.whl", hash = "sha256:bfa2e20342b81921b98edba52f8deb68843fa9c95250739a56b52ceda5ea5c61"},
-    {file = "safetensors-0.4.1-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:ae2d5a31cfb8a973a318f7c4d2cffe0bd1fe753cdf7bb41a1939d45a0a06f964"},
-    {file = "safetensors-0.4.1-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:1a45dbf03e8334d3a5dc93687d98b6dc422f5d04c7d519dac09b84a3c87dd7c6"},
-    {file = "safetensors-0.4.1-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2297b359d91126c0f9d4fd17bae3cfa2fe3a048a6971b8db07db746ad92f850c"},
-    {file = "safetensors-0.4.1-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:bda3d98e2bcece388232cfc551ebf063b55bdb98f65ab54df397da30efc7dcc5"},
-    {file = "safetensors-0.4.1-pp310-pypy310_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:f8934bdfd202ebd0697040a3dff40dd77bc4c5bbf3527ede0532f5e7fb4d970f"},
-    {file = "safetensors-0.4.1-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:42c3710cec7e5c764c7999697516370bee39067de0aa089b7e2cfb97ac8c6b20"},
-    {file = "safetensors-0.4.1-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:53134226053e56bd56e73f7db42596e7908ed79f3c9a1016e4c1dade593ac8e5"},
-    {file = "safetensors-0.4.1-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:257d59e40a1b367cb544122e7451243d65b33c3f34d822a347f4eea6fdf97fdf"},
-    {file = "safetensors-0.4.1-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2d54c2f1826e790d1eb2d2512bfd0ee443f0206b423d6f27095057c7f18a0687"},
-    {file = "safetensors-0.4.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:645b3f1138fce6e818e79d4128afa28f0657430764cc045419c1d069ff93f732"},
-    {file = "safetensors-0.4.1-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:e9a7ffb1e551c6df51d267f5a751f042b183df22690f6feceac8d27364fd51d7"},
-    {file = "safetensors-0.4.1-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:44e230fbbe120de564b64f63ef3a8e6ff02840fa02849d9c443d56252a1646d4"},
-    {file = "safetensors-0.4.1-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:9d16b3b2fcc6fca012c74bd01b5619c655194d3e3c13e4d4d0e446eefa39a463"},
-    {file = "safetensors-0.4.1-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:5d95ea4d8b32233910734a904123bdd3979c137c461b905a5ed32511defc075f"},
-    {file = "safetensors-0.4.1-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:dab431699b5d45e0ca043bc580651ce9583dda594e62e245b7497adb32e99809"},
-    {file = "safetensors-0.4.1-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:16d8bbb7344e39cb9d4762e85c21df94ebeb03edac923dd94bb9ed8c10eac070"},
-    {file = "safetensors-0.4.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1faf5111c66a6ba91f85dff2e36edaaf36e6966172703159daeef330de4ddc7b"},
-    {file = "safetensors-0.4.1-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:660ca1d8bff6c7bc7c6b30b9b32df74ef3ab668f5df42cefd7588f0d40feadcb"},
-    {file = "safetensors-0.4.1-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:ae2f67f04ed0bb2e56fd380a8bd3eef03f609df53f88b6f5c7e89c08e52aae00"},
-    {file = "safetensors-0.4.1-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:c8ed5d2c04cdc1afc6b3c28d59580448ac07732c50d94c15e14670f9c473a2ce"},
-    {file = "safetensors-0.4.1-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:2b6a2814278b6660261aa9a9aae524616de9f1ec364e3716d219b6ed8f91801f"},
-    {file = "safetensors-0.4.1-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:3cfd1ca35eacc635f0eaa894e5c5ed83ffebd0f95cac298fd430014fa7323631"},
-    {file = "safetensors-0.4.1-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4177b456c6b0c722d82429127b5beebdaf07149d265748e97e0a34ff0b3694c8"},
-    {file = "safetensors-0.4.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:313e8472197bde54e3ec54a62df184c414582979da8f3916981b6a7954910a1b"},
-    {file = "safetensors-0.4.1-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:fdb4adb76e21bad318210310590de61c9f4adcef77ee49b4a234f9dc48867869"},
-    {file = "safetensors-0.4.1-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:1d568628e9c43ca15eb96c217da73737c9ccb07520fafd8a1eba3f2750614105"},
-    {file = "safetensors-0.4.1-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:573b6023a55a2f28085fc0a84e196c779b6cbef4d9e73acea14c8094fee7686f"},
-    {file = "safetensors-0.4.1.tar.gz", hash = "sha256:2304658e6ada81a5223225b4efe84748e760c46079bffedf7e321763cafb36c9"},
-]
-
-[package.extras]
-all = ["safetensors[jax]", "safetensors[numpy]", "safetensors[paddlepaddle]", "safetensors[pinned-tf]", "safetensors[quality]", "safetensors[testing]", "safetensors[torch]"]
-dev = ["safetensors[all]"]
-jax = ["flax (>=0.6.3)", "jax (>=0.3.25)", "jaxlib (>=0.3.25)", "safetensors[numpy]"]
-numpy = ["numpy (>=1.21.6)"]
-paddlepaddle = ["paddlepaddle (>=2.4.1)", "safetensors[numpy]"]
-pinned-tf = ["safetensors[numpy]", "tensorflow (==2.11.0)"]
-quality = ["black (==22.3)", "click (==8.0.4)", "flake8 (>=3.8.3)", "isort (>=5.5.4)"]
-tensorflow = ["safetensors[numpy]", "tensorflow (>=2.11.0)"]
-testing = ["h5py (>=3.7.0)", "huggingface_hub (>=0.12.1)", "hypothesis (>=6.70.2)", "pytest (>=7.2.0)", "pytest-benchmark (>=4.0.0)", "safetensors[numpy]", "setuptools_rust (>=1.5.2)"]
-torch = ["safetensors[numpy]", "torch (>=1.10)"]
-
-[[package]]
-name = "scipy"
-version = "1.10.1"
-description = "Fundamental algorithms for scientific computing in Python"
-optional = false
-python-versions = "<3.12,>=3.8"
-files = [
-    {file = "scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019"},
-    {file = "scipy-1.10.1-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:4b3f429188c66603a1a5c549fb414e4d3bdc2a24792e061ffbd607d3d75fd84e"},
-    {file = "scipy-1.10.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1553b5dcddd64ba9a0d95355e63fe6c3fc303a8fd77c7bc91e77d61363f7433f"},
-    {file = "scipy-1.10.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4c0ff64b06b10e35215abce517252b375e580a6125fd5fdf6421b98efbefb2d2"},
-    {file = "scipy-1.10.1-cp310-cp310-win_amd64.whl", hash = "sha256:fae8a7b898c42dffe3f7361c40d5952b6bf32d10c4569098d276b4c547905ee1"},
-    {file = "scipy-1.10.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:0f1564ea217e82c1bbe75ddf7285ba0709ecd503f048cb1236ae9995f64217bd"},
-    {file = "scipy-1.10.1-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:d925fa1c81b772882aa55bcc10bf88324dadb66ff85d548c71515f6689c6dac5"},
-    {file = "scipy-1.10.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:aaea0a6be54462ec027de54fca511540980d1e9eea68b2d5c1dbfe084797be35"},
-    {file = "scipy-1.10.1-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:15a35c4242ec5f292c3dd364a7c71a61be87a3d4ddcc693372813c0b73c9af1d"},
-    {file = "scipy-1.10.1-cp311-cp311-win_amd64.whl", hash = "sha256:43b8e0bcb877faf0abfb613d51026cd5cc78918e9530e375727bf0625c82788f"},
-    {file = "scipy-1.10.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5678f88c68ea866ed9ebe3a989091088553ba12c6090244fdae3e467b1139c35"},
-    {file = "scipy-1.10.1-cp38-cp38-macosx_12_0_arm64.whl", hash = "sha256:39becb03541f9e58243f4197584286e339029e8908c46f7221abeea4b749fa88"},
-    {file = "scipy-1.10.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bce5869c8d68cf383ce240e44c1d9ae7c06078a9396df68ce88a1230f93a30c1"},
-    {file = "scipy-1.10.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:07c3457ce0b3ad5124f98a86533106b643dd811dd61b548e78cf4c8786652f6f"},
-    {file = "scipy-1.10.1-cp38-cp38-win_amd64.whl", hash = "sha256:049a8bbf0ad95277ffba9b3b7d23e5369cc39e66406d60422c8cfef40ccc8415"},
-    {file = "scipy-1.10.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cd9f1027ff30d90618914a64ca9b1a77a431159df0e2a195d8a9e8a04c78abf9"},
-    {file = "scipy-1.10.1-cp39-cp39-macosx_12_0_arm64.whl", hash = "sha256:79c8e5a6c6ffaf3a2262ef1be1e108a035cf4f05c14df56057b64acc5bebffb6"},
-    {file = "scipy-1.10.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:51af417a000d2dbe1ec6c372dfe688e041a7084da4fdd350aeb139bd3fb55353"},
-    {file = "scipy-1.10.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1b4735d6c28aad3cdcf52117e0e91d6b39acd4272f3f5cd9907c24ee931ad601"},
-    {file = "scipy-1.10.1-cp39-cp39-win_amd64.whl", hash = "sha256:7ff7f37b1bf4417baca958d254e8e2875d0cc23aaadbe65b3d5b3077b0eb23ea"},
-    {file = "scipy-1.10.1.tar.gz", hash = "sha256:2cf9dfb80a7b4589ba4c40ce7588986d6d5cebc5457cad2c2880f6bc2d42f3a5"},
-]
-
-[package.dependencies]
-numpy = ">=1.19.5,<1.27.0"
-
-[package.extras]
-dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"]
-doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
-test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
-
-[[package]]
-name = "six"
-version = "1.16.0"
-description = "Python 2 and 3 compatibility utilities"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
-files = [
-    {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
-    {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
-]
-
-[[package]]
-name = "supervision"
-version = "0.17.0rc4"
-description = "A set of easy-to-use utils that will come in handy in any Computer Vision project"
-optional = false
-python-versions = ">=3.8,<3.12.0"
-files = [
-    {file = "supervision-0.17.0rc4-py3-none-any.whl", hash = "sha256:3e2573b78de8a724a7be8c2248c75daa2003f61834fbb686df1b0700cb87a736"},
-    {file = "supervision-0.17.0rc4.tar.gz", hash = "sha256:2442e2f747a1fd3512cb21e3a727e5cf013095f954da1aa7a3cddfb94c0b866c"},
-]
-
-[package.dependencies]
-matplotlib = ">=3.7.1,<4.0.0"
-numpy = ">=1.20.0,<2.0.0"
-opencv-python-headless = ">=4.8.0.74,<5.0.0.0"
-pillow = ">=9.4,<11.0"
-pyyaml = ">=6.0,<7.0"
-scipy = ">=1.9.0,<2.0.0"
-
-[package.extras]
-assets = ["requests (>=2.31.0,<3.0.0)", "tqdm (>=4.66.1,<5.0.0)"]
-desktop = ["opencv-python (>=4.8.0.74,<5.0.0.0)"]
-
-[[package]]
-name = "tokenizers"
-version = "0.15.0"
-description = ""
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "tokenizers-0.15.0-cp310-cp310-macosx_10_7_x86_64.whl", hash = "sha256:cd3cd0299aaa312cd2988957598f80becd04d5a07338741eca076057a2b37d6e"},
-    {file = "tokenizers-0.15.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8a922c492c721744ee175f15b91704be2d305569d25f0547c77cd6c9f210f9dc"},
-    {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:331dd786d02fc38698f835fff61c99480f98b73ce75a4c65bd110c9af5e4609a"},
-    {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:88dd0961c437d413ab027f8b115350c121d49902cfbadf08bb8f634b15fa1814"},
-    {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:6fdcc55339df7761cd52e1fbe8185d3b3963bc9e3f3545faa6c84f9e8818259a"},
-    {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f1480b0051d8ab5408e8e4db2dc832f7082ea24aa0722c427bde2418c6f3bd07"},
-    {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9855e6c258918f9cf62792d4f6ddfa6c56dccd8c8118640f867f6393ecaf8bd7"},
-    {file = "tokenizers-0.15.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:de9529fe75efcd54ba8d516aa725e1851df9199f0669b665c55e90df08f5af86"},
-    {file = "tokenizers-0.15.0-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:8edcc90a36eab0705fe9121d6c77c6e42eeef25c7399864fd57dfb27173060bf"},
-    {file = "tokenizers-0.15.0-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:ae17884aafb3e94f34fb7cfedc29054f5f54e142475ebf8a265a4e388fee3f8b"},
-    {file = "tokenizers-0.15.0-cp310-none-win32.whl", hash = "sha256:9a3241acdc9b44cff6e95c4a55b9be943ef3658f8edb3686034d353734adba05"},
-    {file = "tokenizers-0.15.0-cp310-none-win_amd64.whl", hash = "sha256:4b31807cb393d6ea31926b307911c89a1209d5e27629aa79553d1599c8ffdefe"},
-    {file = "tokenizers-0.15.0-cp311-cp311-macosx_10_7_x86_64.whl", hash = "sha256:af7e9be8c05d30bb137b9fd20f9d99354816599e5fd3d58a4b1e28ba3b36171f"},
-    {file = "tokenizers-0.15.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:c3d7343fa562ea29661783344a2d83662db0d3d17a6fa6a403cac8e512d2d9fd"},
-    {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:32371008788aeeb0309a9244809a23e4c0259625e6b74a103700f6421373f395"},
-    {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:ca9db64c7c9954fbae698884c5bb089764edc549731e5f9b7fa1dd4e4d78d77f"},
-    {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:dbed5944c31195514669cf6381a0d8d47f164943000d10f93d6d02f0d45c25e0"},
-    {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:aab16c4a26d351d63e965b0c792f5da7227a37b69a6dc6d922ff70aa595b1b0c"},
-    {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:3c2b60b12fdd310bf85ce5d7d3f823456b9b65eed30f5438dd7761879c495983"},
-    {file = "tokenizers-0.15.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0344d6602740e44054a9e5bbe9775a5e149c4dddaff15959bb07dcce95a5a859"},
-    {file = "tokenizers-0.15.0-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:4525f6997d81d9b6d9140088f4f5131f6627e4c960c2c87d0695ae7304233fc3"},
-    {file = "tokenizers-0.15.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:65975094fef8cc68919644936764efd2ce98cf1bacbe8db2687155d2b0625bee"},
-    {file = "tokenizers-0.15.0-cp311-none-win32.whl", hash = "sha256:ff5d2159c5d93015f5a4542aac6c315506df31853123aa39042672031768c301"},
-    {file = "tokenizers-0.15.0-cp311-none-win_amd64.whl", hash = "sha256:2dd681b53cf615e60a31a115a3fda3980e543d25ca183797f797a6c3600788a3"},
-    {file = "tokenizers-0.15.0-cp312-cp312-macosx_10_7_x86_64.whl", hash = "sha256:c9cce6ee149a3d703f86877bc2a6d997e34874b2d5a2d7839e36b2273f31d3d9"},
-    {file = "tokenizers-0.15.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:4a0a94bc3370e6f1cc8a07a8ae867ce13b7c1b4291432a773931a61f256d44ea"},
-    {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:309cfcccfc7e502cb1f1de2c9c1c94680082a65bfd3a912d5a5b2c90c677eb60"},
-    {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8413e994dd7d875ab13009127fc85633916c71213917daf64962bafd488f15dc"},
-    {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:d0ebf9430f901dbdc3dcb06b493ff24a3644c9f88c08e6a1d6d0ae2228b9b818"},
-    {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:10361e9c7864b22dd791ec5126327f6c9292fb1d23481d4895780688d5e298ac"},
-    {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:babe42635b8a604c594bdc56d205755f73414fce17ba8479d142a963a6c25cbc"},
-    {file = "tokenizers-0.15.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3768829861e964c7a4556f5f23307fce6a23872c2ebf030eb9822dbbbf7e9b2a"},
-    {file = "tokenizers-0.15.0-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:9c91588a630adc88065e1c03ac6831e3e2112558869b9ebcb2b8afd8a14c944d"},
-    {file = "tokenizers-0.15.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:77606994e793ca54ecf3a3619adc8a906a28ca223d9354b38df41cb8766a0ed6"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-macosx_10_7_x86_64.whl", hash = "sha256:6fe143939f3b596681922b2df12a591a5b010e7dcfbee2202482cd0c1c2f2459"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-macosx_11_0_arm64.whl", hash = "sha256:b7bee0f1795e3e3561e9a557061b1539e5255b8221e3f928f58100282407e090"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:5d37e7f4439b4c46192ab4f2ff38ab815e4420f153caa13dec9272ef14403d34"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:caadf255cf7f951b38d10097836d1f3bcff4aeaaffadfdf748bab780bf5bff95"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:05accb9162bf711a941b1460b743d62fec61c160daf25e53c5eea52c74d77814"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:26a2ef890740127cb115ee5260878f4a677e36a12831795fd7e85887c53b430b"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e54c5f26df14913620046b33e822cb3bcd091a332a55230c0e63cc77135e2169"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:669b8ed653a578bcff919566631156f5da3aab84c66f3c0b11a6281e8b4731c7"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:0ea480d943297df26f06f508dab6e012b07f42bf3dffdd36e70799368a5f5229"},
-    {file = "tokenizers-0.15.0-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:bc80a0a565ebfc7cd89de7dd581da8c2b3238addfca6280572d27d763f135f2f"},
-    {file = "tokenizers-0.15.0-cp37-none-win32.whl", hash = "sha256:cdd945e678bbdf4517d5d8de66578a5030aeefecdb46f5320b034de9cad8d4dd"},
-    {file = "tokenizers-0.15.0-cp37-none-win_amd64.whl", hash = "sha256:1ab96ab7dc706e002c32b2ea211a94c1c04b4f4de48354728c3a6e22401af322"},
-    {file = "tokenizers-0.15.0-cp38-cp38-macosx_10_7_x86_64.whl", hash = "sha256:f21c9eb71c9a671e2a42f18b456a3d118e50c7f0fc4dd9fa8f4eb727fea529bf"},
-    {file = "tokenizers-0.15.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2a5f4543a35889679fc3052086e69e81880b2a5a28ff2a52c5a604be94b77a3f"},
-    {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f8aa81afec893e952bd39692b2d9ef60575ed8c86fce1fd876a06d2e73e82dca"},
-    {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1574a5a4af22c3def93fe8fe4adcc90a39bf5797ed01686a4c46d1c3bc677d2f"},
-    {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:7c7982fd0ec9e9122d03b209dac48cebfea3de0479335100ef379a9a959b9a5a"},
-    {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:f8d16b647032df2ce2c1f9097236e046ea9fedd969b25637b9d5d734d78aa53b"},
-    {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:b3cdf29e6f9653da330515dc8fa414be5a93aae79e57f8acc50d4028dd843edf"},
-    {file = "tokenizers-0.15.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7286f3df10de840867372e3e64b99ef58c677210e3ceb653cd0e740a5c53fe78"},
-    {file = "tokenizers-0.15.0-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:aabc83028baa5a36ce7a94e7659250f0309c47fa4a639e5c2c38e6d5ea0de564"},
-    {file = "tokenizers-0.15.0-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:72f78b0e0e276b1fc14a672fa73f3acca034ba8db4e782124a2996734a9ba9cf"},
-    {file = "tokenizers-0.15.0-cp38-none-win32.whl", hash = "sha256:9680b0ecc26e7e42f16680c1aa62e924d58d1c2dd992707081cc10a374896ea2"},
-    {file = "tokenizers-0.15.0-cp38-none-win_amd64.whl", hash = "sha256:f17cbd88dab695911cbdd385a5a7e3709cc61dff982351f5d1b5939f074a2466"},
-    {file = "tokenizers-0.15.0-cp39-cp39-macosx_10_7_x86_64.whl", hash = "sha256:3661862df7382c5eb23ac4fbf7c75e69b02dc4f5784e4c5a734db406b5b24596"},
-    {file = "tokenizers-0.15.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:c3045d191dad49647f5a5039738ecf1c77087945c7a295f7bcf051c37067e883"},
-    {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:a9fcaad9ab0801f14457d7c820d9f246b5ab590c407fc6b073819b1573097aa7"},
-    {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a79f17027f24fe9485701c8dbb269b9c713954ec3bdc1e7075a66086c0c0cd3c"},
-    {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_armv7l.manylinux2014_armv7l.whl", hash = "sha256:01a3aa332abc4bee7640563949fcfedca4de8f52691b3b70f2fc6ca71bfc0f4e"},
-    {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:05b83896a893cdfedad8785250daa3ba9f0504848323471524d4783d7291661e"},
-    {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cbbf2489fcf25d809731ba2744ff278dd07d9eb3f8b7482726bd6cae607073a4"},
-    {file = "tokenizers-0.15.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ab806ad521a5e9de38078b7add97589c313915f6f5fec6b2f9f289d14d607bd6"},
-    {file = "tokenizers-0.15.0-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:4a522612d5c88a41563e3463226af64e2fa00629f65cdcc501d1995dd25d23f5"},
-    {file = "tokenizers-0.15.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:e58a38c4e6075810bdfb861d9c005236a72a152ebc7005941cc90d1bbf16aca9"},
-    {file = "tokenizers-0.15.0-cp39-none-win32.whl", hash = "sha256:b8034f1041fd2bd2b84ff9f4dc4ae2e1c3b71606820a9cd5c562ebd291a396d1"},
-    {file = "tokenizers-0.15.0-cp39-none-win_amd64.whl", hash = "sha256:edde9aa964145d528d0e0dbf14f244b8a85ebf276fb76869bc02e2530fa37a96"},
-    {file = "tokenizers-0.15.0-pp310-pypy310_pp73-macosx_10_7_x86_64.whl", hash = "sha256:309445d10d442b7521b98083dc9f0b5df14eca69dbbfebeb98d781ee2cef5d30"},
-    {file = "tokenizers-0.15.0-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:d3125a6499226d4d48efc54f7498886b94c418e93a205b673bc59364eecf0804"},
-    {file = "tokenizers-0.15.0-pp310-pypy310_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:ed56ddf0d54877bb9c6d885177db79b41576e61b5ef6defeb579dcb803c04ad5"},
-    {file = "tokenizers-0.15.0-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3b22cd714706cc5b18992a232b023f736e539495f5cc61d2d28d176e55046f6c"},
-    {file = "tokenizers-0.15.0-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fac2719b1e9bc8e8e7f6599b99d0a8e24f33d023eb8ef644c0366a596f0aa926"},
-    {file = "tokenizers-0.15.0-pp310-pypy310_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:85ddae17570ec7e5bfaf51ffa78d044f444a8693e1316e1087ee6150596897ee"},
-    {file = "tokenizers-0.15.0-pp310-pypy310_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:76f1bed992e396bf6f83e3df97b64ff47885e45e8365f8983afed8556a0bc51f"},
-    {file = "tokenizers-0.15.0-pp37-pypy37_pp73-macosx_10_7_x86_64.whl", hash = "sha256:3bb0f4df6dce41a1c7482087b60d18c372ef4463cb99aa8195100fcd41e0fd64"},
-    {file = "tokenizers-0.15.0-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:22c27672c27a059a5f39ff4e49feed8c7f2e1525577c8a7e3978bd428eb5869d"},
-    {file = "tokenizers-0.15.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:78104f5d035c9991f92831fc0efe9e64a05d4032194f2a69f67aaa05a4d75bbb"},
-    {file = "tokenizers-0.15.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a40b73dc19d82c3e3ffb40abdaacca8fbc95eeb26c66b7f9f860aebc07a73998"},
-    {file = "tokenizers-0.15.0-pp37-pypy37_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:d801d1368188c74552cd779b1286e67cb9fd96f4c57a9f9a2a09b6def9e1ab37"},
-    {file = "tokenizers-0.15.0-pp37-pypy37_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:82641ffb13a4da1293fcc9f437d457647e60ed0385a9216cd135953778b3f0a1"},
-    {file = "tokenizers-0.15.0-pp38-pypy38_pp73-macosx_10_7_x86_64.whl", hash = "sha256:160f9d1810f2c18fffa94aa98bf17632f6bd2dabc67fcb01a698ca80c37d52ee"},
-    {file = "tokenizers-0.15.0-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:8d7d6eea831ed435fdeeb9bcd26476226401d7309d115a710c65da4088841948"},
-    {file = "tokenizers-0.15.0-pp38-pypy38_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:f6456bec6c557d63d8ec0023758c32f589e1889ed03c055702e84ce275488bed"},
-    {file = "tokenizers-0.15.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1eef39a502fad3bf104b9e1906b4fb0cee20e44e755e51df9a98f8922c3bf6d4"},
-    {file = "tokenizers-0.15.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c1e4664c5b797e093c19b794bbecc19d2367e782b4a577d8b7c1821db5dc150d"},
-    {file = "tokenizers-0.15.0-pp38-pypy38_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:ca003fb5f3995ff5cf676db6681b8ea5d54d3b30bea36af1120e78ee1a4a4cdf"},
-    {file = "tokenizers-0.15.0-pp38-pypy38_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:7f17363141eb0c53752c89e10650b85ef059a52765d0802ba9613dbd2d21d425"},
-    {file = "tokenizers-0.15.0-pp39-pypy39_pp73-macosx_10_7_x86_64.whl", hash = "sha256:8a765db05581c7d7e1280170f2888cda351760d196cc059c37ea96f121125799"},
-    {file = "tokenizers-0.15.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:2a0dd641a72604486cd7302dd8f87a12c8a9b45e1755e47d2682733f097c1af5"},
-    {file = "tokenizers-0.15.0-pp39-pypy39_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:0a1a3c973e4dc97797fc19e9f11546c95278ffc55c4492acb742f69e035490bc"},
-    {file = "tokenizers-0.15.0-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4fab75642aae4e604e729d6f78e0addb9d7e7d49e28c8f4d16b24da278e5263"},
-    {file = "tokenizers-0.15.0-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:65f80be77f6327a86d8fd35a4467adcfe6174c159b4ab52a1a8dd4c6f2d7d9e1"},
-    {file = "tokenizers-0.15.0-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:a8da7533dbe66b88afd430c56a2f2ce1fd82e2681868f857da38eeb3191d7498"},
-    {file = "tokenizers-0.15.0-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:fa8eb4584fc6cbe6a84d7a7864be3ed28e23e9fd2146aa8ef1814d579df91958"},
-    {file = "tokenizers-0.15.0.tar.gz", hash = "sha256:10c7e6e7b4cabd757da59e93f5f8d1126291d16f8b54f28510825ef56a3e5d0e"},
-]
-
-[package.dependencies]
-huggingface_hub = ">=0.16.4,<1.0"
-
-[package.extras]
-dev = ["tokenizers[testing]"]
-docs = ["setuptools_rust", "sphinx", "sphinx_rtd_theme"]
-testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"]
-
-[[package]]
-name = "tomli"
-version = "2.0.1"
-description = "A lil' TOML parser"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"},
-    {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
-]
-
-[[package]]
-name = "tqdm"
-version = "4.66.1"
-description = "Fast, Extensible Progress Meter"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "tqdm-4.66.1-py3-none-any.whl", hash = "sha256:d302b3c5b53d47bce91fea46679d9c3c6508cf6332229aa1e7d8653723793386"},
-    {file = "tqdm-4.66.1.tar.gz", hash = "sha256:d88e651f9db8d8551a62556d3cff9e3034274ca5d66e93197cf2490e2dcb69c7"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "platform_system == \"Windows\""}
-
-[package.extras]
-dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
-notebook = ["ipywidgets (>=6)"]
-slack = ["slack-sdk"]
-telegram = ["requests"]
-
-[[package]]
-name = "transformers"
-version = "4.35.2"
-description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
-optional = false
-python-versions = ">=3.8.0"
-files = [
-    {file = "transformers-4.35.2-py3-none-any.whl", hash = "sha256:9dfa76f8692379544ead84d98f537be01cd1070de75c74efb13abcbc938fbe2f"},
-    {file = "transformers-4.35.2.tar.gz", hash = "sha256:2d125e197d77b0cdb6c9201df9fa7e2101493272e448b9fba9341c695bee2f52"},
-]
-
-[package.dependencies]
-filelock = "*"
-huggingface-hub = ">=0.16.4,<1.0"
-numpy = ">=1.17"
-packaging = ">=20.0"
-pyyaml = ">=5.1"
-regex = "!=2019.12.17"
-requests = "*"
-safetensors = ">=0.3.1"
-tokenizers = ">=0.14,<0.19"
-tqdm = ">=4.27"
-
-[package.extras]
-accelerate = ["accelerate (>=0.20.3)"]
-agents = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.10,!=1.12.0)"]
-all = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
-audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-codecarbon = ["codecarbon (==1.2.0)"]
-deepspeed = ["accelerate (>=0.20.3)", "deepspeed (>=0.9.3)"]
-deepspeed-testing = ["GitPython (<3.1.19)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "deepspeed (>=0.9.3)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "optuna", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "timeout-decorator"]
-dev = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "decord (==0.6.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "flax (>=0.4.1,<=0.7.0)", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-dev-tensorflow = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "nltk", "onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "tensorboard", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timeout-decorator", "tokenizers (>=0.14,<0.19)", "urllib3 (<2.0.0)"]
-dev-torch = ["GitPython (<3.1.19)", "Pillow (<10.0.0)", "accelerate (>=0.20.3)", "beautifulsoup4", "black (>=23.1,<24.0)", "codecarbon (==1.2.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "fugashi (>=1.0)", "hf-doc-builder", "hf-doc-builder (>=0.3.0)", "ipadic (>=1.0.0,<2.0)", "isort (>=5.5.4)", "kenlm", "librosa", "nltk", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "optuna", "parameterized", "phonemizer", "protobuf", "psutil", "pyctcdecode (>=0.4.0)", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "ray[tune]", "rhoknp (>=1.1.0,<1.3.1)", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "ruff (>=0.0.241,<=0.0.259)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "scikit-learn", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "tensorboard", "timeout-decorator", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)", "urllib3 (<2.0.0)"]
-docs = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "av (==9.2.0)", "codecarbon (==1.2.0)", "decord (==0.6.0)", "flax (>=0.4.1,<=0.7.0)", "hf-doc-builder", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "kenlm", "keras-nlp (>=0.3.1)", "librosa", "onnxconverter-common", "optax (>=0.0.8,<=0.1.4)", "optuna", "phonemizer", "protobuf", "pyctcdecode (>=0.4.0)", "ray[tune]", "sentencepiece (>=0.1.91,!=0.1.92)", "sigopt", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx", "timm", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "torchaudio", "torchvision"]
-docs-specific = ["hf-doc-builder"]
-flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
-flax-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-ftfy = ["ftfy"]
-integrations = ["optuna", "ray[tune]", "sigopt"]
-ja = ["fugashi (>=1.0)", "ipadic (>=1.0.0,<2.0)", "rhoknp (>=1.1.0,<1.3.1)", "sudachidict-core (>=20220729)", "sudachipy (>=0.6.6)", "unidic (>=1.0.2)", "unidic-lite (>=1.0.7)"]
-modelcreation = ["cookiecutter (==1.7.3)"]
-natten = ["natten (>=0.14.6)"]
-onnx = ["onnxconverter-common", "onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)", "tf2onnx"]
-onnxruntime = ["onnxruntime (>=1.4.0)", "onnxruntime-tools (>=1.4.2)"]
-optuna = ["optuna"]
-quality = ["GitPython (<3.1.19)", "black (>=23.1,<24.0)", "datasets (!=2.5.0)", "hf-doc-builder (>=0.3.0)", "isort (>=5.5.4)", "ruff (>=0.0.241,<=0.0.259)", "urllib3 (<2.0.0)"]
-ray = ["ray[tune]"]
-retrieval = ["datasets (!=2.5.0)", "faiss-cpu"]
-sagemaker = ["sagemaker (>=2.31.0)"]
-sentencepiece = ["protobuf", "sentencepiece (>=0.1.91,!=0.1.92)"]
-serving = ["fastapi", "pydantic (<2)", "starlette", "uvicorn"]
-sigopt = ["sigopt"]
-sklearn = ["scikit-learn"]
-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "tensorboard", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
-tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
-timm = ["timm"]
-tokenizers = ["tokenizers (>=0.14,<0.19)"]
-torch = ["accelerate (>=0.20.3)", "torch (>=1.10,!=1.12.0)"]
-torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
-torch-vision = ["Pillow (<10.0.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.16.4,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.14,<0.19)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
-video = ["av (==9.2.0)", "decord (==0.6.0)"]
-vision = ["Pillow (<10.0.0)"]
-
-[[package]]
-name = "typing-extensions"
-version = "4.8.0"
-description = "Backported and Experimental Type Hints for Python 3.8+"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
-    {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
-]
-
-[[package]]
-name = "urllib3"
-version = "2.1.0"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
-    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
-]
-
-[package.extras]
-brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
-socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
-zstd = ["zstandard (>=0.18.0)"]
-
-[[package]]
-name = "watchdog"
-version = "3.0.0"
-description = "Filesystem events monitoring"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:336adfc6f5cc4e037d52db31194f7581ff744b67382eb6021c868322e32eef41"},
-    {file = "watchdog-3.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:a70a8dcde91be523c35b2bf96196edc5730edb347e374c7de7cd20c43ed95397"},
-    {file = "watchdog-3.0.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:adfdeab2da79ea2f76f87eb42a3ab1966a5313e5a69a0213a3cc06ef692b0e96"},
-    {file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:2b57a1e730af3156d13b7fdddfc23dea6487fceca29fc75c5a868beed29177ae"},
-    {file = "watchdog-3.0.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:7ade88d0d778b1b222adebcc0927428f883db07017618a5e684fd03b83342bd9"},
-    {file = "watchdog-3.0.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:7e447d172af52ad204d19982739aa2346245cc5ba6f579d16dac4bfec226d2e7"},
-    {file = "watchdog-3.0.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:9fac43a7466eb73e64a9940ac9ed6369baa39b3bf221ae23493a9ec4d0022674"},
-    {file = "watchdog-3.0.0-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:8ae9cda41fa114e28faf86cb137d751a17ffd0316d1c34ccf2235e8a84365c7f"},
-    {file = "watchdog-3.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:25f70b4aa53bd743729c7475d7ec41093a580528b100e9a8c5b5efe8899592fc"},
-    {file = "watchdog-3.0.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4f94069eb16657d2c6faada4624c39464f65c05606af50bb7902e036e3219be3"},
-    {file = "watchdog-3.0.0-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:7c5f84b5194c24dd573fa6472685b2a27cc5a17fe5f7b6fd40345378ca6812e3"},
-    {file = "watchdog-3.0.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:3aa7f6a12e831ddfe78cdd4f8996af9cf334fd6346531b16cec61c3b3c0d8da0"},
-    {file = "watchdog-3.0.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:233b5817932685d39a7896b1090353fc8efc1ef99c9c054e46c8002561252fb8"},
-    {file = "watchdog-3.0.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:13bbbb462ee42ec3c5723e1205be8ced776f05b100e4737518c67c8325cf6100"},
-    {file = "watchdog-3.0.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:8f3ceecd20d71067c7fd4c9e832d4e22584318983cabc013dbf3f70ea95de346"},
-    {file = "watchdog-3.0.0-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:c9d8c8ec7efb887333cf71e328e39cffbf771d8f8f95d308ea4125bf5f90ba64"},
-    {file = "watchdog-3.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0e06ab8858a76e1219e68c7573dfeba9dd1c0219476c5a44d5333b01d7e1743a"},
-    {file = "watchdog-3.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:d00e6be486affb5781468457b21a6cbe848c33ef43f9ea4a73b4882e5f188a44"},
-    {file = "watchdog-3.0.0-py3-none-manylinux2014_i686.whl", hash = "sha256:c07253088265c363d1ddf4b3cdb808d59a0468ecd017770ed716991620b8f77a"},
-    {file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:5113334cf8cf0ac8cd45e1f8309a603291b614191c9add34d33075727a967709"},
-    {file = "watchdog-3.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:51f90f73b4697bac9c9a78394c3acbbd331ccd3655c11be1a15ae6fe289a8c83"},
-    {file = "watchdog-3.0.0-py3-none-manylinux2014_s390x.whl", hash = "sha256:ba07e92756c97e3aca0912b5cbc4e5ad802f4557212788e72a72a47ff376950d"},
-    {file = "watchdog-3.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:d429c2430c93b7903914e4db9a966c7f2b068dd2ebdd2fa9b9ce094c7d459f33"},
-    {file = "watchdog-3.0.0-py3-none-win32.whl", hash = "sha256:3ed7c71a9dccfe838c2f0b6314ed0d9b22e77d268c67e015450a29036a81f60f"},
-    {file = "watchdog-3.0.0-py3-none-win_amd64.whl", hash = "sha256:4c9956d27be0bb08fc5f30d9d0179a855436e655f046d288e2bcc11adfae893c"},
-    {file = "watchdog-3.0.0-py3-none-win_ia64.whl", hash = "sha256:5d9f3a10e02d7371cd929b5d8f11e87d4bad890212ed3901f9b4d68767bee759"},
-    {file = "watchdog-3.0.0.tar.gz", hash = "sha256:4d98a320595da7a7c5a18fc48cb633c2e73cda78f93cac2ef42d42bf609a33f9"},
-]
-
-[package.extras]
-watchmedo = ["PyYAML (>=3.10)"]
-
-[[package]]
-name = "zipp"
-version = "3.17.0"
-description = "Backport of pathlib-compatible object wrapper for zip files"
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"},
-    {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"},
-]
-
-[package.extras]
-docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
-
-[metadata]
-lock-version = "2.0"
-python-versions = ">=3.8,<3.12.0"
-content-hash = "0dc130ed9149c05685afdd1b072e746263e0497e691cab4903ac0af506dcbcb0"
diff --git a/pyproject.toml b/pyproject.toml
deleted file mode 100644
index 26a2983..0000000
--- a/pyproject.toml
+++ /dev/null
@@ -1,52 +0,0 @@
-[tool.poetry]
-name = "maestro"
-version = "0.1.1rc1"
-description = "Visual Prompting for Large Multimodal Models (LMMs)"
-authors = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
-maintainers = ["Piotr Skalski <piotr.skalski92@gmail.com>"]
-readme = "README.md"
-license = "MIT"
-packages = [{include = "maestro"}]
-homepage = "https://github.com/roboflow/multimodal-maestro"
-repository = "https://github.com/roboflow/multimodal-maestro"
-documentation = "https://roboflow.github.io/multimodal-maestro"
-
-classifiers=[
-    'Development Status :: 3 - Alpha',
-    'Intended Audience :: Developers',
-    'Intended Audience :: Education',
-    'Intended Audience :: Science/Research',
-    'License :: OSI Approved :: MIT License',
-    'Programming Language :: Python :: 3',
-    'Programming Language :: Python :: 3.8',
-    'Programming Language :: Python :: 3.9',
-    'Programming Language :: Python :: 3.10',
-    'Programming Language :: Python :: 3.11',
-    'Programming Language :: Python :: 3 :: Only',
-    'Topic :: Software Development',
-    'Topic :: Scientific/Engineering',
-    "Topic :: Scientific/Engineering :: Artificial Intelligence",
-    'Typing :: Typed',
-    'Operating System :: Microsoft :: Windows',
-    'Operating System :: POSIX :: Linux',
-    'Operating System :: MacOS'
-]
-
-[tool.poetry.dependencies]
-python = ">=3.8,<3.12.0"
-supervision = "^0.17.0rc4"
-requests = "^2.31.0"
-transformers = "^4.35.2"
-
-[tool.poetry.group.dev.dependencies]
-pytest = "^7.2.2"
-
-[tool.poetry.group.docs.dependencies]
-mkdocs-material = "^9.1.4"
-mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}
-
-[tool.setuptools]
-include-package-data = false
-
-[tool.setuptools.packages.find]
-exclude = ["docs*", "test*","cookbooks*"]
\ No newline at end of file
diff --git a/requirements/requirements.docs.txt b/requirements/requirements.docs.txt
new file mode 100644
index 0000000..877109e
--- /dev/null
+++ b/requirements/requirements.docs.txt
@@ -0,0 +1,2 @@
+mkdocs-material~=9.5.33
+mkdocstrings[python]>=0.20.0,<0.25.2
diff --git a/requirements/requirements.test.txt b/requirements/requirements.test.txt
new file mode 100644
index 0000000..615272d
--- /dev/null
+++ b/requirements/requirements.test.txt
@@ -0,0 +1,5 @@
+pytest~=8.3.2
+black~=24.8.0
+pre-commit~=3.8.0
+mypy~=1.11.2
+flake8~=7.1.1
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
new file mode 100644
index 0000000..1f0c4ed
--- /dev/null
+++ b/requirements/requirements.txt
@@ -0,0 +1,4 @@
+supervision~=0.22.0
+requests>=2.31.0,<=2.32.3
+transformers~=4.44.2
+torch~=2.4.0
diff --git a/setup.py b/setup.py
new file mode 100644
index 0000000..9e16b7b
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,66 @@
+from typing import List, Union
+
+import setuptools
+from setuptools import find_packages
+
+
+with open("README.md", "r") as fh:
+    long_description = fh.read()
+
+
+def read_requirements(path: Union[str, List[str]]) -> List[str]:
+    if not isinstance(path, list):
+        path = [path]
+    requirements = []
+    for p in path:
+        with open(p) as fh:
+            requirements.extend([line.strip() for line in fh])
+    return requirements
+
+
+setuptools.setup(
+    name="maestro",
+    version="0.2.0rc1",
+    author="Roboflow",
+    author_email="help@roboflow.com",
+    description="Visual Prompting for Large Multimodal Models (LMMs)",
+    long_description=long_description,
+    long_description_content_type="text/markdown",
+    url="https://github.com/roboflow/multimodal-maestro",
+    packages=find_packages(
+        where=".",
+        exclude=(
+            "cookbooks",
+            "docs",
+            "tests",
+            "tests.*",
+            "requirements",
+        ),
+    ),
+    install_requires=read_requirements("requirements/requirements.txt"),
+    extras_require={
+        "dev": read_requirements("requirements/requirements.test.txt"),
+        "docs": read_requirements("requirements/requirements.docs.txt"),
+    },
+    classifiers=[
+        "Development Status :: 3 - Alpha",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Education",
+        "Intended Audience :: Science/Research",
+        "License :: OSI Approved :: MIT License",
+        "Programming Language :: Python :: 3",
+        "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
+        "Programming Language :: Python :: 3.11",
+        "Programming Language :: Python :: 3.12",
+        "Programming Language :: Python :: 3 :: Only",
+        "Topic :: Software Development",
+        "Topic :: Scientific/Engineering",
+        "Topic :: Scientific/Engineering :: Artificial Intelligence",
+        "Typing :: Typed",
+        "Operating System :: Microsoft :: Windows",
+        "Operating System :: POSIX :: Linux",
+        "Operating System :: MacOS",
+    ],
+    python_requires=">=3.9,<3.13",
+)

From b3eee67276edff248e9e8ed1d30b3bfa9ef988d3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Tue, 27 Aug 2024 00:27:34 +0200
Subject: [PATCH 02/69] Add basic utils

---
 maestro/trainer/__init__.py                   |  0
 maestro/trainer/common/__init__.py            |  0
 .../trainer/common/configuration/__init__.py  |  0
 maestro/trainer/common/configuration/env.py   |  2 ++
 .../trainer/common/data_loaders/__init__.py   |  0
 maestro/trainer/common/data_loaders/jsonl.py  | 31 +++++++++++++++++++
 maestro/trainer/common/utils/__init__.py      |  0
 maestro/trainer/common/utils/file_system.py   | 28 +++++++++++++++++
 .../trainer/common/utils/reproducibility.py   | 24 ++++++++++++++
 maestro/trainer/models/__init__.py            |  0
 maestro/trainer/models/florence_2/__init__.py |  0
 maestro/trainer/models/paligemma/__init__.py  |  0
 mypy.ini                                      |  2 ++
 requirements/requirements.txt                 |  3 ++
 14 files changed, 90 insertions(+)
 create mode 100644 maestro/trainer/__init__.py
 create mode 100644 maestro/trainer/common/__init__.py
 create mode 100644 maestro/trainer/common/configuration/__init__.py
 create mode 100644 maestro/trainer/common/configuration/env.py
 create mode 100644 maestro/trainer/common/data_loaders/__init__.py
 create mode 100644 maestro/trainer/common/data_loaders/jsonl.py
 create mode 100644 maestro/trainer/common/utils/__init__.py
 create mode 100644 maestro/trainer/common/utils/file_system.py
 create mode 100644 maestro/trainer/common/utils/reproducibility.py
 create mode 100644 maestro/trainer/models/__init__.py
 create mode 100644 maestro/trainer/models/florence_2/__init__.py
 create mode 100644 maestro/trainer/models/paligemma/__init__.py
 create mode 100644 mypy.ini

diff --git a/maestro/trainer/__init__.py b/maestro/trainer/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/__init__.py b/maestro/trainer/common/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/configuration/__init__.py b/maestro/trainer/common/configuration/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/configuration/env.py b/maestro/trainer/common/configuration/env.py
new file mode 100644
index 0000000..99c5e5b
--- /dev/null
+++ b/maestro/trainer/common/configuration/env.py
@@ -0,0 +1,2 @@
+SEED_ENV = "SEED"
+DEFAULT_SEED = "42"
diff --git a/maestro/trainer/common/data_loaders/__init__.py b/maestro/trainer/common/data_loaders/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/data_loaders/jsonl.py b/maestro/trainer/common/data_loaders/jsonl.py
new file mode 100644
index 0000000..4507b80
--- /dev/null
+++ b/maestro/trainer/common/data_loaders/jsonl.py
@@ -0,0 +1,31 @@
+from __future__ import annotations
+
+import random
+from typing import List
+
+from torch.utils.data import Dataset
+
+from maestro.trainer.common.utils.file_system import read_jsonl
+
+
+class Data(Dataset):
+    # TODO: implementation could be better - avoiding loading
+    #  whole files to memory
+
+    @classmethod
+    def from_jsonl_file(cls, path: str) -> Data:
+        file_content = read_jsonl(path=path)
+        random.shuffle(file_content)
+        return cls(jsons=file_content)
+
+    def __init__(self, jsons: List[dict]):
+        self.jsons = jsons
+
+    def __getitem__(self, index):
+        return self.jsons[index]
+
+    def __len__(self):
+        return len(self.jsons)
+
+    def shuffle(self):
+        random.shuffle(self.jsons)
diff --git a/maestro/trainer/common/utils/__init__.py b/maestro/trainer/common/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/common/utils/file_system.py b/maestro/trainer/common/utils/file_system.py
new file mode 100644
index 0000000..bf4d72f
--- /dev/null
+++ b/maestro/trainer/common/utils/file_system.py
@@ -0,0 +1,28 @@
+import json
+from typing import Union, List
+
+
+def read_jsonl(path: str) -> List[dict]:
+    file_lines = read_file(
+        path=path,
+        split_lines=True,
+    )
+    return [json.loads(line) for line in file_lines]
+
+
+def read_file(
+    path: str,
+    split_lines: bool = False,
+    strip_white_spaces: bool = False,
+    line_separator: str = "\n",
+) -> Union[str, List[str]]:
+    with open(path, "r") as f:
+        file_content = f.read()
+    if strip_white_spaces:
+        file_content = file_content.strip()
+    if not split_lines:
+        return file_content
+    lines = file_content.split(line_separator)
+    if not strip_white_spaces:
+        return lines
+    return [line.strip() for line in lines]
diff --git a/maestro/trainer/common/utils/reproducibility.py b/maestro/trainer/common/utils/reproducibility.py
new file mode 100644
index 0000000..f55d646
--- /dev/null
+++ b/maestro/trainer/common/utils/reproducibility.py
@@ -0,0 +1,24 @@
+import os
+import random
+from typing import Optional
+
+import torch
+import numpy as np
+
+from maestro.trainer.common.configuration.env import SEED_ENV, DEFAULT_SEED
+
+
+def set_random_generators_seeds(
+    seed: Optional[int] = None,
+    disable_cudnn_benchmark: bool = True,
+    avoid_non_deterministic_algorithms: bool = True,
+) -> None:
+    if seed is None:
+        seed = int(os.getenv(SEED_ENV, DEFAULT_SEED))
+    random.seed(seed)
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    if avoid_non_deterministic_algorithms:
+        torch.use_deterministic_algorithms(True)
+    if disable_cudnn_benchmark:
+        torch.backends.cudnn.benchmark = False
diff --git a/maestro/trainer/models/__init__.py b/maestro/trainer/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/models/florence_2/__init__.py b/maestro/trainer/models/florence_2/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/models/paligemma/__init__.py b/maestro/trainer/models/paligemma/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mypy.ini b/mypy.ini
new file mode 100644
index 0000000..61a34b0
--- /dev/null
+++ b/mypy.ini
@@ -0,0 +1,2 @@
+[mypy-requests]
+ignore_missing_imports = True
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index 1f0c4ed..a302f7d 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -2,3 +2,6 @@ supervision~=0.22.0
 requests>=2.31.0,<=2.32.3
 transformers~=4.44.2
 torch~=2.4.0
+accelerate~=0.33.0
+sentencepiece~=0.2.0
+peft~=0.12.0

From 8a0713e744f4542086e3f32ae226c953a59f6508 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Tue, 27 Aug 2024 01:24:48 +0200
Subject: [PATCH 03/69] Add basic training utils

---
 .pre-commit-config.yaml                       |   2 +
 maestro/trainer/common/configuration/env.py   |   3 +
 maestro/trainer/common/data_loaders/jsonl.py  |   4 +-
 .../trainer/common/utils/metrics_tracing.py   |  28 +++
 maestro/trainer/models/paligemma/training.py  | 220 ++++++++++++++++++
 5 files changed, 255 insertions(+), 2 deletions(-)
 create mode 100644 maestro/trainer/common/utils/metrics_tracing.py
 create mode 100644 maestro/trainer/models/paligemma/training.py

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ed3e8f3..bf39b30 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,6 +9,7 @@ repos:
     rev: 24.8.0
     hooks:
     -   id: black
+        args: [--line-length=120]
 -   repo: https://github.com/pre-commit/mirrors-mypy
     rev: v1.11.2
     hooks:
@@ -17,3 +18,4 @@ repos:
     rev: 7.1.1
     hooks:
     -   id: flake8
+        args: [--max-line-length=120]
diff --git a/maestro/trainer/common/configuration/env.py b/maestro/trainer/common/configuration/env.py
index 99c5e5b..eeb9105 100644
--- a/maestro/trainer/common/configuration/env.py
+++ b/maestro/trainer/common/configuration/env.py
@@ -1,2 +1,5 @@
 SEED_ENV = "SEED"
 DEFAULT_SEED = "42"
+CUDA_DEVICE_ENV = "CUDA_DEVICE"
+DEFAULT_CUDA_DEVICE = "cuda:0"
+HF_TOKEN_ENV = "HF_TOKEN"
diff --git a/maestro/trainer/common/data_loaders/jsonl.py b/maestro/trainer/common/data_loaders/jsonl.py
index 4507b80..3630e11 100644
--- a/maestro/trainer/common/data_loaders/jsonl.py
+++ b/maestro/trainer/common/data_loaders/jsonl.py
@@ -8,12 +8,12 @@
 from maestro.trainer.common.utils.file_system import read_jsonl
 
 
-class Data(Dataset):
+class JSONLDataset(Dataset):
     # TODO: implementation could be better - avoiding loading
     #  whole files to memory
 
     @classmethod
-    def from_jsonl_file(cls, path: str) -> Data:
+    def from_jsonl_file(cls, path: str) -> JSONLDataset:
         file_content = read_jsonl(path=path)
         random.shuffle(file_content)
         return cls(jsons=file_content)
diff --git a/maestro/trainer/common/utils/metrics_tracing.py b/maestro/trainer/common/utils/metrics_tracing.py
new file mode 100644
index 0000000..6c5b6ae
--- /dev/null
+++ b/maestro/trainer/common/utils/metrics_tracing.py
@@ -0,0 +1,28 @@
+from __future__ import annotations
+
+from typing import Dict, Tuple, List
+
+
+class MetricsTracker:
+
+    @classmethod
+    def init(cls, metrics: List[str]) -> MetricsTracker:
+        return cls(metrics={metric: [] for metric in metrics})
+
+    def __init__(self, metrics: Dict[str, List[Tuple[int, int, float]]]):
+        self._metrics = metrics
+
+    def register(self, metric: str, epoch: int, step: int, value: float) -> None:
+        self._metrics[metric].append((epoch, step, value))
+
+    def describe_metrics(self) -> List[str]:
+        return list(self._metrics.keys())
+
+    def get_metric_values(
+        self,
+        metric: str,
+        with_index: bool = True,
+    ) -> list:
+        if with_index:
+            return self._metrics[metric]
+        return [value[2] for value in self._metrics[metric]]
diff --git a/maestro/trainer/models/paligemma/training.py b/maestro/trainer/models/paligemma/training.py
new file mode 100644
index 0000000..48cb2cc
--- /dev/null
+++ b/maestro/trainer/models/paligemma/training.py
@@ -0,0 +1,220 @@
+import os
+from typing import List, Tuple, Optional, Literal, Union, Iterator
+
+from PIL import Image
+from torch import optim
+from torch.optim import Optimizer
+from torch.optim.lr_scheduler import LRScheduler
+from tqdm import tqdm
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+import torch
+from peft import LoraConfig, get_peft_model, PeftModel
+
+from maestro.trainer.common.configuration.env import (
+    CUDA_DEVICE_ENV,
+    DEFAULT_CUDA_DEVICE,
+    HF_TOKEN_ENV,
+)
+from maestro.trainer.common.data_loaders.jsonl import JSONLDataset
+from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
+
+DEFAULT_PALIGEMMA_MODEL_ID = "google/paligemma-3b-pt-224"
+DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
+
+LoraInitLiteral = Literal["gaussian", "olora", "pissa", "pissa_niter_[number of iters]", "loftq"]
+
+
+def train(
+    model: PaliGemmaForConditionalGeneration,
+    processor: AutoProcessor,
+    train_dataset: JSONLDataset,
+    dataset_root: str,
+    batch_size: int,
+    epochs: int,
+    learning_rate: float,
+    device: torch.device = DEVICE,
+) -> MetricsTracker:
+    if device.type == "cup":
+        raise RuntimeError("PaliGemma training process requires GPU")
+    metrics_tracker = MetricsTracker.init(metrics=["loss"])
+    peft_model = prepare_peft_model(model=model).train()
+    train_steps = len(train_dataset) // batch_size
+    with torch.amp.autocast(device.type, torch.float16):
+        lora_layers = filter(lambda p: p.requires_grad, peft_model.parameters())
+        optimizer = optim.SGD(lora_layers, lr=learning_rate)
+        scheduler = optim.lr_scheduler.CosineAnnealingLR(
+            optimizer,
+            epochs * train_steps + 1,
+            eta_min=learning_rate / 10,
+        )
+        run_training_epochs(
+            peft_model=peft_model,
+            processor=processor,
+            epochs=epochs,
+            train_steps=train_steps,
+            train_dataset=train_dataset,
+            batch_size=batch_size,
+            dataset_root=dataset_root,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            metrics_tracker=metrics_tracker,
+        )
+    return metrics_tracker
+
+
+def run_training_epochs(
+    peft_model: PeftModel,
+    processor: AutoProcessor,
+    epochs: int,
+    train_steps: int,
+    train_dataset: JSONLDataset,
+    batch_size: int,
+    dataset_root: str,
+    optimizer: Optimizer,
+    scheduler: LRScheduler,
+    metrics_tracker: MetricsTracker,
+) -> None:
+    for epoch in tqdm(range(epochs), desc="EPOCHS"):
+        train_dataset.shuffle()
+        dataset_iterator = iter(train_dataset)
+        progress_bar = tqdm(range(train_steps), desc="STEPS")
+        run_training_steps(
+            peft_model=peft_model,
+            processor=processor,
+            epoch=epoch,
+            train_steps=train_steps,
+            batch_size=batch_size,
+            dataset_iterator=dataset_iterator,
+            dataset_root=dataset_root,
+            optimizer=optimizer,
+            scheduler=scheduler,
+            metrics_tracker=metrics_tracker,
+            progress_bar=progress_bar,
+        )
+
+
+def run_training_steps(
+    peft_model: PeftModel,
+    processor: AutoProcessor,
+    epoch: int,
+    train_steps: int,
+    batch_size: int,
+    dataset_iterator: Iterator[dict],
+    dataset_root: str,
+    optimizer: Optimizer,
+    scheduler: LRScheduler,
+    metrics_tracker: MetricsTracker,
+    progress_bar: tqdm[int],
+) -> None:
+    for step in range(1, train_steps + 1):
+        batch = collect_batch(
+            batch_size=batch_size,
+            dataset_iterator=dataset_iterator,
+            dataset_root=dataset_root,
+            processor=processor,
+        )
+        loss_tensor = peft_model(**batch)["loss"]
+        loss_tensor.backward()
+        loss = loss_tensor.cpu().detach().numpy()
+        optimizer.step()
+        optimizer.zero_grad()
+        scheduler.step()
+        progress_bar.update(1)
+        progress_bar.set_description(f"Loss: {loss}")
+        metrics_tracker.register(metric="loss", epoch=epoch, step=step, value=loss)
+
+
+def collect_batch(
+    batch_size: int,
+    dataset_iterator: Iterator[dict],
+    dataset_root: str,
+    processor: AutoProcessor,
+) -> torch.Tensor:
+    with torch.no_grad():
+        examples = []
+        for _ in range(batch_size):
+            examples.append(next(dataset_iterator))
+        return _collate_fn(
+            examples=examples,
+            dataset_root=dataset_root,
+            processor=processor,
+        )
+
+
+def load_model(
+    model_id: str = DEFAULT_PALIGEMMA_MODEL_ID,
+    revision: str = "float16",
+    device: torch.device = DEVICE,
+    hf_token: Optional[str] = None,
+    cache_dir: Optional[str] = None,
+) -> Tuple[AutoProcessor, PaliGemmaForConditionalGeneration]:
+    if hf_token is None:
+        hf_token = os.getenv(HF_TOKEN_ENV)
+    processor = AutoProcessor.from_pretrained(model_id, token=hf_token, cache_dir=cache_dir)
+    model = PaliGemmaForConditionalGeneration.from_pretrained(
+        model_id,
+        revision=revision,
+        device_map=device,
+        cache_dir=cache_dir,
+        token=hf_token,
+        torch_dtype=torch.float16,
+    ).eval()
+    return processor, model
+
+
+def prepare_peft_model(
+    model: PaliGemmaForConditionalGeneration,
+    r: int = 12,
+    lora_alpha: int = 12,
+    lora_dropout: float = 0.05,
+    bias: Literal["none", "all", "lora_only"] = "none",
+    inference_mode: bool = False,
+    use_rslora: bool = True,
+    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian",
+    revision: str = "float16",
+    device: torch.device = DEVICE,
+) -> PeftModel:
+    config = LoraConfig(
+        r=r,
+        lora_alpha=lora_alpha,
+        target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "linear"],
+        task_type="CAUSAL_LM",
+        lora_dropout=lora_dropout,
+        bias=bias,
+        inference_mode=inference_mode,
+        use_rslora=use_rslora,
+        init_lora_weights=init_lora_weights,
+        revision=revision,
+    )
+    return get_peft_model(model, config).to(device)
+
+
+def _collate_fn(
+    examples: List[dict],
+    dataset_root: str,
+    processor: AutoProcessor,
+    device: torch.device = DEVICE,
+    image_file_key: str = "image",
+    prefix_key: str = "prefix",
+    suffix_key: str = "suffix",
+) -> torch.Tensor:
+    images = [
+        _load_image_from_dataset(
+            image_name=example[image_file_key],
+            dataset_root=dataset_root,
+        )
+        for example in examples
+    ]
+    tokens = processor(
+        text=[example[prefix_key] for example in examples],
+        suffix=[example[suffix_key] for example in examples],
+        images=images,
+        return_tensors="pt",
+        padding="longest",
+    )
+    return tokens.to(device)
+
+
+def _load_image_from_dataset(image_name: str, dataset_root: str) -> Image.Image:
+    image_path = os.path.join(dataset_root, "dataset", image_name)
+    return Image.open(image_path).convert("RGB")

From 2d6e4cfbf57f6c237c33eb50d48600896edcbded Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Tue, 27 Aug 2024 09:01:19 +0200
Subject: [PATCH 04/69] Fix wrong typing

---
 maestro/trainer/models/paligemma/training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/models/paligemma/training.py b/maestro/trainer/models/paligemma/training.py
index 48cb2cc..695c5a9 100644
--- a/maestro/trainer/models/paligemma/training.py
+++ b/maestro/trainer/models/paligemma/training.py
@@ -104,7 +104,7 @@ def run_training_steps(
     optimizer: Optimizer,
     scheduler: LRScheduler,
     metrics_tracker: MetricsTracker,
-    progress_bar: tqdm[int],
+    progress_bar,
 ) -> None:
     for step in range(1, train_steps + 1):
         batch = collect_batch(

From aded4dde1e9816cffe25bfb6618cc94d40d3920d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Tue, 27 Aug 2024 09:43:49 +0200
Subject: [PATCH 05/69] use reproducibility utils

---
 maestro/trainer/models/paligemma/training.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/maestro/trainer/models/paligemma/training.py b/maestro/trainer/models/paligemma/training.py
index 695c5a9..cfd5f1f 100644
--- a/maestro/trainer/models/paligemma/training.py
+++ b/maestro/trainer/models/paligemma/training.py
@@ -17,6 +17,7 @@
 )
 from maestro.trainer.common.data_loaders.jsonl import JSONLDataset
 from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
+from maestro.trainer.common.utils.reproducibility import set_random_generators_seeds
 
 DEFAULT_PALIGEMMA_MODEL_ID = "google/paligemma-3b-pt-224"
 DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
@@ -34,6 +35,7 @@ def train(
     learning_rate: float,
     device: torch.device = DEVICE,
 ) -> MetricsTracker:
+    set_random_generators_seeds()
     if device.type == "cup":
         raise RuntimeError("PaliGemma training process requires GPU")
     metrics_tracker = MetricsTracker.init(metrics=["loss"])

From 263b7febcb48795b2d99df8cb9c4273ae807344d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:08:41 +0200
Subject: [PATCH 06/69] Add basic utils for florence

---
 maestro/trainer/models/florence_2/training.py | 162 ++++++++++++++++++
 requirements/requirements.txt                 |   2 +
 2 files changed, 164 insertions(+)
 create mode 100644 maestro/trainer/models/florence_2/training.py

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
new file mode 100644
index 0000000..4ab7a41
--- /dev/null
+++ b/maestro/trainer/models/florence_2/training.py
@@ -0,0 +1,162 @@
+import os
+from typing import Optional, Tuple, Literal, Union
+
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor
+import supervision as sv
+
+from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
+
+DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
+DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
+DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
+
+
+def load_model(
+    model_id: str = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: torch.device = DEVICE,
+    cache_dir: Optional[str] = None,
+) -> Tuple[AutoProcessor, AutoModelForCausalLM]:
+    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True, revision=revision)
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id, trust_remote_code=True, revision=revision, cache_dir=cache_dir
+    ).to(device)
+    return processor, model
+
+
+def caption_image(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal["<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>"],
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> str:
+    model_device = model.device
+    inputs = processor(text=task, images=image, return_tensors="pt").to(model_device)
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    response = processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
+    return response[task]
+
+
+TASKS_THAT_REQUIRE_PROMPT = {
+    "<CAPTION_TO_PHRASE_GROUNDING>",
+    "<REFERRING_EXPRESSION_SEGMENTATION>",
+    "<REGION_TO_SEGMENTATION>",
+}
+
+
+def segment_objects(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal[
+        "<REFERRING_EXPRESSION_SEGMENTATION>",
+        "<REGION_TO_SEGMENTATION>",
+    ],
+    prompt: Optional[Union[str, tuple]] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> sv.Detections:
+    return _prompt_and_retrieve_detections(
+        image=image,
+        processor=processor,
+        model=model,
+        task=task,
+        prompt=prompt,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+
+
+def detect_objects(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal[
+        "<OD>",
+        "<DENSE_REGION_CAPTION>",
+        "<REGION_PROPOSAL>",
+        "<OCR_WITH_REGION>",
+        "<OPEN_VOCABULARY_DETECTION>",
+        "<REGION_TO_CATEGORY>",
+        "<REGION_TO_DESCRIPTION>",
+    ],
+    prompt: Optional[Union[str, tuple]] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> sv.Detections:
+    return _prompt_and_retrieve_detections(
+        image=image,
+        processor=processor,
+        model=model,
+        task=task,
+        prompt=prompt,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+
+
+def _prompt_and_retrieve_detections(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal[
+        "<OD>",
+        "<DENSE_REGION_CAPTION>",
+        "<REGION_PROPOSAL>",
+        "<OCR_WITH_REGION>",
+        "<OPEN_VOCABULARY_DETECTION>",
+        "<REGION_TO_CATEGORY>",
+        "<REGION_TO_DESCRIPTION>",
+        "<REFERRING_EXPRESSION_SEGMENTATION>",
+        "<REGION_TO_SEGMENTATION>",
+    ],
+    prompt: Optional[Union[str, tuple]] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> sv.Detections:
+    if prompt is None:
+        if task in TASKS_THAT_REQUIRE_PROMPT:
+            raise ValueError(f"Task {task} requires prompt")
+        prompt = task
+    elif isinstance(prompt, tuple):
+        x_min, y_min, x_max, y_max = prompt
+        x_min, x_max = round(x_min / image.width), round(x_max / image.width)
+        y_min, y_max = round(y_min / image.height), round(y_max / image.height)
+        prompt = f"<loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
+    model_device = model.device
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    response = processor.post_process_generation(
+        generated_text,
+        task=task,
+        image_size=(image.width, image.height),
+    )
+    return sv.Detections.from_lmm(
+        lmm=sv.LMM.FLORENCE_2,
+        result=response,
+        resolution_wh=image.size,
+    )
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a302f7d..ce70d29 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,3 +5,5 @@ torch~=2.4.0
 accelerate~=0.33.0
 sentencepiece~=0.2.0
 peft~=0.12.0
+flash-attn~=2.6.3
+einops~=0.8.0

From f9abed815ed7e60688252f93ec8046b3796226e8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:24:52 +0200
Subject: [PATCH 07/69] Fix prompting

---
 maestro/trainer/models/florence_2/training.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 4ab7a41..21aea4c 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -139,7 +139,9 @@ def _prompt_and_retrieve_detections(
         x_min, y_min, x_max, y_max = prompt
         x_min, x_max = round(x_min / image.width), round(x_max / image.width)
         y_min, y_max = round(y_min / image.height), round(y_max / image.height)
-        prompt = f"<loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
+        prompt = f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
+    else:
+        prompt = f"{task} {prompt}"
     model_device = model.device
     inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
     generated_ids = model.generate(

From c65fc442d34d62bbac63d9e165faacf6f8ce127e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:37:30 +0200
Subject: [PATCH 08/69] Fix prompting

---
 maestro/trainer/models/florence_2/training.py | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 21aea4c..09154c7 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -1,6 +1,7 @@
 import os
 from typing import Optional, Tuple, Literal, Union
 
+import numpy as np
 import torch
 from PIL import Image
 from transformers import AutoModelForCausalLM, AutoProcessor
@@ -53,6 +54,8 @@ def caption_image(
     "<CAPTION_TO_PHRASE_GROUNDING>",
     "<REFERRING_EXPRESSION_SEGMENTATION>",
     "<REGION_TO_SEGMENTATION>",
+    "<REGION_TO_CATEGORY>",
+    "<REGION_TO_DESCRIPTION>",
 }
 
 
@@ -64,7 +67,7 @@ def segment_objects(
         "<REFERRING_EXPRESSION_SEGMENTATION>",
         "<REGION_TO_SEGMENTATION>",
     ],
-    prompt: Optional[Union[str, tuple]] = None,
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
     max_new_tokens: int = 1024,
     do_sample: bool = False,
     num_beams: int = 3,
@@ -94,7 +97,7 @@ def detect_objects(
         "<REGION_TO_CATEGORY>",
         "<REGION_TO_DESCRIPTION>",
     ],
-    prompt: Optional[Union[str, tuple]] = None,
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
     max_new_tokens: int = 1024,
     do_sample: bool = False,
     num_beams: int = 3,
@@ -126,7 +129,7 @@ def _prompt_and_retrieve_detections(
         "<REFERRING_EXPRESSION_SEGMENTATION>",
         "<REGION_TO_SEGMENTATION>",
     ],
-    prompt: Optional[Union[str, tuple]] = None,
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
     max_new_tokens: int = 1024,
     do_sample: bool = False,
     num_beams: int = 3,
@@ -135,11 +138,12 @@ def _prompt_and_retrieve_detections(
         if task in TASKS_THAT_REQUIRE_PROMPT:
             raise ValueError(f"Task {task} requires prompt")
         prompt = task
-    elif isinstance(prompt, tuple):
+    elif isinstance(prompt, tuple) or isinstance(prompt, list) or isinstance(prompt, np.ndarray):
+        if len(prompt) != 4:
+            raise ValueError("Expected sequence of 4 elements describing (x_min, y_min, x_max, y_max)")
         x_min, y_min, x_max, y_max = prompt
-        x_min, x_max = round(x_min / image.width), round(x_max / image.width)
-        y_min, y_max = round(y_min / image.height), round(y_max / image.height)
-        prompt = f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
+        x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
+        y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
     else:
         prompt = f"{task} {prompt}"
     model_device = model.device

From 0c83e90c150d543db2e355b9d4e82c2a5f9d086f Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:39:10 +0200
Subject: [PATCH 09/69] Fix prompting

---
 maestro/trainer/models/florence_2/training.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 09154c7..2204259 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -144,6 +144,7 @@ def _prompt_and_retrieve_detections(
         x_min, y_min, x_max, y_max = prompt
         x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
         y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
+        prompt = f"<loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
     else:
         prompt = f"{task} {prompt}"
     model_device = model.device

From 9fbfc10fa28b7d403c7cfc00f712be419de3977c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:41:31 +0200
Subject: [PATCH 10/69] Fix prompting

---
 maestro/trainer/models/florence_2/training.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 2204259..c27d212 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -162,6 +162,7 @@ def _prompt_and_retrieve_detections(
         task=task,
         image_size=(image.width, image.height),
     )
+    print("DEBUG:", prompt, response)
     return sv.Detections.from_lmm(
         lmm=sv.LMM.FLORENCE_2,
         result=response,

From 453d4d12f5ef6f4c84fedddd6dfa87daee9fa26d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:42:23 +0200
Subject: [PATCH 11/69] Fix prompting

---
 maestro/trainer/models/florence_2/training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index c27d212..815ba6e 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -144,7 +144,7 @@ def _prompt_and_retrieve_detections(
         x_min, y_min, x_max, y_max = prompt
         x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
         y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
-        prompt = f"<loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
+        prompt = f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
     else:
         prompt = f"{task} {prompt}"
     model_device = model.device

From 45a04da7aa5a662bcdb8cdfcc97a4a795b64db66 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:51:05 +0200
Subject: [PATCH 12/69] Fix prompting

---
 maestro/trainer/models/florence_2/training.py | 39 ++++++++++++-------
 1 file changed, 24 insertions(+), 15 deletions(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 815ba6e..fc2b480 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -31,11 +31,13 @@ def caption_image(
     image: Image.Image,
     processor: AutoProcessor,
     model: AutoModelForCausalLM,
-    task: Literal["<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>"],
+    task: Literal["<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>", "<VQA>"],
+    prompt: Optional[str] = None,
     max_new_tokens: int = 1024,
     do_sample: bool = False,
     num_beams: int = 3,
 ) -> str:
+    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
     model_device = model.device
     inputs = processor(text=task, images=image, return_tensors="pt").to(model_device)
     generated_ids = model.generate(
@@ -56,6 +58,7 @@ def caption_image(
     "<REGION_TO_SEGMENTATION>",
     "<REGION_TO_CATEGORY>",
     "<REGION_TO_DESCRIPTION>",
+    "<VQA>",
 }
 
 
@@ -134,19 +137,7 @@ def _prompt_and_retrieve_detections(
     do_sample: bool = False,
     num_beams: int = 3,
 ) -> sv.Detections:
-    if prompt is None:
-        if task in TASKS_THAT_REQUIRE_PROMPT:
-            raise ValueError(f"Task {task} requires prompt")
-        prompt = task
-    elif isinstance(prompt, tuple) or isinstance(prompt, list) or isinstance(prompt, np.ndarray):
-        if len(prompt) != 4:
-            raise ValueError("Expected sequence of 4 elements describing (x_min, y_min, x_max, y_max)")
-        x_min, y_min, x_max, y_max = prompt
-        x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
-        y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
-        prompt = f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
-    else:
-        prompt = f"{task} {prompt}"
+    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
     model_device = model.device
     inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
     generated_ids = model.generate(
@@ -162,9 +153,27 @@ def _prompt_and_retrieve_detections(
         task=task,
         image_size=(image.width, image.height),
     )
-    print("DEBUG:", prompt, response)
     return sv.Detections.from_lmm(
         lmm=sv.LMM.FLORENCE_2,
         result=response,
         resolution_wh=image.size,
     )
+
+
+def _pre_process_prompt(
+    image: Image.Image,
+    task: str,
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
+) -> str:
+    if prompt is None:
+        if task in TASKS_THAT_REQUIRE_PROMPT:
+            raise ValueError(f"Task {task} requires prompt")
+        return task
+    if isinstance(prompt, tuple) or isinstance(prompt, list) or isinstance(prompt, np.ndarray):
+        if len(prompt) != 4:
+            raise ValueError("Expected sequence of 4 elements describing (x_min, y_min, x_max, y_max)")
+        x_min, y_min, x_max, y_max = prompt
+        x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
+        y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
+        return f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
+    return f"{task} {prompt}"

From 13d9d36c8490bc58b6cc25148dc639c36bb4b476 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 11:53:21 +0200
Subject: [PATCH 13/69] Fix prompting

---
 maestro/trainer/models/florence_2/training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index fc2b480..79f56bd 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -39,7 +39,7 @@ def caption_image(
 ) -> str:
     prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
     model_device = model.device
-    inputs = processor(text=task, images=image, return_tensors="pt").to(model_device)
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
     generated_ids = model.generate(
         input_ids=inputs["input_ids"],
         pixel_values=inputs["pixel_values"],

From e4e9fee026c2d40d9e9dfb7fa6fde5c589d07016 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 13:40:18 +0200
Subject: [PATCH 14/69] Scratch of training loop

---
 .../trainer/common/data_loaders/datasets.py   |  50 ++
 maestro/trainer/common/utils/leaderboard.py   |  41 ++
 .../trainer/common/utils/reproducibility.py   |   2 +-
 .../trainer/models/florence_2/inference.py    | 159 ++++++
 maestro/trainer/models/florence_2/training.py | 504 +++++++++++++-----
 maestro/trainer/models/paligemma/training.py  |   4 +-
 6 files changed, 613 insertions(+), 147 deletions(-)
 create mode 100644 maestro/trainer/common/data_loaders/datasets.py
 create mode 100644 maestro/trainer/common/utils/leaderboard.py
 create mode 100644 maestro/trainer/models/florence_2/inference.py

diff --git a/maestro/trainer/common/data_loaders/datasets.py b/maestro/trainer/common/data_loaders/datasets.py
new file mode 100644
index 0000000..b79cfec
--- /dev/null
+++ b/maestro/trainer/common/data_loaders/datasets.py
@@ -0,0 +1,50 @@
+import json
+import os
+from typing import List, Dict, Any, Tuple
+
+from PIL import Image
+from transformers.pipelines.base import Dataset
+
+
+class JSONLDataset:
+    def __init__(self, jsonl_file_path: str, image_directory_path: str):
+        self.jsonl_file_path = jsonl_file_path
+        self.image_directory_path = image_directory_path
+        self.entries = self._load_entries()
+
+    def _load_entries(self) -> List[Dict[str, Any]]:
+        entries = []
+        with open(self.jsonl_file_path, "r") as file:
+            for line in file:
+                data = json.loads(line)
+                entries.append(data)
+        return entries
+
+    def __len__(self) -> int:
+        return len(self.entries)
+
+    def __getitem__(self, idx: int) -> Tuple[Image.Image, Dict[str, Any]]:
+        if idx < 0 or idx >= len(self.entries):
+            raise IndexError("Index out of range")
+
+        entry = self.entries[idx]
+        image_path = os.path.join(self.image_directory_path, entry["image"])
+        try:
+            image = Image.open(image_path)
+            return (image, entry)
+        except FileNotFoundError:
+            raise FileNotFoundError(f"Image file {image_path} not found.")
+
+
+class DetectionDataset(Dataset):
+    def __init__(self, jsonl_file_path: str, image_directory_path: str):
+        self.dataset = JSONLDataset(jsonl_file_path, image_directory_path)
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, idx):
+        image, data = self.dataset[idx]
+        prefix = data["prefix"]
+        suffix = data["suffix"]
+        return prefix, suffix, image
diff --git a/maestro/trainer/common/utils/leaderboard.py b/maestro/trainer/common/utils/leaderboard.py
new file mode 100644
index 0000000..b5b0a61
--- /dev/null
+++ b/maestro/trainer/common/utils/leaderboard.py
@@ -0,0 +1,41 @@
+from typing import Dict, Tuple, Optional
+
+
+class CheckpointsLeaderboard:
+
+    def __init__(
+        self,
+        max_checkpoints: int,
+    ):
+        self._max_checkpoints = max(max_checkpoints, 1)
+        self._leaderboard: Dict[int, Tuple[str, float]] = {}
+
+    def register_checkpoint(self, epoch: int, path: str, loss: float) -> Tuple[bool, Optional[str]]:
+        if len(self._leaderboard) < self._max_checkpoints:
+            self._leaderboard[epoch] = (path, loss)
+            return True, None
+        max_loss_key, max_loss_in_leaderboard = None, None
+        for key, (_, loss) in self._leaderboard.items():
+            if max_loss_in_leaderboard is None:
+                max_loss_key = key
+                max_loss_in_leaderboard = loss
+            if loss > max_loss_in_leaderboard:  # type: ignore
+                max_loss_key = key
+                max_loss_in_leaderboard = loss
+        if loss >= max_loss_in_leaderboard:  # type: ignore
+            return False, None
+        to_be_removed, _ = self._leaderboard.pop(max_loss_key)  # type: ignore
+        return True, to_be_removed
+
+    def get_best_model(self) -> str:
+        min_loss_key, min_loss_in_leaderboard = None, None
+        for key, (_, loss) in self._leaderboard.items():
+            if min_loss_in_leaderboard is None:
+                min_loss_key = key
+                min_loss_in_leaderboard = loss
+            if loss < min_loss_in_leaderboard:  # type: ignore
+                min_loss_key = key
+                min_loss_in_leaderboard = loss
+        if min_loss_key is None:
+            raise RuntimeError("Could not retrieve best model")
+        return min_loss_key  # type: ignore
diff --git a/maestro/trainer/common/utils/reproducibility.py b/maestro/trainer/common/utils/reproducibility.py
index f55d646..69e6f1f 100644
--- a/maestro/trainer/common/utils/reproducibility.py
+++ b/maestro/trainer/common/utils/reproducibility.py
@@ -8,7 +8,7 @@
 from maestro.trainer.common.configuration.env import SEED_ENV, DEFAULT_SEED
 
 
-def set_random_generators_seeds(
+def make_it_reproducible(
     seed: Optional[int] = None,
     disable_cudnn_benchmark: bool = True,
     avoid_non_deterministic_algorithms: bool = True,
diff --git a/maestro/trainer/models/florence_2/inference.py b/maestro/trainer/models/florence_2/inference.py
new file mode 100644
index 0000000..4f0c154
--- /dev/null
+++ b/maestro/trainer/models/florence_2/inference.py
@@ -0,0 +1,159 @@
+from typing import Literal, Optional, Union
+
+import numpy as np
+import supervision as sv
+
+from PIL import Image
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+
+def caption_image(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal["<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>", "<VQA>"],
+    prompt: Optional[str] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> str:
+    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
+    model_device = model.device
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    response = processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
+    return response[task]
+
+
+TASKS_THAT_REQUIRE_PROMPT = {
+    "<CAPTION_TO_PHRASE_GROUNDING>",
+    "<REFERRING_EXPRESSION_SEGMENTATION>",
+    "<REGION_TO_SEGMENTATION>",
+    "<REGION_TO_CATEGORY>",
+    "<REGION_TO_DESCRIPTION>",
+    "<VQA>",
+}
+
+
+def segment_objects(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal[
+        "<REFERRING_EXPRESSION_SEGMENTATION>",
+        "<REGION_TO_SEGMENTATION>",
+    ],
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> sv.Detections:
+    return _prompt_and_retrieve_detections(
+        image=image,
+        processor=processor,
+        model=model,
+        task=task,
+        prompt=prompt,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+
+
+def detect_objects(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal[
+        "<OD>",
+        "<DENSE_REGION_CAPTION>",
+        "<REGION_PROPOSAL>",
+        "<OCR_WITH_REGION>",
+        "<OPEN_VOCABULARY_DETECTION>",
+        "<REGION_TO_CATEGORY>",
+        "<REGION_TO_DESCRIPTION>",
+    ],
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> sv.Detections:
+    return _prompt_and_retrieve_detections(
+        image=image,
+        processor=processor,
+        model=model,
+        task=task,
+        prompt=prompt,
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+
+
+def _prompt_and_retrieve_detections(
+    image: Image.Image,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    task: Literal[
+        "<OD>",
+        "<DENSE_REGION_CAPTION>",
+        "<REGION_PROPOSAL>",
+        "<OCR_WITH_REGION>",
+        "<OPEN_VOCABULARY_DETECTION>",
+        "<REGION_TO_CATEGORY>",
+        "<REGION_TO_DESCRIPTION>",
+        "<REFERRING_EXPRESSION_SEGMENTATION>",
+        "<REGION_TO_SEGMENTATION>",
+    ],
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
+    max_new_tokens: int = 1024,
+    do_sample: bool = False,
+    num_beams: int = 3,
+) -> sv.Detections:
+    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
+    model_device = model.device
+    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=max_new_tokens,
+        do_sample=do_sample,
+        num_beams=num_beams,
+    )
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    response = processor.post_process_generation(
+        generated_text,
+        task=task,
+        image_size=(image.width, image.height),
+    )
+    return sv.Detections.from_lmm(
+        lmm=sv.LMM.FLORENCE_2,
+        result=response,
+        resolution_wh=image.size,
+    )
+
+
+def _pre_process_prompt(
+    image: Image.Image,
+    task: str,
+    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
+) -> str:
+    if prompt is None:
+        if task in TASKS_THAT_REQUIRE_PROMPT:
+            raise ValueError(f"Task {task} requires prompt")
+        return task
+    if isinstance(prompt, tuple) or isinstance(prompt, list) or isinstance(prompt, np.ndarray):
+        if len(prompt) != 4:
+            raise ValueError("Expected sequence of 4 elements describing (x_min, y_min, x_max, y_max)")
+        x_min, y_min, x_max, y_max = prompt
+        x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
+        y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
+        return f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
+    return f"{task} {prompt}"
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 79f56bd..85de6d3 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -1,179 +1,395 @@
+import logging
 import os
-from typing import Optional, Tuple, Literal, Union
+import shutil
+from dataclasses import dataclass, replace
+from functools import partial
+from glob import glob
+from typing import Optional, Tuple, List, Literal, Union
 
-import numpy as np
-import torch
 from PIL import Image
-from transformers import AutoModelForCausalLM, AutoProcessor
-import supervision as sv
+import torch
+from peft import PeftModel, LoraConfig, get_peft_model
+from torch.optim import Optimizer, AdamW, Adam, SGD
+from torch.optim.lr_scheduler import LRScheduler
+from torch.utils.data import DataLoader
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
 from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
+from maestro.trainer.common.data_loaders.datasets import DetectionDataset
+from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
+from maestro.trainer.common.utils.reproducibility import make_it_reproducible
+from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
 DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
 DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
 DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
 
 
+@dataclass(frozen=True)
+class TrainingConfiguration:
+    dataset_location: str
+    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION
+    device: torch.device = DEVICE
+    transformers_cache_dir: Optional[str] = None
+    training_epochs: int = 10
+    optimiser: Literal["SGD", "adamw", "adam"] = "adamw"
+    learning_rate: float = 1e-5
+    lr_scheduler: Literal["linear", "cosine", "polynomial"] = "linear"
+    train_batch_size: int = 4
+    test_batch_size: Optional[int] = None
+    loaders_workers: int = 0
+    test_loaders_workers: Optional[int] = None
+    lora_r: int = 8
+    lora_alpha: int = 8
+    lora_dropout: float = 0.05
+    bias: Literal["none", "all", "lora_only"] = "none"
+    use_rslora: bool = True
+    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
+    training_dir = "./training/florence-2"
+    max_checkpoints_to_keep: int = 3
+
+
+def train(configuration: TrainingConfiguration) -> None:
+    make_it_reproducible()
+    training_run_dir = _establish_training_run_dir(
+        training_dir=configuration.training_dir,
+    )
+    configuration = replace(
+        configuration,
+        training_dir=training_run_dir,  # type: ignore
+    )
+    checkpoints_leaderboard = CheckpointsLeaderboard(
+        max_checkpoints=configuration.max_checkpoints_to_keep,
+    )
+    processor, model = load_model(
+        model_id_or_path=configuration.model_id_or_path,
+        revision=configuration.revision,
+        device=configuration.device,
+        cache_dir=configuration.transformers_cache_dir,
+    )
+    train_loader, val_loader, test_loader = prepare_data_loaders(
+        dataset_location=configuration.dataset_location,
+        train_batch_size=configuration.train_batch_size,
+        processor=processor,
+        device=configuration.device,
+        num_workers=configuration.loaders_workers,
+        test_loaders_workers=configuration.test_loaders_workers,
+    )
+    if test_loader is None:
+        test_loader = val_loader
+    peft_model = prepare_peft_model(
+        model=model,
+        r=configuration.lora_r,
+        lora_alpha=configuration.lora_alpha,
+        lora_dropout=configuration.lora_dropout,
+        bias=configuration.bias,
+        use_rslora=configuration.use_rslora,
+        init_lora_weights=configuration.init_lora_weights,
+        revision=configuration.revision,
+    )
+    run_training_loop(
+        processor=processor,
+        model=peft_model,
+        data_loaders=(train_loader, val_loader),
+        configuration=configuration,
+        checkpoints_leaderboard=checkpoints_leaderboard,
+    )
+    best_model_path = checkpoints_leaderboard.get_best_model()
+    print(f"Loading best model from {best_model_path}")
+    processor, model = load_model(
+        model_id_or_path=best_model_path,
+    )
+    if test_loader is not None:
+        run_validation_epoch(
+            processor=processor,
+            model=model,
+            loader=test_loader,
+            epoch_number=None,
+            configuration=configuration,
+            title="Test",
+        )
+    best_model_dir = os.path.join(configuration.training_dir, "best_model")
+    print(f"Saving best model: {best_model_dir}")
+    model.save_pretrained(best_model_dir)
+    processor.save_pretrained(best_model_dir)
+
+
 def load_model(
-    model_id: str = DEFAULT_FLORENCE2_MODEL_ID,
+    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID,
     revision: str = DEFAULT_FLORENCE2_MODEL_REVISION,
     device: torch.device = DEVICE,
     cache_dir: Optional[str] = None,
 ) -> Tuple[AutoProcessor, AutoModelForCausalLM]:
-    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True, revision=revision)
+    processor = AutoProcessor.from_pretrained(
+        model_id_or_path,
+        trust_remote_code=True,
+        revision=revision,
+    )
     model = AutoModelForCausalLM.from_pretrained(
-        model_id, trust_remote_code=True, revision=revision, cache_dir=cache_dir
+        model_id_or_path,
+        trust_remote_code=True,
+        revision=revision,
+        cache_dir=cache_dir,
     ).to(device)
     return processor, model
 
 
-def caption_image(
-    image: Image.Image,
-    processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-    task: Literal["<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>", "<VQA>"],
-    prompt: Optional[str] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> str:
-    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
-    model_device = model.device
-    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
-    generated_ids = model.generate(
-        input_ids=inputs["input_ids"],
-        pixel_values=inputs["pixel_values"],
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
-    )
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    response = processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
-    return response[task]
-
-
-TASKS_THAT_REQUIRE_PROMPT = {
-    "<CAPTION_TO_PHRASE_GROUNDING>",
-    "<REFERRING_EXPRESSION_SEGMENTATION>",
-    "<REGION_TO_SEGMENTATION>",
-    "<REGION_TO_CATEGORY>",
-    "<REGION_TO_DESCRIPTION>",
-    "<VQA>",
-}
-
-
-def segment_objects(
-    image: Image.Image,
+def prepare_data_loaders(
+    dataset_location: str,
+    train_batch_size: int,
     processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-    task: Literal[
-        "<REFERRING_EXPRESSION_SEGMENTATION>",
-        "<REGION_TO_SEGMENTATION>",
-    ],
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> sv.Detections:
-    return _prompt_and_retrieve_detections(
-        image=image,
+    device: torch.device,
+    num_workers: int = 0,
+    test_batch_size: Optional[int] = None,
+    test_loaders_workers: Optional[int] = None,
+) -> Tuple[
+    DataLoader,
+    Optional[DataLoader],
+    Optional[DataLoader],
+]:
+    test_batch_size = test_batch_size or train_batch_size
+    test_loaders_workers = test_loaders_workers or num_workers
+    train_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="train",
+        batch_size=train_batch_size,
         processor=processor,
-        model=model,
-        task=task,
-        prompt=prompt,
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
+        device=device,
+        num_workers=num_workers,
+        shuffle=True,
+    )
+    if train_data_loader is None:
+        raise RuntimeError("Could not initialise train data loader")
+    valid_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="valid",
+        batch_size=test_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=test_loaders_workers,
+        shuffle=False,
+    )
+    test_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="test",
+        batch_size=test_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=test_loaders_workers,
+        shuffle=False,
     )
+    return train_data_loader, valid_data_loader, test_data_loader
 
 
-def detect_objects(
-    image: Image.Image,
+def prepare_detection_data_loader(
+    dataset_location: str,
+    split_name: str,
+    batch_size: int,
     processor: AutoProcessor,
+    device: torch.device,
+    num_workers: int = 0,
+    shuffle: bool = True,
+) -> Optional[DataLoader]:
+    image_directory_path = os.path.join(dataset_location, split_name)
+    jsonl_file_path = os.path.join(dataset_location, split_name, "annotations.jsonl")
+    if os.path.exists(image_directory_path):
+        logging.warning(f"Could not data directory: {image_directory_path}")
+        return None
+    if not os.path.exists(jsonl_file_path):
+        logging.warning(f"Could not find JSONL file: {jsonl_file_path}")
+        return None
+    dataset = DetectionDataset(
+        jsonl_file_path=jsonl_file_path,
+        image_directory_path=image_directory_path,
+    )
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        collate_fn=partial(_collate_fn, processor=processor, device=device),
+        num_workers=num_workers,
+        shuffle=shuffle,
+    )
+
+
+def prepare_peft_model(
     model: AutoModelForCausalLM,
-    task: Literal[
-        "<OD>",
-        "<DENSE_REGION_CAPTION>",
-        "<REGION_PROPOSAL>",
-        "<OCR_WITH_REGION>",
-        "<OPEN_VOCABULARY_DETECTION>",
-        "<REGION_TO_CATEGORY>",
-        "<REGION_TO_DESCRIPTION>",
-    ],
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> sv.Detections:
-    return _prompt_and_retrieve_detections(
-        image=image,
+    r: int = 8,
+    lora_alpha: int = 8,
+    lora_dropout: float = 0.05,
+    bias: Literal["none", "all", "lora_only"] = "none",
+    inference_mode: bool = False,
+    use_rslora: bool = True,
+    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian",
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION,
+) -> PeftModel:
+    config = LoraConfig(
+        r=r,
+        lora_alpha=lora_alpha,
+        target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "linear", "Conv2d", "lm_head", "fc2"],
+        task_type="CAUSAL_LM",
+        lora_dropout=lora_dropout,
+        bias=bias,
+        inference_mode=inference_mode,
+        use_rslora=use_rslora,
+        init_lora_weights=init_lora_weights,
+        revision=revision,
+    )
+    peft_model = get_peft_model(model, config)
+    peft_model.print_trainable_parameters()
+    return peft_model.to(model.device)
+
+
+def run_training_loop(
+    processor: AutoProcessor,
+    model: PeftModel,
+    data_loaders: Tuple[DataLoader, Optional[DataLoader]],
+    configuration: TrainingConfiguration,
+    checkpoints_leaderboard: CheckpointsLeaderboard,
+) -> None:
+    train_loader, val_loader = data_loaders
+    optimizer = _get_optimizer(model=model, configuration=configuration)
+    total_num_training_steps = configuration.training_epochs * len(train_loader)
+    lr_scheduler = get_scheduler(
+        name=configuration.lr_scheduler,
+        optimizer=optimizer,
+        num_warmup_steps=0,
+        num_training_steps=total_num_training_steps,
+    )
+    for epoch in range(configuration.training_epochs):
+        run_training_epoch(
+            processor=processor,
+            model=model,
+            train_loader=train_loader,
+            val_loader=val_loader,
+            epoch_number=epoch,
+            configuration=configuration,
+            optimizer=optimizer,
+            lr_scheduler=lr_scheduler,
+            checkpoints_leaderboard=checkpoints_leaderboard,
+        )
+
+
+def run_training_epoch(
+    processor: AutoProcessor,
+    model: PeftModel,
+    train_loader: DataLoader,
+    val_loader: Optional[DataLoader],
+    epoch_number: int,
+    configuration: TrainingConfiguration,
+    optimizer: Optimizer,
+    lr_scheduler: LRScheduler,
+    checkpoints_leaderboard: CheckpointsLeaderboard,
+) -> None:
+    model.train()
+    training_losses: List[float] = []
+    last_100_losses = training_losses[-100:]
+    loss_moving_average = sum(last_100_losses) / len(last_100_losses) if len(last_100_losses) > 0 else 0.0
+    for inputs, answers in tqdm(
+        train_loader,
+        desc=f"Epoch {epoch_number + 1}/{configuration.training_epochs}. " f"Loss: {round(loss_moving_average, 4)}",
+    ):
+        input_ids = inputs["input_ids"]
+        pixel_values = inputs["pixel_values"]
+        labels = processor.tokenizer(
+            text=answers, return_tensors="pt", padding=True, return_token_type_ids=False
+        ).input_ids.to(configuration.device)
+        outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)
+        loss = outputs.loss
+        loss.backward()
+        optimizer.step()
+        lr_scheduler.step()
+        optimizer.zero_grad()
+        training_losses.append(loss.item())
+    if len(training_losses) > 0:
+        avg_train_loss = sum(training_losses) / len(training_losses)
+        print(f"Average Training Loss: {avg_train_loss}")
+    if val_loader is None or len(val_loader) == 0:
+        return None
+    validation_loss = run_validation_epoch(
         processor=processor,
         model=model,
-        task=task,
-        prompt=prompt,
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
+        loader=val_loader,
+        epoch_number=epoch_number,
+        configuration=configuration,
+    )
+    checkpoint_dir = os.path.join(configuration.training_dir, "checkpoints", str(epoch_number))
+    should_save, to_remove = checkpoints_leaderboard.register_checkpoint(
+        epoch=epoch_number,
+        path=checkpoint_dir,
+        loss=validation_loss,
     )
+    if should_save:
+        print(f"Saving checkpoint under {checkpoint_dir}")
+        os.makedirs(checkpoint_dir, exist_ok=True)
+        model.save_pretrained(checkpoint_dir)
+        processor.save_pretrained(checkpoint_dir)
+    if to_remove is not None:
+        print(f"Removing checkpoint {to_remove}")
+        shutil.rmtree(to_remove, ignore_errors=True)
+
+
+def run_validation_epoch(
+    processor: AutoProcessor,
+    model: Union[PeftModel, AutoModelForCausalLM],
+    loader: DataLoader,
+    epoch_number: Optional[int],
+    configuration: TrainingConfiguration,
+    title: str = "Validation",
+) -> float:
+    val_loss = 0.0
+    epoch_marker = ""
+    if epoch_number is not None:
+        epoch_marker = f"Epoch {epoch_number + 1}/{configuration.training_epochs}"
+    with torch.no_grad():
+        for inputs, answers in tqdm(loader, desc=f"{title} | {epoch_marker}"):
+            input_ids = inputs["input_ids"]
+            pixel_values = inputs["pixel_values"]
+            labels = processor.tokenizer(
+                text=answers, return_tensors="pt", padding=True, return_token_type_ids=False
+            ).input_ids.to(configuration.device)
+            outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)
+            loss = outputs.loss
+            val_loss += loss.item()
+        avg_val_loss = val_loss / len(loader)
+        print(f"Average {title} Loss: {avg_val_loss}")
+        return avg_val_loss
 
 
-def _prompt_and_retrieve_detections(
-    image: Image.Image,
+def save_model(
+    target_dir: str,
     processor: AutoProcessor,
     model: AutoModelForCausalLM,
-    task: Literal[
-        "<OD>",
-        "<DENSE_REGION_CAPTION>",
-        "<REGION_PROPOSAL>",
-        "<OCR_WITH_REGION>",
-        "<OPEN_VOCABULARY_DETECTION>",
-        "<REGION_TO_CATEGORY>",
-        "<REGION_TO_DESCRIPTION>",
-        "<REFERRING_EXPRESSION_SEGMENTATION>",
-        "<REGION_TO_SEGMENTATION>",
-    ],
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> sv.Detections:
-    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
-    model_device = model.device
-    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
-    generated_ids = model.generate(
-        input_ids=inputs["input_ids"],
-        pixel_values=inputs["pixel_values"],
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
-    )
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    response = processor.post_process_generation(
-        generated_text,
-        task=task,
-        image_size=(image.width, image.height),
-    )
-    return sv.Detections.from_lmm(
-        lmm=sv.LMM.FLORENCE_2,
-        result=response,
-        resolution_wh=image.size,
-    )
-
-
-def _pre_process_prompt(
-    image: Image.Image,
-    task: str,
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-) -> str:
-    if prompt is None:
-        if task in TASKS_THAT_REQUIRE_PROMPT:
-            raise ValueError(f"Task {task} requires prompt")
-        return task
-    if isinstance(prompt, tuple) or isinstance(prompt, list) or isinstance(prompt, np.ndarray):
-        if len(prompt) != 4:
-            raise ValueError("Expected sequence of 4 elements describing (x_min, y_min, x_max, y_max)")
-        x_min, y_min, x_max, y_max = prompt
-        x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
-        y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
-        return f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
-    return f"{task} {prompt}"
+) -> None:
+    os.makedirs(target_dir, exist_ok=True)
+    processor.save_pretrained(target_dir)
+    model.save_pretrained(target_dir)
+
+
+def _establish_training_run_dir(training_dir: str) -> str:
+    training_dir = os.path.abspath(training_dir)
+    existing_directory_entries = glob(os.path.join(training_dir, "*"))
+    subdirectories = [path for path in existing_directory_entries if os.path.isdir(path)]
+    run_id = len(subdirectories) + 1
+    training_run_dir = os.path.join(training_dir, str(run_id))
+    os.makedirs(training_run_dir, exist_ok=True)
+    return training_run_dir
+
+
+def _get_optimizer(model: PeftModel, configuration: TrainingConfiguration) -> Optimizer:
+    if configuration.optimiser == "adamw":
+        return AdamW(model.parameters(), lr=configuration.learning_rate)
+    if configuration.optimiser == "adam":
+        return Adam(model.parameters(), lr=configuration.learning_rate)
+    return SGD(model.parameters(), lr=configuration.learning_rate)
+
+
+def _collate_fn(
+    batch: Tuple[List[str], List[str], List[Image.Image]],
+    processor: AutoProcessor,
+    device: torch.device,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    questions, answers, images = zip(*batch)
+    inputs = processor(text=list(questions), images=list(images), return_tensors="pt", padding=True).to(device)
+    return inputs, answers
diff --git a/maestro/trainer/models/paligemma/training.py b/maestro/trainer/models/paligemma/training.py
index cfd5f1f..f0e9737 100644
--- a/maestro/trainer/models/paligemma/training.py
+++ b/maestro/trainer/models/paligemma/training.py
@@ -17,7 +17,7 @@
 )
 from maestro.trainer.common.data_loaders.jsonl import JSONLDataset
 from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
-from maestro.trainer.common.utils.reproducibility import set_random_generators_seeds
+from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 
 DEFAULT_PALIGEMMA_MODEL_ID = "google/paligemma-3b-pt-224"
 DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
@@ -35,7 +35,7 @@ def train(
     learning_rate: float,
     device: torch.device = DEVICE,
 ) -> MetricsTracker:
-    set_random_generators_seeds()
+    make_it_reproducible()
     if device.type == "cup":
         raise RuntimeError("PaliGemma training process requires GPU")
     metrics_tracker = MetricsTracker.init(metrics=["loss"])

From 785014c9cc8078103c7831815ede6db7e0b2b069 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 13:48:18 +0200
Subject: [PATCH 15/69] Fix bug

---
 maestro/trainer/models/florence_2/training.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 85de6d3..9a095f1 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -47,7 +47,7 @@ class TrainingConfiguration:
     bias: Literal["none", "all", "lora_only"] = "none"
     use_rslora: bool = True
     init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
-    training_dir = "./training/florence-2"
+    training_dir: str = "./training/florence-2"
     max_checkpoints_to_keep: int = 3
 
 
@@ -58,7 +58,7 @@ def train(configuration: TrainingConfiguration) -> None:
     )
     configuration = replace(
         configuration,
-        training_dir=training_run_dir,  # type: ignore
+        training_dir=training_run_dir,
     )
     checkpoints_leaderboard = CheckpointsLeaderboard(
         max_checkpoints=configuration.max_checkpoints_to_keep,

From bf2b3e0156338174eb90f6bbe7edeb2548181fb4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 13:51:21 +0200
Subject: [PATCH 16/69] Fix bug

---
 maestro/trainer/models/florence_2/training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 9a095f1..f4a9f47 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -194,7 +194,7 @@ def prepare_detection_data_loader(
 ) -> Optional[DataLoader]:
     image_directory_path = os.path.join(dataset_location, split_name)
     jsonl_file_path = os.path.join(dataset_location, split_name, "annotations.jsonl")
-    if os.path.exists(image_directory_path):
+    if not os.path.exists(image_directory_path):
         logging.warning(f"Could not data directory: {image_directory_path}")
         return None
     if not os.path.exists(jsonl_file_path):

From d21333e384909926935fd3833e03e26f8a3effa3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 13:52:45 +0200
Subject: [PATCH 17/69] Fix bug

---
 maestro/trainer/models/florence_2/training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index f4a9f47..07295d8 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -52,7 +52,7 @@ class TrainingConfiguration:
 
 
 def train(configuration: TrainingConfiguration) -> None:
-    make_it_reproducible()
+    make_it_reproducible(avoid_non_deterministic_algorithms=False)
     training_run_dir = _establish_training_run_dir(
         training_dir=configuration.training_dir,
     )

From b870667336b021a9ae3a6fb7abbff5abfba727c1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 13:57:12 +0200
Subject: [PATCH 18/69] Fix loss display

---
 maestro/trainer/models/florence_2/training.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 07295d8..66fbda2 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -284,12 +284,8 @@ def run_training_epoch(
 ) -> None:
     model.train()
     training_losses: List[float] = []
-    last_100_losses = training_losses[-100:]
-    loss_moving_average = sum(last_100_losses) / len(last_100_losses) if len(last_100_losses) > 0 else 0.0
-    for inputs, answers in tqdm(
-        train_loader,
-        desc=f"Epoch {epoch_number + 1}/{configuration.training_epochs}. " f"Loss: {round(loss_moving_average, 4)}",
-    ):
+    training_iterator = tqdm(train_loader, desc=f"Epoch {epoch_number + 1}/{configuration.training_epochs}")
+    for inputs, answers in training_iterator:
         input_ids = inputs["input_ids"]
         pixel_values = inputs["pixel_values"]
         labels = processor.tokenizer(
@@ -302,6 +298,11 @@ def run_training_epoch(
         lr_scheduler.step()
         optimizer.zero_grad()
         training_losses.append(loss.item())
+        last_100_losses = training_losses[-100:]
+        loss_moving_average = sum(last_100_losses) / len(last_100_losses) if len(last_100_losses) > 0 else 0.0
+        training_iterator.set_description(
+            f"Epoch {epoch_number + 1}/{configuration.training_epochs}. Loss: {round(loss_moving_average, 4)}"
+        )
     if len(training_losses) > 0:
         avg_train_loss = sum(training_losses) / len(training_losses)
         print(f"Average Training Loss: {avg_train_loss}")

From d4bb6f9701f6febe564d39d2ff2db4375086c249 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 14:06:29 +0200
Subject: [PATCH 19/69] Fix bug with model loading

---
 maestro/trainer/common/utils/leaderboard.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/common/utils/leaderboard.py b/maestro/trainer/common/utils/leaderboard.py
index b5b0a61..7d147fa 100644
--- a/maestro/trainer/common/utils/leaderboard.py
+++ b/maestro/trainer/common/utils/leaderboard.py
@@ -38,4 +38,4 @@ def get_best_model(self) -> str:
                 min_loss_in_leaderboard = loss
         if min_loss_key is None:
             raise RuntimeError("Could not retrieve best model")
-        return min_loss_key  # type: ignore
+        return self._leaderboard[min_loss_key][0]

From 4a24e9719ed6664ecbf48e2a40819c0556678028 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 15:02:26 +0200
Subject: [PATCH 20/69] Add training summary

---
 maestro/trainer/common/utils/file_system.py   |  12 ++
 .../trainer/models/florence_2/data_loading.py | 110 +++++++++++
 maestro/trainer/models/florence_2/entities.py |   0
 maestro/trainer/models/florence_2/metrics.py  | 183 ++++++++++++++++++
 maestro/trainer/models/florence_2/training.py | 107 ++--------
 5 files changed, 320 insertions(+), 92 deletions(-)
 create mode 100644 maestro/trainer/models/florence_2/data_loading.py
 create mode 100644 maestro/trainer/models/florence_2/entities.py
 create mode 100644 maestro/trainer/models/florence_2/metrics.py

diff --git a/maestro/trainer/common/utils/file_system.py b/maestro/trainer/common/utils/file_system.py
index bf4d72f..e258f25 100644
--- a/maestro/trainer/common/utils/file_system.py
+++ b/maestro/trainer/common/utils/file_system.py
@@ -1,4 +1,5 @@
 import json
+import os
 from typing import Union, List
 
 
@@ -26,3 +27,14 @@ def read_file(
     if not strip_white_spaces:
         return lines
     return [line.strip() for line in lines]
+
+
+def save_json(path: str, content: dict) -> None:
+    ensure_parent_dir_exists(path=path)
+    with open(path, "w") as f:
+        json.dump(content, f, indent=4)
+
+
+def ensure_parent_dir_exists(path: str) -> None:
+    parent_dir = os.path.dirname(os.path.abspath(path))
+    os.makedirs(parent_dir, exist_ok=True)
diff --git a/maestro/trainer/models/florence_2/data_loading.py b/maestro/trainer/models/florence_2/data_loading.py
new file mode 100644
index 0000000..4fa9708
--- /dev/null
+++ b/maestro/trainer/models/florence_2/data_loading.py
@@ -0,0 +1,110 @@
+import logging
+import os
+from functools import partial
+from typing import Optional, Tuple, List
+
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from transformers import AutoProcessor
+
+from maestro.trainer.common.data_loaders.datasets import DetectionDataset
+
+
+def prepare_data_loaders(
+    dataset_location: str,
+    train_batch_size: int,
+    processor: AutoProcessor,
+    device: torch.device,
+    num_workers: int = 0,
+    test_batch_size: Optional[int] = None,
+    test_loaders_workers: Optional[int] = None,
+) -> Tuple[
+    DataLoader,
+    Optional[DataLoader],
+    Optional[DataLoader],
+]:
+    test_batch_size = test_batch_size or train_batch_size
+    test_loaders_workers = test_loaders_workers or num_workers
+    train_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="train",
+        batch_size=train_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=num_workers,
+        shuffle=True,
+    )
+    if train_data_loader is None:
+        raise RuntimeError("Could not initialise train data loader")
+    valid_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="valid",
+        batch_size=test_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=test_loaders_workers,
+        shuffle=False,
+    )
+    test_data_loader = prepare_detection_data_loader(
+        dataset_location=dataset_location,
+        split_name="test",
+        batch_size=test_batch_size,
+        processor=processor,
+        device=device,
+        num_workers=test_loaders_workers,
+        shuffle=False,
+    )
+    return train_data_loader, valid_data_loader, test_data_loader
+
+
+def prepare_detection_data_loader(
+    dataset_location: str,
+    split_name: str,
+    batch_size: int,
+    processor: AutoProcessor,
+    device: torch.device,
+    num_workers: int = 0,
+    shuffle: bool = True,
+) -> Optional[DataLoader]:
+    dataset = prepare_detection_dataset(
+        dataset_location=dataset_location,
+        split_name=split_name,
+    )
+    if dataset is None:
+        return None
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        collate_fn=partial(_collate_fn, processor=processor, device=device),
+        num_workers=num_workers,
+        shuffle=shuffle,
+    )
+
+
+def prepare_detection_dataset(
+    dataset_location: str,
+    split_name: str,
+) -> Optional[DetectionDataset]:
+    image_directory_path = os.path.join(dataset_location, split_name)
+    jsonl_file_path = os.path.join(dataset_location, split_name, "annotations.jsonl")
+    if not os.path.exists(image_directory_path):
+        logging.warning(f"Could not data directory: {image_directory_path}")
+        return None
+    if not os.path.exists(jsonl_file_path):
+        logging.warning(f"Could not find JSONL file: {jsonl_file_path}")
+        return None
+    return DetectionDataset(
+        jsonl_file_path=jsonl_file_path,
+        image_directory_path=image_directory_path,
+    )
+
+
+def _collate_fn(
+    batch: Tuple[List[str], List[str], List[Image.Image]],
+    processor: AutoProcessor,
+    device: torch.device,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    questions, answers, images = zip(*batch)
+    inputs = processor(text=list(questions), images=list(images), return_tensors="pt", padding=True).to(device)
+    return inputs, answers
diff --git a/maestro/trainer/models/florence_2/entities.py b/maestro/trainer/models/florence_2/entities.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
new file mode 100644
index 0000000..7c43118
--- /dev/null
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -0,0 +1,183 @@
+import os
+import random
+import re
+from typing import List, Tuple
+
+import cv2
+import numpy as np
+import torch
+import supervision as sv
+from tqdm import tqdm
+from transformers import AutoProcessor, AutoModelForCausalLM
+
+from maestro.trainer.common.data_loaders.datasets import DetectionDataset
+from maestro.trainer.common.utils.file_system import save_json
+from maestro.trainer.models.florence_2.data_loading import prepare_detection_dataset
+
+
+DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 ]+ of [a-zA-Z0-9 ]+)<loc_\d+>"
+
+
+def prepare_detection_training_summary(
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    dataset_location: str,
+    split_name: str,
+    training_dir: str,
+    num_samples_to_visualise: int,
+    device: torch.device,
+) -> None:
+    dataset = prepare_detection_dataset(
+        dataset_location=dataset_location,
+        split_name=split_name,
+    )
+    if dataset is None:
+        return None
+    targets, predictions, post_processed_text_outputs = get_ground_truths_and_predictions(
+        dataset=dataset,
+        processor=processor,
+        model=model,
+        split_name=split_name,
+        device=device,
+    )
+    mean_average_precision = sv.MeanAveragePrecision.from_detections(
+        predictions=predictions,
+        targets=targets,
+    )
+    print(f"{split_name} | map50_95: {mean_average_precision.map50_95:.2f}")
+    print(f"{split_name} | map50: {mean_average_precision.map50:.2f}")
+    print(f"{split_name} |map75: {mean_average_precision.map75:.2f}")
+    dump_metrics(
+        training_dir=training_dir,
+        split_name=split_name,
+        metrics=mean_average_precision,
+    )
+    dump_post_processed_outputs(
+        dataset=dataset,
+        post_processed_text_outputs=post_processed_text_outputs,
+        training_dir=training_dir,
+        split_name=split_name,
+    )
+    dump_visualised_samples(
+        dataset=dataset,
+        targets=targets,
+        predictions=predictions,
+        num_samples_to_visualise=num_samples_to_visualise,
+        training_dir=training_dir,
+        split_name=split_name,
+    )
+
+
+def get_ground_truths_and_predictions(
+    dataset: DetectionDataset,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+    split_name: str,
+    device: torch.device,
+) -> Tuple[List[sv.Detections], List[sv.Detections], List[str]]:
+    classes = extract_classes(dataset=dataset)
+    targets = []
+    predictions = []
+    post_processed_text_outputs = []
+    for idx in tqdm(list(range(len(dataset))), desc=f"Generating {split_name} predictions..."):
+        image, data = dataset.dataset[idx]
+        prefix = data["prefix"]
+        suffix = data["suffix"]
+
+        inputs = processor(text=prefix, images=image, return_tensors="pt").to(device)
+        generated_ids = model.generate(
+            input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3
+        )
+        generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+        prediction = processor.post_process_generation(generated_text, task="<OD>", image_size=image.size)
+        post_processed_text_outputs.append(prediction)
+        prediction = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, prediction, resolution_wh=image.size)
+        prediction = prediction[np.isin(prediction["class_name"], classes)]
+        prediction.class_id = np.array([class_name.index(class_name) for class_name in prediction["class_name"]])
+        prediction.confidence = np.ones(len(prediction))
+        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
+        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
+        target.class_id = np.array([class_name.index(class_name) for class_name in target["class_name"]])
+        targets.append(target)
+        predictions.append(prediction)
+    return targets, predictions, post_processed_text_outputs
+
+
+def extract_classes(dataset: DetectionDataset) -> List[str]:
+    class_set = set()
+    for i in range(len(dataset.dataset)):
+        image, data = dataset.dataset[i]
+        suffix = data["suffix"]
+        classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
+        class_set.update(classes)
+    return sorted(class_set)
+
+
+def dump_metrics(
+    training_dir: str,
+    split_name: str,
+    metrics: sv.MeanAveragePrecision,
+) -> None:
+    target_path = os.path.join(training_dir, split_name, f"metrics_{split_name}.json")
+    content = {
+        "map50_95": metrics.map50_95,
+        "map50": metrics.map50,
+        "map75": metrics.map75,
+    }
+    save_json(path=target_path, content=content)
+
+
+def dump_post_processed_outputs(
+    dataset: DetectionDataset,
+    post_processed_text_outputs: List[str],
+    training_dir: str,
+    split_name: str,
+) -> None:
+    result_dict = {
+        dataset.dataset.entries[idx]["image"]: output for idx, output in enumerate(post_processed_text_outputs)
+    }
+    target_path = os.path.join(training_dir, split_name, f"post_processed_text_predictions_{split_name}.json")
+    save_json(path=target_path, content=result_dict)
+
+
+def dump_visualised_samples(
+    dataset: DetectionDataset,
+    targets: List[sv.Detections],
+    predictions: List[sv.Detections],
+    num_samples_to_visualise: int,
+    training_dir: str,
+    split_name: str,
+) -> None:
+    all_indices = list(range(len(dataset)))
+    random.shuffle(all_indices)
+    selected_indices = all_indices[:num_samples_to_visualise]
+    target_dir = os.path.join(training_dir, "predictions_visualised", split_name)
+    os.makedirs(target_dir, exist_ok=True)
+    boxes_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
+    label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
+    for idx in tqdm(selected_indices, desc="Preparing predictions visualisations..."):
+        image_name = dataset.dataset.entries[idx]["image"]
+        image = dataset.dataset[idx][0]
+        prediction_image = boxes_annotator.annotate(
+            image.copy(),
+            predictions[idx],
+        )
+        prediction_image = np.asarray(
+            label_annotator.annotate(
+                prediction_image,
+                predictions[idx],
+            ).convert("BRG")
+        )
+        target_image = boxes_annotator.annotate(
+            image.copy(),
+            targets[idx],
+        )
+        target_image = np.asarray(
+            label_annotator.annotate(
+                target_image,
+                targets[idx],
+            ).convert("BRG")
+        )
+        concatenated = cv2.hconcat([target_image, prediction_image])
+        target_image_path = os.path.join(target_dir, image_name)
+        cv2.imwrite(target_image_path, concatenated)
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 66fbda2..c201bfd 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -1,12 +1,9 @@
-import logging
 import os
 import shutil
-from dataclasses import dataclass, replace
-from functools import partial
+from dataclasses import replace, dataclass
 from glob import glob
 from typing import Optional, Tuple, List, Literal, Union
 
-from PIL import Image
 import torch
 from peft import PeftModel, LoraConfig, get_peft_model
 from torch.optim import Optimizer, AdamW, Adam, SGD
@@ -16,11 +13,13 @@
 from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
 from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
-from maestro.trainer.common.data_loaders.datasets import DetectionDataset
 from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
+from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
+from maestro.trainer.models.florence_2.metrics import prepare_detection_training_summary
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
+
 DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
 DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
 DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
@@ -49,6 +48,7 @@ class TrainingConfiguration:
     init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
     training_dir: str = "./training/florence-2"
     max_checkpoints_to_keep: int = 3
+    num_samples_to_visualise: int = 64
 
 
 def train(configuration: TrainingConfiguration) -> None:
@@ -114,6 +114,16 @@ def train(configuration: TrainingConfiguration) -> None:
     print(f"Saving best model: {best_model_dir}")
     model.save_pretrained(best_model_dir)
     processor.save_pretrained(best_model_dir)
+    for split_name in ["valid", "test"]:
+        prepare_detection_training_summary(
+            processor=processor,
+            model=model,
+            dataset_location=configuration.dataset_location,
+            split_name=split_name,
+            training_dir=configuration.training_dir,
+            num_samples_to_visualise=configuration.num_samples_to_visualise,
+            device=configuration.device,
+        )
 
 
 def load_model(
@@ -136,83 +146,6 @@ def load_model(
     return processor, model
 
 
-def prepare_data_loaders(
-    dataset_location: str,
-    train_batch_size: int,
-    processor: AutoProcessor,
-    device: torch.device,
-    num_workers: int = 0,
-    test_batch_size: Optional[int] = None,
-    test_loaders_workers: Optional[int] = None,
-) -> Tuple[
-    DataLoader,
-    Optional[DataLoader],
-    Optional[DataLoader],
-]:
-    test_batch_size = test_batch_size or train_batch_size
-    test_loaders_workers = test_loaders_workers or num_workers
-    train_data_loader = prepare_detection_data_loader(
-        dataset_location=dataset_location,
-        split_name="train",
-        batch_size=train_batch_size,
-        processor=processor,
-        device=device,
-        num_workers=num_workers,
-        shuffle=True,
-    )
-    if train_data_loader is None:
-        raise RuntimeError("Could not initialise train data loader")
-    valid_data_loader = prepare_detection_data_loader(
-        dataset_location=dataset_location,
-        split_name="valid",
-        batch_size=test_batch_size,
-        processor=processor,
-        device=device,
-        num_workers=test_loaders_workers,
-        shuffle=False,
-    )
-    test_data_loader = prepare_detection_data_loader(
-        dataset_location=dataset_location,
-        split_name="test",
-        batch_size=test_batch_size,
-        processor=processor,
-        device=device,
-        num_workers=test_loaders_workers,
-        shuffle=False,
-    )
-    return train_data_loader, valid_data_loader, test_data_loader
-
-
-def prepare_detection_data_loader(
-    dataset_location: str,
-    split_name: str,
-    batch_size: int,
-    processor: AutoProcessor,
-    device: torch.device,
-    num_workers: int = 0,
-    shuffle: bool = True,
-) -> Optional[DataLoader]:
-    image_directory_path = os.path.join(dataset_location, split_name)
-    jsonl_file_path = os.path.join(dataset_location, split_name, "annotations.jsonl")
-    if not os.path.exists(image_directory_path):
-        logging.warning(f"Could not data directory: {image_directory_path}")
-        return None
-    if not os.path.exists(jsonl_file_path):
-        logging.warning(f"Could not find JSONL file: {jsonl_file_path}")
-        return None
-    dataset = DetectionDataset(
-        jsonl_file_path=jsonl_file_path,
-        image_directory_path=image_directory_path,
-    )
-    return DataLoader(
-        dataset,
-        batch_size=batch_size,
-        collate_fn=partial(_collate_fn, processor=processor, device=device),
-        num_workers=num_workers,
-        shuffle=shuffle,
-    )
-
-
 def prepare_peft_model(
     model: AutoModelForCausalLM,
     r: int = 8,
@@ -384,13 +317,3 @@ def _get_optimizer(model: PeftModel, configuration: TrainingConfiguration) -> Op
     if configuration.optimiser == "adam":
         return Adam(model.parameters(), lr=configuration.learning_rate)
     return SGD(model.parameters(), lr=configuration.learning_rate)
-
-
-def _collate_fn(
-    batch: Tuple[List[str], List[str], List[Image.Image]],
-    processor: AutoProcessor,
-    device: torch.device,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    questions, answers, images = zip(*batch)
-    inputs = processor(text=list(questions), images=list(images), return_tensors="pt", padding=True).to(device)
-    return inputs, answers

From 44a2d0be5ee34fac0fe103703569e255b4ca9b8d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 15:08:24 +0200
Subject: [PATCH 21/69] Fix visualisation bug

---
 maestro/trainer/models/florence_2/metrics.py  | 4 ++--
 maestro/trainer/models/florence_2/training.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 7c43118..83a22bc 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -166,7 +166,7 @@ def dump_visualised_samples(
             label_annotator.annotate(
                 prediction_image,
                 predictions[idx],
-            ).convert("BRG")
+            ).convert("BGR")
         )
         target_image = boxes_annotator.annotate(
             image.copy(),
@@ -176,7 +176,7 @@ def dump_visualised_samples(
             label_annotator.annotate(
                 target_image,
                 targets[idx],
-            ).convert("BRG")
+            ).convert("BGR")
         )
         concatenated = cv2.hconcat([target_image, prediction_image])
         target_image_path = os.path.join(target_dir, image_name)
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index c201bfd..6d9d7c3 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -275,9 +275,9 @@ def run_validation_epoch(
     val_loss = 0.0
     epoch_marker = ""
     if epoch_number is not None:
-        epoch_marker = f"Epoch {epoch_number + 1}/{configuration.training_epochs}"
+        epoch_marker = f"| Epoch {epoch_number + 1}/{configuration.training_epochs}"
     with torch.no_grad():
-        for inputs, answers in tqdm(loader, desc=f"{title} | {epoch_marker}"):
+        for inputs, answers in tqdm(loader, desc=f"{title} {epoch_marker}"):
             input_ids = inputs["input_ids"]
             pixel_values = inputs["pixel_values"]
             labels = processor.tokenizer(

From 5048079c377eb9d007e2717ec534d545ac8962a4 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 15:13:48 +0200
Subject: [PATCH 22/69] Fix visualisation bug

---
 maestro/trainer/models/florence_2/metrics.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 83a22bc..f9a835e 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -166,8 +166,8 @@ def dump_visualised_samples(
             label_annotator.annotate(
                 prediction_image,
                 predictions[idx],
-            ).convert("BGR")
-        )
+            )
+        )[:, :, ::-1]
         target_image = boxes_annotator.annotate(
             image.copy(),
             targets[idx],
@@ -176,8 +176,8 @@ def dump_visualised_samples(
             label_annotator.annotate(
                 target_image,
                 targets[idx],
-            ).convert("BGR")
-        )
+            )
+        )[:, :, ::-1]
         concatenated = cv2.hconcat([target_image, prediction_image])
         target_image_path = os.path.join(target_dir, image_name)
         cv2.imwrite(target_image_path, concatenated)

From 7c62cae6ef0dcacd1232956178e7114831222dec Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 17:15:41 +0200
Subject: [PATCH 23/69] Add metrics plots

---
 maestro/trainer/models/florence_2/metrics.py  | 46 ++++++++++++++++++-
 maestro/trainer/models/florence_2/training.py | 35 ++++++++++++--
 2 files changed, 76 insertions(+), 5 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index f9a835e..fb0ba29 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -3,6 +3,7 @@
 import re
 from typing import List, Tuple
 
+import matplotlib.pyplot as plt
 import cv2
 import numpy as np
 import torch
@@ -12,6 +13,7 @@
 
 from maestro.trainer.common.data_loaders.datasets import DetectionDataset
 from maestro.trainer.common.utils.file_system import save_json
+from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
 from maestro.trainer.models.florence_2.data_loading import prepare_detection_dataset
 
 
@@ -46,7 +48,7 @@ def prepare_detection_training_summary(
     )
     print(f"{split_name} | map50_95: {mean_average_precision.map50_95:.2f}")
     print(f"{split_name} | map50: {mean_average_precision.map50:.2f}")
-    print(f"{split_name} |map75: {mean_average_precision.map75:.2f}")
+    print(f"{split_name} | map75: {mean_average_precision.map75:.2f}")
     dump_metrics(
         training_dir=training_dir,
         split_name=split_name,
@@ -118,7 +120,7 @@ def dump_metrics(
     split_name: str,
     metrics: sv.MeanAveragePrecision,
 ) -> None:
-    target_path = os.path.join(training_dir, split_name, f"metrics_{split_name}.json")
+    target_path = os.path.join(training_dir, "metrics", split_name, f"metrics_{split_name}.json")
     content = {
         "map50_95": metrics.map50_95,
         "map50": metrics.map50,
@@ -181,3 +183,43 @@ def dump_visualised_samples(
         concatenated = cv2.hconcat([target_image, prediction_image])
         target_image_path = os.path.join(target_dir, image_name)
         cv2.imwrite(target_image_path, concatenated)
+
+
+def summarise_training_metrics(
+    training_metrics_tracker: MetricsTracker,
+    validation_metrics_tracker: MetricsTracker,
+    training_dir: str,
+) -> None:
+    summarise_metrics(metrics_tracker=training_metrics_tracker, training_dir=training_dir, split_name="train")
+    summarise_metrics(metrics_tracker=validation_metrics_tracker, training_dir=training_dir, split_name="valid")
+
+
+def summarise_metrics(
+    metrics_tracker: MetricsTracker,
+    training_dir: str,
+    split_name: str,
+) -> None:
+    plots_dir_path = os.path.join(training_dir, "metrics", split_name)
+    for metric_name in metrics_tracker.describe_metrics():
+        plot_path = os.path.join(plots_dir_path, f"metric_{metric_name}_plot.png")
+        plt.clf()
+        metric_values_with_index = metrics_tracker.get_metric_values(
+            metric=metric_name,
+            with_index=True,
+        )
+        xs = np.arange(0, len(metric_values_with_index))
+        xticks_xs, xticks_labels = [], []
+        previous = None
+        for v, x in zip(metric_values_with_index, xs):
+            if v[0] != previous:
+                xticks_xs.append(x)
+                xticks_labels.append(v[0])
+            previous = v[0]
+        ys = [e[2] for e in metric_values_with_index]
+        plt.scatter(xs, ys, marker="x")
+        plt.plot(xs, ys, linestyle="dashed", linewidth=0.3)
+        plt.title(f"Value of {metric_name} for {split_name} set")
+        plt.xticks(xticks_xs, labels=xticks_labels)
+        plt.xlabel("Epochs")
+        plt.savefig(plot_path, dpi=120)
+        plt.clf()
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 6d9d7c3..54993c5 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -14,9 +14,10 @@
 
 from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
 from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
+from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
-from maestro.trainer.models.florence_2.metrics import prepare_detection_training_summary
+from maestro.trainer.models.florence_2.metrics import prepare_detection_training_summary, summarise_training_metrics
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
 
@@ -89,12 +90,16 @@ def train(configuration: TrainingConfiguration) -> None:
         init_lora_weights=configuration.init_lora_weights,
         revision=configuration.revision,
     )
+    training_metrics_tracker = MetricsTracker.init(metrics=["loss"])
+    validation_metrics_tracker = MetricsTracker.init(metrics=["loss"])
     run_training_loop(
         processor=processor,
         model=peft_model,
         data_loaders=(train_loader, val_loader),
         configuration=configuration,
         checkpoints_leaderboard=checkpoints_leaderboard,
+        training_metrics_tracker=training_metrics_tracker,
+        validation_metrics_tracker=validation_metrics_tracker,
     )
     best_model_path = checkpoints_leaderboard.get_best_model()
     print(f"Loading best model from {best_model_path}")
@@ -114,6 +119,11 @@ def train(configuration: TrainingConfiguration) -> None:
     print(f"Saving best model: {best_model_dir}")
     model.save_pretrained(best_model_dir)
     processor.save_pretrained(best_model_dir)
+    summarise_training_metrics(
+        training_metrics_tracker=training_metrics_tracker,
+        validation_metrics_tracker=validation_metrics_tracker,
+        training_dir=configuration.training_dir,
+    )
     for split_name in ["valid", "test"]:
         prepare_detection_training_summary(
             processor=processor,
@@ -180,6 +190,8 @@ def run_training_loop(
     data_loaders: Tuple[DataLoader, Optional[DataLoader]],
     configuration: TrainingConfiguration,
     checkpoints_leaderboard: CheckpointsLeaderboard,
+    training_metrics_tracker: MetricsTracker,
+    validation_metrics_tracker: MetricsTracker,
 ) -> None:
     train_loader, val_loader = data_loaders
     optimizer = _get_optimizer(model=model, configuration=configuration)
@@ -201,6 +213,8 @@ def run_training_loop(
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
             checkpoints_leaderboard=checkpoints_leaderboard,
+            training_metrics_tracker=training_metrics_tracker,
+            validation_metrics_tracker=validation_metrics_tracker,
         )
 
 
@@ -214,11 +228,13 @@ def run_training_epoch(
     optimizer: Optimizer,
     lr_scheduler: LRScheduler,
     checkpoints_leaderboard: CheckpointsLeaderboard,
+    training_metrics_tracker: MetricsTracker,
+    validation_metrics_tracker: MetricsTracker,
 ) -> None:
     model.train()
     training_losses: List[float] = []
     training_iterator = tqdm(train_loader, desc=f"Epoch {epoch_number + 1}/{configuration.training_epochs}")
-    for inputs, answers in training_iterator:
+    for step_id, (inputs, answers) in enumerate(training_iterator):
         input_ids = inputs["input_ids"]
         pixel_values = inputs["pixel_values"]
         labels = processor.tokenizer(
@@ -230,7 +246,14 @@ def run_training_epoch(
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
-        training_losses.append(loss.item())
+        loss = loss.item()
+        training_metrics_tracker.register(
+            metric="loss",
+            epoch=epoch_number,
+            step=step_id + 1,
+            value=loss,
+        )
+        training_losses.append(loss)
         last_100_losses = training_losses[-100:]
         loss_moving_average = sum(last_100_losses) / len(last_100_losses) if len(last_100_losses) > 0 else 0.0
         training_iterator.set_description(
@@ -248,6 +271,12 @@ def run_training_epoch(
         epoch_number=epoch_number,
         configuration=configuration,
     )
+    validation_metrics_tracker.register(
+        metric="loss",
+        epoch=epoch_number,
+        step=1,
+        value=validation_loss,
+    )
     checkpoint_dir = os.path.join(configuration.training_dir, "checkpoints", str(epoch_number))
     should_save, to_remove = checkpoints_leaderboard.register_checkpoint(
         epoch=epoch_number,

From 783dc886750dd24c6fa3f689538e9d2b16d94be3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 17:22:12 +0200
Subject: [PATCH 24/69] Add metrics plots

---
 maestro/trainer/models/florence_2/metrics.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index fb0ba29..d45c0b0 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -200,6 +200,7 @@ def summarise_metrics(
     split_name: str,
 ) -> None:
     plots_dir_path = os.path.join(training_dir, "metrics", split_name)
+    os.makedirs(plots_dir_path, exist_ok=True)
     for metric_name in metrics_tracker.describe_metrics():
         plot_path = os.path.join(plots_dir_path, f"metric_{metric_name}_plot.png")
         plt.clf()

From dbe7593f9e644d5f203d8421e5297ed6dc768d31 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 17:31:23 +0200
Subject: [PATCH 25/69] Add metrics plots

---
 maestro/trainer/models/florence_2/metrics.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index d45c0b0..5746665 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -138,7 +138,12 @@ def dump_post_processed_outputs(
     result_dict = {
         dataset.dataset.entries[idx]["image"]: output for idx, output in enumerate(post_processed_text_outputs)
     }
-    target_path = os.path.join(training_dir, split_name, f"post_processed_text_predictions_{split_name}.json")
+    target_path = os.path.join(
+        training_dir,
+        "model_debug",
+        split_name,
+        f"post_processed_text_predictions_{split_name}.json",
+    )
     save_json(path=target_path, content=result_dict)
 
 

From 2fd4d7d1ab43891fa1aaa5a0d62d9472c1878283 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 29 Aug 2024 17:53:54 +0200
Subject: [PATCH 26/69] Fix minor issues

---
 maestro/trainer/common/utils/leaderboard.py   | 1 +
 maestro/trainer/models/florence_2/training.py | 6 +++---
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/maestro/trainer/common/utils/leaderboard.py b/maestro/trainer/common/utils/leaderboard.py
index 7d147fa..6f381fe 100644
--- a/maestro/trainer/common/utils/leaderboard.py
+++ b/maestro/trainer/common/utils/leaderboard.py
@@ -25,6 +25,7 @@ def register_checkpoint(self, epoch: int, path: str, loss: float) -> Tuple[bool,
         if loss >= max_loss_in_leaderboard:  # type: ignore
             return False, None
         to_be_removed, _ = self._leaderboard.pop(max_loss_key)  # type: ignore
+        self._leaderboard[epoch] = (path, loss)
         return True, to_be_removed
 
     def get_best_model(self) -> str:
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 54993c5..4a0cd5e 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -208,7 +208,7 @@ def run_training_loop(
             model=model,
             train_loader=train_loader,
             val_loader=val_loader,
-            epoch_number=epoch,
+            epoch_number=epoch + 1,
             configuration=configuration,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
@@ -233,7 +233,7 @@ def run_training_epoch(
 ) -> None:
     model.train()
     training_losses: List[float] = []
-    training_iterator = tqdm(train_loader, desc=f"Epoch {epoch_number + 1}/{configuration.training_epochs}")
+    training_iterator = tqdm(train_loader, desc=f"Epoch {epoch_number}/{configuration.training_epochs}")
     for step_id, (inputs, answers) in enumerate(training_iterator):
         input_ids = inputs["input_ids"]
         pixel_values = inputs["pixel_values"]
@@ -257,7 +257,7 @@ def run_training_epoch(
         last_100_losses = training_losses[-100:]
         loss_moving_average = sum(last_100_losses) / len(last_100_losses) if len(last_100_losses) > 0 else 0.0
         training_iterator.set_description(
-            f"Epoch {epoch_number + 1}/{configuration.training_epochs}. Loss: {round(loss_moving_average, 4)}"
+            f"Epoch {epoch_number}/{configuration.training_epochs}. Loss: {round(loss_moving_average, 4)}"
         )
     if len(training_losses) > 0:
         avg_train_loss = sum(training_losses) / len(training_losses)

From 6ff192651684f351d8906a5f3e17e4451ca21133 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 3 Sep 2024 13:00:18 +0200
Subject: [PATCH 27/69] fix: current Florence-2 training pipeline is missing
 `timm`

---
 requirements/requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index ce70d29..c10deb3 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -7,3 +7,4 @@ sentencepiece~=0.2.0
 peft~=0.12.0
 flash-attn~=2.6.3
 einops~=0.8.0
+timm~=1.0.9
\ No newline at end of file

From 47aef063789453bbd76bb451d570e0d52fc5d99f Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 4 Sep 2024 11:18:59 +0200
Subject: [PATCH 28/69] update maestro `README.md`

---
 README.md | 133 ++++++------------------------------------------------
 1 file changed, 13 insertions(+), 120 deletions(-)

diff --git a/README.md b/README.md
index ec1dfa1..9814a8e 100644
--- a/README.md
+++ b/README.md
@@ -1,142 +1,35 @@
 
 <div align="center">
 
-  <h1>multimodal-maestro</h1>
+  <h1>maestro</h1>
 
-  <br>
-
-  [![version](https://badge.fury.io/py/maestro.svg)](https://badge.fury.io/py/maestro)
-  [![license](https://img.shields.io/pypi/l/maestro)](https://github.com/roboflow/multimodal-maestro/blob/main/LICENSE)
-  [![python-version](https://img.shields.io/pypi/pyversions/maestro)](https://badge.fury.io/py/maestro)
-  [![Gradio](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/Roboflow/SoM)
-  [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/multimodal-maestro/blob/develop/cookbooks/multimodal_maestro_gpt_4_vision.ipynb)
+  <p>coming: when it's ready...</p>
 
 </div>
 
 ## 👋 hello
 
-Multimodal-Maestro gives you more control over large multimodal models to get the 
-outputs you want. With more effective prompting tactics, you can get multimodal models 
-to do tasks you didn't know (or think!) were possible. Curious how it works? Try our 
-[HF space](https://huggingface.co/spaces/Roboflow/SoM)!
+**maestro** is a tool designed to streamline and accelerate the fine-tuning process for 
+multimodal models. It provides ready-to-use recipes for fine-tuning popular 
+vision-language models (VLMs) such as **Florence-2**, **PaliGemma**, and 
+**Phi-3.5 Vision** on downstream vision-language tasks.
 
 ## 💻 install
 
-⚠️ Our package has been renamed to `maestro`. Install the package in a
-[**3.11>=Python>=3.8**](https://www.python.org/) environment.
+Pip install the supervision package in a
+[**Python>=3.8**](https://www.python.org/) environment.
 
 ```bash
 pip install maestro
 ```
 
-## 🔌 API
-
-🚧 The project is still under construction. The redesigned API is coming soon.
-
-![maestro-docs-Snap](https://github.com/roboflow/multimodal-maestro/assets/26109316/a787b7c0-527e-465a-9ca9-d46f4d63ea53)
-
-## 🧑‍🍳 prompting cookbooks
-
-| Description                                                     | Colab                                                                                                                                                                                                   |
-|:----------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-| Prompt LMMs with Multimodal Maestro                             | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/multimodal-maestro/blob/develop/cookbooks/multimodal_maestro_gpt_4_vision.ipynb) |
-| Manually annotate ONE image and let GPT-4V annotate ALL of them | [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow/multimodal-maestro/blob/develop/cookbooks/grounding_dino_and_gpt4_vision.ipynb)  |
-
-
 ## 🚀 example
 
-```
-Find dog.
-
->>> The dog is prominently featured in the center of the image with the label [9].
-```
-
-<details close>
-<summary>👉 read more</summary>
-
-<br>
-
-- **load image**
-
-  ```python
-  import cv2
-  
-  image = cv2.imread("...")
-  ```
-
-- **create and refine marks**
-
-  ```python
-  import maestro
-  
-  generator = maestro.SegmentAnythingMarkGenerator(device='cuda')
-  marks = generator.generate(image=image)
-  marks = maestro.refine_marks(marks=marks)
-  ```
-
-- **visualize marks**
-
-  ```python
-  mark_visualizer = maestro.MarkVisualizer()
-  marked_image = mark_visualizer.visualize(image=image, marks=marks)
-  ```
-  ![image-vs-marked-image](https://github.com/roboflow/multimodal-maestro/assets/26109316/92951ed2-65c0-475a-9279-6fd344757092)
-
-- **prompt**
-
-  ```python
-  prompt = "Find dog."
-  
-  response = maestro.prompt_image(api_key=api_key, image=marked_image, prompt=prompt)
-  ```
-  
-  ```
-  >>> "The dog is prominently featured in the center of the image with the label [9]."
-  ```
-
-- **extract related marks**
-
-  ```python
-  masks = maestro.extract_relevant_masks(text=response, detections=refined_marks)
-  ```
-  
-  ```
-  >>> {'6': array([
-  ...     [False, False, False, ..., False, False, False],
-  ...     [False, False, False, ..., False, False, False],
-  ...     [False, False, False, ..., False, False, False],
-  ...     ...,
-  ...     [ True,  True,  True, ..., False, False, False],
-  ...     [ True,  True,  True, ..., False, False, False],
-  ...     [ True,  True,  True, ..., False, False, False]])
-  ... }
-  ```
-
-</details>
-
-![multimodal-maestro](https://github.com/roboflow/multimodal-maestro/assets/26109316/c04f2b18-2a1d-4535-9582-e5d3ec0a926e)
+Documentation and Florence-2 fine-tuning examples for object detection and VQA coming 
+soon.
 
 ## 🚧 roadmap
 
-- [ ] Rewriting the `maestro` API.
-- [ ] Update [HF space](https://huggingface.co/spaces/Roboflow/SoM).
-- [ ] Documentation page.
-- [ ] Add GroundingDINO prompting strategy.
-- [ ] CovVLM demo.
-- [ ] Qwen-VL demo.
-
-## 💜 acknowledgement
-
-- [Set-of-Mark Prompting Unleashes Extraordinary Visual Grounding
-in GPT-4V](https://arxiv.org/abs/2310.11441) by Jianwei Yang, Hao Zhang, Feng Li, Xueyan
-Zou, Chunyuan Li, Jianfeng Gao.
-- [The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)](https://arxiv.org/abs/2309.17421)
-by Zhengyuan Yang, Linjie Li, Kevin Lin, Jianfeng Wang, Chung-Ching Lin, Zicheng Liu, 
-Lijuan Wang
-
-## 🦸 contribution
-
-We would love your help in making this repository even better! If you noticed any bug, 
-or if you have any suggestions for improvement, feel free to open an 
-[issue](https://github.com/roboflow/multimodal-maestro/issues) or submit a 
-[pull request](https://github.com/roboflow/multimodal-maestro/pulls).
+- [ ] Release a CLI for predefined fine-tuning recipes.
+- [ ] Multi-GPU fine-tuning support.
+- [ ] Allow multi-dataset fine-tuning and support multiple tasks at the same time.

From 36251e581b232f8302419e8d3e8227fd76e89dac Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 4 Sep 2024 14:19:57 +0200
Subject: [PATCH 29/69] wip

---
 maestro/trainer/models/florence_2/training.py | 69 ++++++++++---------
 1 file changed, 36 insertions(+), 33 deletions(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 4a0cd5e..91239e0 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -101,39 +101,42 @@ def train(configuration: TrainingConfiguration) -> None:
         training_metrics_tracker=training_metrics_tracker,
         validation_metrics_tracker=validation_metrics_tracker,
     )
-    best_model_path = checkpoints_leaderboard.get_best_model()
-    print(f"Loading best model from {best_model_path}")
-    processor, model = load_model(
-        model_id_or_path=best_model_path,
-    )
-    if test_loader is not None:
-        run_validation_epoch(
-            processor=processor,
-            model=model,
-            loader=test_loader,
-            epoch_number=None,
-            configuration=configuration,
-            title="Test",
-        )
-    best_model_dir = os.path.join(configuration.training_dir, "best_model")
-    print(f"Saving best model: {best_model_dir}")
-    model.save_pretrained(best_model_dir)
-    processor.save_pretrained(best_model_dir)
-    summarise_training_metrics(
-        training_metrics_tracker=training_metrics_tracker,
-        validation_metrics_tracker=validation_metrics_tracker,
-        training_dir=configuration.training_dir,
-    )
-    for split_name in ["valid", "test"]:
-        prepare_detection_training_summary(
-            processor=processor,
-            model=model,
-            dataset_location=configuration.dataset_location,
-            split_name=split_name,
-            training_dir=configuration.training_dir,
-            num_samples_to_visualise=configuration.num_samples_to_visualise,
-            device=configuration.device,
-        )
+
+    return training_metrics_tracker, validation_metrics_tracker
+
+    # best_model_path = checkpoints_leaderboard.get_best_model()
+    # print(f"Loading best model from {best_model_path}")
+    # processor, model = load_model(
+    #     model_id_or_path=best_model_path,
+    # )
+    # if test_loader is not None:
+    #     run_validation_epoch(
+    #         processor=processor,
+    #         model=model,
+    #         loader=test_loader,
+    #         epoch_number=None,
+    #         configuration=configuration,
+    #         title="Test",
+    #     )
+    # best_model_dir = os.path.join(configuration.training_dir, "best_model")
+    # print(f"Saving best model: {best_model_dir}")
+    # model.save_pretrained(best_model_dir)
+    # processor.save_pretrained(best_model_dir)
+    # summarise_training_metrics(
+    #     training_metrics_tracker=training_metrics_tracker,
+    #     validation_metrics_tracker=validation_metrics_tracker,
+    #     training_dir=configuration.training_dir,
+    # )
+    # for split_name in ["valid", "test"]:
+    #     prepare_detection_training_summary(
+    #         processor=processor,
+    #         model=model,
+    #         dataset_location=configuration.dataset_location,
+    #         split_name=split_name,
+    #         training_dir=configuration.training_dir,
+    #         num_samples_to_visualise=configuration.num_samples_to_visualise,
+    #         device=configuration.device,
+    #     )
 
 
 def load_model(

From 5f025afeea7cc74da3c4321020f0423deb179275 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 4 Sep 2024 16:13:24 +0200
Subject: [PATCH 30/69] ready for test

---
 .../trainer/common/utils/metrics_tracing.py   | 70 +++++++++++++++++
 maestro/trainer/models/florence_2/metrics.py  | 43 -----------
 maestro/trainer/models/florence_2/training.py | 76 +++++++++----------
 requirements/requirements.txt                 |  2 +-
 4 files changed, 109 insertions(+), 82 deletions(-)

diff --git a/maestro/trainer/common/utils/metrics_tracing.py b/maestro/trainer/common/utils/metrics_tracing.py
index 6c5b6ae..a69a2ae 100644
--- a/maestro/trainer/common/utils/metrics_tracing.py
+++ b/maestro/trainer/common/utils/metrics_tracing.py
@@ -1,7 +1,10 @@
 from __future__ import annotations
 
+from collections import defaultdict
 from typing import Dict, Tuple, List
 
+import matplotlib.pyplot as plt
+
 
 class MetricsTracker:
 
@@ -26,3 +29,70 @@ def get_metric_values(
         if with_index:
             return self._metrics[metric]
         return [value[2] for value in self._metrics[metric]]
+
+
+def aggregate_by_epoch(metric_values: List[Tuple[int, int, float]]) -> Dict[int, float]:
+    epoch_data = defaultdict(list)
+    for epoch, step, value in metric_values:
+        epoch_data[epoch].append(value)
+    avg_per_epoch = {
+        epoch: sum(values) / len(values)
+        for epoch, values
+        in epoch_data.items()
+    }
+    return avg_per_epoch
+
+
+def save_metric_plots(
+    training_tracker: MetricsTracker,
+    validation_tracker: MetricsTracker,
+    output_dir: str
+):
+    training_metrics = training_tracker.describe_metrics()
+    validation_metrics = validation_tracker.describe_metrics()
+    all_metrics = set(training_metrics + validation_metrics)
+
+    for metric in all_metrics:
+        plt.figure(figsize=(8, 6))
+
+        if metric in training_metrics:
+            training_values = training_tracker.get_metric_values(
+                metric=metric, with_index=True)
+            training_avg_values = aggregate_by_epoch(training_values)
+            training_epochs = sorted(training_avg_values.keys())
+            training_vals = [training_avg_values[epoch] for epoch in training_epochs]
+            plt.plot(
+                x=training_epochs,
+                y=training_vals,
+                label=f'Training {metric}',
+                marker='o',
+                linestyle='-',
+                color='blue'
+            )
+
+        if metric in validation_metrics:
+            validation_values = validation_tracker.get_metric_values(
+                metric=metric, with_index=True)
+            validation_avg_values = aggregate_by_epoch(validation_values)
+            validation_epochs = sorted(validation_avg_values.keys())
+            validation_vals = [
+                validation_avg_values[epoch]
+                for epoch
+                in validation_epochs
+            ]
+            plt.plot(
+                x=validation_epochs,
+                y=validation_vals,
+                label=f'Validation {metric}',
+                marker='o',
+                linestyle='--',
+                color='orange'
+            )
+
+        plt.title(f'{metric.capitalize()} over Epochs')
+        plt.xlabel('Epoch')
+        plt.ylabel(f'{metric.capitalize()} Value')
+        plt.legend()
+        plt.grid(True)
+        plt.savefig(f'{output_dir}/{metric}_plot.png')
+        plt.close()
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 5746665..3ed7aab 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -3,7 +3,6 @@
 import re
 from typing import List, Tuple
 
-import matplotlib.pyplot as plt
 import cv2
 import numpy as np
 import torch
@@ -13,7 +12,6 @@
 
 from maestro.trainer.common.data_loaders.datasets import DetectionDataset
 from maestro.trainer.common.utils.file_system import save_json
-from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
 from maestro.trainer.models.florence_2.data_loading import prepare_detection_dataset
 
 
@@ -188,44 +186,3 @@ def dump_visualised_samples(
         concatenated = cv2.hconcat([target_image, prediction_image])
         target_image_path = os.path.join(target_dir, image_name)
         cv2.imwrite(target_image_path, concatenated)
-
-
-def summarise_training_metrics(
-    training_metrics_tracker: MetricsTracker,
-    validation_metrics_tracker: MetricsTracker,
-    training_dir: str,
-) -> None:
-    summarise_metrics(metrics_tracker=training_metrics_tracker, training_dir=training_dir, split_name="train")
-    summarise_metrics(metrics_tracker=validation_metrics_tracker, training_dir=training_dir, split_name="valid")
-
-
-def summarise_metrics(
-    metrics_tracker: MetricsTracker,
-    training_dir: str,
-    split_name: str,
-) -> None:
-    plots_dir_path = os.path.join(training_dir, "metrics", split_name)
-    os.makedirs(plots_dir_path, exist_ok=True)
-    for metric_name in metrics_tracker.describe_metrics():
-        plot_path = os.path.join(plots_dir_path, f"metric_{metric_name}_plot.png")
-        plt.clf()
-        metric_values_with_index = metrics_tracker.get_metric_values(
-            metric=metric_name,
-            with_index=True,
-        )
-        xs = np.arange(0, len(metric_values_with_index))
-        xticks_xs, xticks_labels = [], []
-        previous = None
-        for v, x in zip(metric_values_with_index, xs):
-            if v[0] != previous:
-                xticks_xs.append(x)
-                xticks_labels.append(v[0])
-            previous = v[0]
-        ys = [e[2] for e in metric_values_with_index]
-        plt.scatter(xs, ys, marker="x")
-        plt.plot(xs, ys, linestyle="dashed", linewidth=0.3)
-        plt.title(f"Value of {metric_name} for {split_name} set")
-        plt.xticks(xticks_xs, labels=xticks_labels)
-        plt.xlabel("Epochs")
-        plt.savefig(plot_path, dpi=120)
-        plt.clf()
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 91239e0..d5351ef 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -12,12 +12,14 @@
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
-from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
+from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, \
+    DEFAULT_CUDA_DEVICE
 from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
-from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
+from maestro.trainer.common.utils.metrics_tracing import MetricsTracker, \
+    save_metric_plots
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
-from maestro.trainer.models.florence_2.metrics import prepare_detection_training_summary, summarise_training_metrics
+from maestro.trainer.models.florence_2.metrics import prepare_detection_training_summary
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
 
@@ -102,41 +104,39 @@ def train(configuration: TrainingConfiguration) -> None:
         validation_metrics_tracker=validation_metrics_tracker,
     )
 
-    return training_metrics_tracker, validation_metrics_tracker
-
-    # best_model_path = checkpoints_leaderboard.get_best_model()
-    # print(f"Loading best model from {best_model_path}")
-    # processor, model = load_model(
-    #     model_id_or_path=best_model_path,
-    # )
-    # if test_loader is not None:
-    #     run_validation_epoch(
-    #         processor=processor,
-    #         model=model,
-    #         loader=test_loader,
-    #         epoch_number=None,
-    #         configuration=configuration,
-    #         title="Test",
-    #     )
-    # best_model_dir = os.path.join(configuration.training_dir, "best_model")
-    # print(f"Saving best model: {best_model_dir}")
-    # model.save_pretrained(best_model_dir)
-    # processor.save_pretrained(best_model_dir)
-    # summarise_training_metrics(
-    #     training_metrics_tracker=training_metrics_tracker,
-    #     validation_metrics_tracker=validation_metrics_tracker,
-    #     training_dir=configuration.training_dir,
-    # )
-    # for split_name in ["valid", "test"]:
-    #     prepare_detection_training_summary(
-    #         processor=processor,
-    #         model=model,
-    #         dataset_location=configuration.dataset_location,
-    #         split_name=split_name,
-    #         training_dir=configuration.training_dir,
-    #         num_samples_to_visualise=configuration.num_samples_to_visualise,
-    #         device=configuration.device,
-    #     )
+    best_model_path = checkpoints_leaderboard.get_best_model()
+    print(f"Loading best model from {best_model_path}")
+    processor, model = load_model(
+        model_id_or_path=best_model_path,
+    )
+    if test_loader is not None:
+        run_validation_epoch(
+            processor=processor,
+            model=model,
+            loader=test_loader,
+            epoch_number=None,
+            configuration=configuration,
+            title="Test",
+        )
+    best_model_dir = os.path.join(configuration.training_dir, "best_model")
+    print(f"Saving best model: {best_model_dir}")
+    model.save_pretrained(best_model_dir)
+    processor.save_pretrained(best_model_dir)
+    save_metric_plots(
+        training_tracker=training_metrics_tracker,
+        validation_tracker=validation_metrics_tracker,
+        output_dir=configuration.training_dir,
+    )
+    for split_name in ["valid", "test"]:
+        prepare_detection_training_summary(
+            processor=processor,
+            model=model,
+            dataset_location=configuration.dataset_location,
+            split_name=split_name,
+            training_dir=configuration.training_dir,
+            num_samples_to_visualise=configuration.num_samples_to_visualise,
+            device=configuration.device,
+        )
 
 
 def load_model(
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index c10deb3..d640ad7 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -5,6 +5,6 @@ torch~=2.4.0
 accelerate~=0.33.0
 sentencepiece~=0.2.0
 peft~=0.12.0
-flash-attn~=2.6.3
+flash-attn~=2.6.3 # does not work on mac
 einops~=0.8.0
 timm~=1.0.9
\ No newline at end of file

From 166f4ecb205aadcb7342f1b6a197f687a4e77c41 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 4 Sep 2024 16:31:40 +0200
Subject: [PATCH 31/69] small fix

---
 maestro/trainer/common/utils/metrics_tracing.py | 8 ++++----
 maestro/trainer/models/florence_2/training.py   | 2 +-
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/maestro/trainer/common/utils/metrics_tracing.py b/maestro/trainer/common/utils/metrics_tracing.py
index a69a2ae..95ea552 100644
--- a/maestro/trainer/common/utils/metrics_tracing.py
+++ b/maestro/trainer/common/utils/metrics_tracing.py
@@ -62,8 +62,8 @@ def save_metric_plots(
             training_epochs = sorted(training_avg_values.keys())
             training_vals = [training_avg_values[epoch] for epoch in training_epochs]
             plt.plot(
-                x=training_epochs,
-                y=training_vals,
+                training_epochs,
+                training_vals,
                 label=f'Training {metric}',
                 marker='o',
                 linestyle='-',
@@ -81,8 +81,8 @@ def save_metric_plots(
                 in validation_epochs
             ]
             plt.plot(
-                x=validation_epochs,
-                y=validation_vals,
+                validation_epochs,
+                validation_vals,
                 label=f'Validation {metric}',
                 marker='o',
                 linestyle='--',
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index d5351ef..e993736 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -307,7 +307,7 @@ def run_validation_epoch(
     val_loss = 0.0
     epoch_marker = ""
     if epoch_number is not None:
-        epoch_marker = f"| Epoch {epoch_number + 1}/{configuration.training_epochs}"
+        epoch_marker = f"| Epoch {epoch_number}/{configuration.training_epochs}"
     with torch.no_grad():
         for inputs, answers in tqdm(loader, desc=f"{title} {epoch_marker}"):
             input_ids = inputs["input_ids"]

From 7ab38eb82147b0a89c9e7c0b45b91d340a383e18 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 4 Sep 2024 18:10:25 +0200
Subject: [PATCH 32/69] ready for test

---
 .../trainer/common/utils/metrics_tracing.py   | 27 +++++++++++++++++++
 maestro/trainer/models/florence_2/training.py |  9 ++++++-
 requirements/requirements.txt                 |  2 +-
 3 files changed, 36 insertions(+), 2 deletions(-)

diff --git a/maestro/trainer/common/utils/metrics_tracing.py b/maestro/trainer/common/utils/metrics_tracing.py
index 95ea552..d99bbbe 100644
--- a/maestro/trainer/common/utils/metrics_tracing.py
+++ b/maestro/trainer/common/utils/metrics_tracing.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import json
+import os
 from collections import defaultdict
 from typing import Dict, Tuple, List
 
@@ -30,6 +32,28 @@ def get_metric_values(
             return self._metrics[metric]
         return [value[2] for value in self._metrics[metric]]
 
+    def as_json(
+        self,
+        output_dir: str = None,
+        filename: str = None
+    ) -> Dict[str, List[Dict[str, float]]]:
+        metrics_data = {}
+        for metric, values in self._metrics.items():
+            metrics_data[metric] = [
+                {'epoch': epoch, 'step': step, 'value': value}
+                for epoch, step, value
+                in values
+            ]
+
+        if output_dir and filename:
+            if not os.path.exists(output_dir):
+                os.makedirs(output_dir)
+            filepath = os.path.join(output_dir, filename)
+            with open(filepath, 'w') as file:
+                json.dump(metrics_data, file, indent=4)
+
+        return metrics_data
+
 
 def aggregate_by_epoch(metric_values: List[Tuple[int, int, float]]) -> Dict[int, float]:
     epoch_data = defaultdict(list)
@@ -48,6 +72,9 @@ def save_metric_plots(
     validation_tracker: MetricsTracker,
     output_dir: str
 ):
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
     training_metrics = training_tracker.describe_metrics()
     validation_metrics = validation_tracker.describe_metrics()
     all_metrics = set(training_metrics + validation_metrics)
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index e993736..aaa1687 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -125,8 +125,15 @@ def train(configuration: TrainingConfiguration) -> None:
     save_metric_plots(
         training_tracker=training_metrics_tracker,
         validation_tracker=validation_metrics_tracker,
-        output_dir=configuration.training_dir,
+        output_dir=os.path.join(configuration.training_dir, "metrics"),
     )
+    training_metrics_tracker.as_json(
+        output_dir=os.path.join(configuration.training_dir, "metrics"),
+        filename="training.json")
+    validation_metrics_tracker.as_json(
+        output_dir=os.path.join(configuration.training_dir, "metrics"),
+        filename="validation.json")
+
     for split_name in ["valid", "test"]:
         prepare_detection_training_summary(
             processor=processor,
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index d640ad7..a181a1a 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -1,4 +1,4 @@
-supervision~=0.22.0
+supervision~=0.24.0rc1
 requests>=2.31.0,<=2.32.3
 transformers~=4.44.2
 torch~=2.4.0

From 8219f5f5e8cfb675b7ce32961a21109c67027def Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Thu, 5 Sep 2024 15:01:53 +0200
Subject: [PATCH 33/69] initial refactor

---
 .../utils/{metrics_tracing.py => metrics.py}  |  35 +++++-
 maestro/trainer/models/florence_2/metrics.py  |  26 ++++-
 maestro/trainer/models/florence_2/training.py | 102 ++++++++++--------
 3 files changed, 113 insertions(+), 50 deletions(-)
 rename maestro/trainer/common/utils/{metrics_tracing.py => metrics.py} (80%)

diff --git a/maestro/trainer/common/utils/metrics_tracing.py b/maestro/trainer/common/utils/metrics.py
similarity index 80%
rename from maestro/trainer/common/utils/metrics_tracing.py
rename to maestro/trainer/common/utils/metrics.py
index d99bbbe..d5f4ae9 100644
--- a/maestro/trainer/common/utils/metrics_tracing.py
+++ b/maestro/trainer/common/utils/metrics.py
@@ -2,12 +2,45 @@
 
 import json
 import os
+from abc import ABC, abstractmethod
 from collections import defaultdict
-from typing import Dict, Tuple, List
+from typing import Dict, Tuple, List, Any
 
 import matplotlib.pyplot as plt
 
 
+class BaseMetric(ABC):
+    """
+    Abstract base class for custom metrics. Subclasses must implement
+    the 'describe' and 'compute' methods.
+    """
+
+    @abstractmethod
+    def describe(self) -> List[str]:
+        """
+        Describe the names of the metrics that this class will compute.
+
+        Returns:
+            List[str]: A list of metric names that will be computed.
+        """
+        pass
+
+    @abstractmethod
+    def compute(self, targets: List[Any], predictions: List[Any]) -> Dict[str, float]:
+        """
+        Compute the metric based on the targets and predictions.
+
+        Args:
+            targets (List[Any]): The ground truth.
+            predictions (List[Any]): The prediction result.
+
+        Returns:
+            Dict[str, float]: A dictionary of computed metrics with metric names as
+                keys and their values.
+        """
+        pass
+
+
 class MetricsTracker:
 
     @classmethod
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 3ed7aab..87f686b 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -1,23 +1,43 @@
 import os
 import random
 import re
-from typing import List, Tuple
+from typing import List, Dict
+from typing import Tuple
 
 import cv2
 import numpy as np
-import torch
 import supervision as sv
+import torch
+from supervision.metrics.mean_average_precision import MeanAveragePrecision
 from tqdm import tqdm
 from transformers import AutoProcessor, AutoModelForCausalLM
 
 from maestro.trainer.common.data_loaders.datasets import DetectionDataset
 from maestro.trainer.common.utils.file_system import save_json
+from maestro.trainer.common.utils.metrics import BaseMetric
 from maestro.trainer.models.florence_2.data_loading import prepare_detection_dataset
 
-
 DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 ]+ of [a-zA-Z0-9 ]+)<loc_\d+>"
 
 
+class MeanAveragePrecisionMetric(BaseMetric):
+
+    def describe(self) -> List[str]:
+        return ["map50:95", "map50", "map75"]
+
+    def compute(
+        self,
+        targets: List[sv.Detections],
+        predictions: List[sv.Detections]
+    ) -> Dict[str, float]:
+        result = MeanAveragePrecision().update(targets, predictions).compute()
+        return {
+            "map50:95": result.map50_95,
+            "map50": result.map50,
+            "map75": result.map75
+        }
+
+
 def prepare_detection_training_summary(
     processor: AutoProcessor,
     model: AutoModelForCausalLM,
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index aaa1687..3d27bb6 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -1,6 +1,6 @@
 import os
 import shutil
-from dataclasses import replace, dataclass
+from dataclasses import replace, dataclass, field
 from glob import glob
 from typing import Optional, Tuple, List, Literal, Union
 
@@ -15,17 +15,17 @@
 from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, \
     DEFAULT_CUDA_DEVICE
 from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
-from maestro.trainer.common.utils.metrics_tracing import MetricsTracker, \
-    save_metric_plots
+from maestro.trainer.common.utils.metrics import MetricsTracker, \
+    save_metric_plots, BaseMetric
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
-from maestro.trainer.models.florence_2.metrics import prepare_detection_training_summary
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
-
 DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
 DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
-DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
+DEVICE = torch.device("cpu") \
+    if not torch.cuda.is_available() \
+    else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
 
 
 @dataclass(frozen=True)
@@ -52,6 +52,7 @@ class TrainingConfiguration:
     training_dir: str = "./training/florence-2"
     max_checkpoints_to_keep: int = 3
     num_samples_to_visualise: int = 64
+    metrics: List[BaseMetric] = field(default_factory=list)
 
 
 def train(configuration: TrainingConfiguration) -> None:
@@ -80,8 +81,8 @@ def train(configuration: TrainingConfiguration) -> None:
         num_workers=configuration.loaders_workers,
         test_loaders_workers=configuration.test_loaders_workers,
     )
-    if test_loader is None:
-        test_loader = val_loader
+    # if test_loader is None:
+    #     test_loader = val_loader
     peft_model = prepare_peft_model(
         model=model,
         r=configuration.lora_r,
@@ -93,7 +94,11 @@ def train(configuration: TrainingConfiguration) -> None:
         revision=configuration.revision,
     )
     training_metrics_tracker = MetricsTracker.init(metrics=["loss"])
-    validation_metrics_tracker = MetricsTracker.init(metrics=["loss"])
+    metrics = ["loss"]
+    for metric in configuration.metrics:
+        metrics += metric.describe()
+    validation_metrics_tracker = MetricsTracker.init(metrics=metrics)
+
     run_training_loop(
         processor=processor,
         model=peft_model,
@@ -109,15 +114,15 @@ def train(configuration: TrainingConfiguration) -> None:
     processor, model = load_model(
         model_id_or_path=best_model_path,
     )
-    if test_loader is not None:
-        run_validation_epoch(
-            processor=processor,
-            model=model,
-            loader=test_loader,
-            epoch_number=None,
-            configuration=configuration,
-            title="Test",
-        )
+    # if test_loader is not None:
+    #     run_validation_epoch(
+    #         processor=processor,
+    #         model=model,
+    #         loader=test_loader,
+    #         epoch_number=None,
+    #         configuration=configuration,
+    #         title="Test",
+    #     )
     best_model_dir = os.path.join(configuration.training_dir, "best_model")
     print(f"Saving best model: {best_model_dir}")
     model.save_pretrained(best_model_dir)
@@ -134,16 +139,16 @@ def train(configuration: TrainingConfiguration) -> None:
         output_dir=os.path.join(configuration.training_dir, "metrics"),
         filename="validation.json")
 
-    for split_name in ["valid", "test"]:
-        prepare_detection_training_summary(
-            processor=processor,
-            model=model,
-            dataset_location=configuration.dataset_location,
-            split_name=split_name,
-            training_dir=configuration.training_dir,
-            num_samples_to_visualise=configuration.num_samples_to_visualise,
-            device=configuration.device,
-        )
+    # for split_name in ["valid", "test"]:
+    #     prepare_detection_training_summary(
+    #         processor=processor,
+    #         model=model,
+    #         dataset_location=configuration.dataset_location,
+    #         split_name=split_name,
+    #         training_dir=configuration.training_dir,
+    #         num_samples_to_visualise=configuration.num_samples_to_visualise,
+    #         device=configuration.device,
+    #     )
 
 
 def load_model(
@@ -274,19 +279,16 @@ def run_training_epoch(
         print(f"Average Training Loss: {avg_train_loss}")
     if val_loader is None or len(val_loader) == 0:
         return None
-    validation_loss = run_validation_epoch(
+
+    run_validation_epoch(
         processor=processor,
         model=model,
         loader=val_loader,
         epoch_number=epoch_number,
         configuration=configuration,
+        metrics_tracker=validation_metrics_tracker,
     )
-    validation_metrics_tracker.register(
-        metric="loss",
-        epoch=epoch_number,
-        step=1,
-        value=validation_loss,
-    )
+    validation_loss = validation_metrics_tracker.get_metric_values("loss")[-1][2]
     checkpoint_dir = os.path.join(configuration.training_dir, "checkpoints", str(epoch_number))
     should_save, to_remove = checkpoints_leaderboard.register_checkpoint(
         epoch=epoch_number,
@@ -307,27 +309,35 @@ def run_validation_epoch(
     processor: AutoProcessor,
     model: Union[PeftModel, AutoModelForCausalLM],
     loader: DataLoader,
-    epoch_number: Optional[int],
     configuration: TrainingConfiguration,
-    title: str = "Validation",
-) -> float:
+    metrics_tracker: MetricsTracker,
+    epoch_number: int
+) -> None:
     val_loss = 0.0
-    epoch_marker = ""
-    if epoch_number is not None:
-        epoch_marker = f"| Epoch {epoch_number}/{configuration.training_epochs}"
     with torch.no_grad():
-        for inputs, answers in tqdm(loader, desc=f"{title} {epoch_marker}"):
+        for inputs, targets in loader:
             input_ids = inputs["input_ids"]
             pixel_values = inputs["pixel_values"]
             labels = processor.tokenizer(
-                text=answers, return_tensors="pt", padding=True, return_token_type_ids=False
+                text=targets,
+                return_tensors="pt",
+                padding=True,
+                return_token_type_ids=False
             ).input_ids.to(configuration.device)
-            outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)
+            outputs = model(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                labels=labels
+            )
             loss = outputs.loss
             val_loss += loss.item()
         avg_val_loss = val_loss / len(loader)
-        print(f"Average {title} Loss: {avg_val_loss}")
-        return avg_val_loss
+        metrics_tracker.register(
+            metric="loss",
+            epoch=epoch_number,
+            step=1,
+            value=avg_val_loss,
+        )
 
 
 def save_model(

From 1b4a22496e83a97341fb8f2656ab4ef07d1a5d95 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Thu, 5 Sep 2024 15:06:58 +0200
Subject: [PATCH 34/69] small fix

---
 maestro/trainer/models/paligemma/training.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/models/paligemma/training.py b/maestro/trainer/models/paligemma/training.py
index f0e9737..9de0f0c 100644
--- a/maestro/trainer/models/paligemma/training.py
+++ b/maestro/trainer/models/paligemma/training.py
@@ -16,7 +16,7 @@
     HF_TOKEN_ENV,
 )
 from maestro.trainer.common.data_loaders.jsonl import JSONLDataset
-from maestro.trainer.common.utils.metrics_tracing import MetricsTracker
+from maestro.trainer.common.utils.metrics import MetricsTracker
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 
 DEFAULT_PALIGEMMA_MODEL_ID = "google/paligemma-3b-pt-224"

From ee1a6fabef26bb07465bf38abebcc211b974bee1 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Pawe=C5=82=20P=C4=99czek?= <pawel@roboflow.com>
Date: Thu, 5 Sep 2024 22:24:18 +0200
Subject: [PATCH 35/69] Add first scratch of implementation for maestro CLI

---
 maestro/cli/__init__.py                       |   0
 maestro/cli/env.py                            |   2 +
 maestro/cli/introspection.py                  |  36 +++++
 maestro/cli/main.py                           |  15 ++
 maestro/cli/utils.py                          |   2 +
 .../trainer/common/utils/metrics_tracing.py   |  61 +++----
 maestro/trainer/models/florence_2/entities.py |  41 +++++
 .../trainer/models/florence_2/entrypoint.py   | 152 ++++++++++++++++++
 maestro/trainer/models/florence_2/training.py |  52 ++----
 .../trainer/models/paligemma/entrypoint.py    |  13 ++
 requirements/requirements.txt                 |   3 +-
 setup.py                                      |   5 +
 12 files changed, 298 insertions(+), 84 deletions(-)
 create mode 100644 maestro/cli/__init__.py
 create mode 100644 maestro/cli/env.py
 create mode 100644 maestro/cli/introspection.py
 create mode 100644 maestro/cli/main.py
 create mode 100644 maestro/cli/utils.py
 create mode 100644 maestro/trainer/models/florence_2/entrypoint.py
 create mode 100644 maestro/trainer/models/paligemma/entrypoint.py

diff --git a/maestro/cli/__init__.py b/maestro/cli/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/maestro/cli/env.py b/maestro/cli/env.py
new file mode 100644
index 0000000..b95525e
--- /dev/null
+++ b/maestro/cli/env.py
@@ -0,0 +1,2 @@
+DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "DISABLE_RECIPE_IMPORTS_WARNINGS"
+DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "False"
diff --git a/maestro/cli/introspection.py b/maestro/cli/introspection.py
new file mode 100644
index 0000000..3a62ce3
--- /dev/null
+++ b/maestro/cli/introspection.py
@@ -0,0 +1,36 @@
+import os
+
+import typer
+
+from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
+from maestro.cli.utils import str2bool
+
+
+def find_training_recipes(app: typer.Typer) -> None:
+    try:
+        from maestro.trainer.models.florence_2.entrypoint import florence_2_app
+
+        app.add_typer(florence_2_app, name="florence2")
+    except Exception:
+        _warn_about_recipe_import_error(model_name="Florence 2")
+
+    try:
+        from maestro.trainer.models.paligemma.entrypoint import paligemma_app
+
+        app.add_typer(paligemma_app, name="paligemma")
+    except Exception:
+        _warn_about_recipe_import_error(model_name="PaliGemma")
+
+
+def _warn_about_recipe_import_error(model_name: str) -> None:
+    disable_warnings = str2bool(
+        os.getenv(
+            DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+            DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+        )
+    )
+    if disable_warnings:
+        return None
+    warning = typer.style("WARNING", fg=typer.colors.RED, bold=True)
+    message = "🚧 " + warning + f" cannot import recipe for {model_name}"
+    typer.echo(message)
diff --git a/maestro/cli/main.py b/maestro/cli/main.py
new file mode 100644
index 0000000..b600e3a
--- /dev/null
+++ b/maestro/cli/main.py
@@ -0,0 +1,15 @@
+import typer
+
+from maestro.cli.introspection import find_training_recipes
+
+app = typer.Typer()
+find_training_recipes(app=app)
+
+
+@app.command(help="Display information about maestro")
+def info():
+    typer.echo("Welcome to maestro CLI. Let's train some VLM! 🏋")
+
+
+if __name__ == "__main__":
+    app()
diff --git a/maestro/cli/utils.py b/maestro/cli/utils.py
new file mode 100644
index 0000000..0751fef
--- /dev/null
+++ b/maestro/cli/utils.py
@@ -0,0 +1,2 @@
+def str2bool(value: str) -> bool:
+    return value.lower() in {"y", "t", "yes", "true"}
diff --git a/maestro/trainer/common/utils/metrics_tracing.py b/maestro/trainer/common/utils/metrics_tracing.py
index d99bbbe..c7b011e 100644
--- a/maestro/trainer/common/utils/metrics_tracing.py
+++ b/maestro/trainer/common/utils/metrics_tracing.py
@@ -3,7 +3,7 @@
 import json
 import os
 from collections import defaultdict
-from typing import Dict, Tuple, List
+from typing import Dict, Tuple, List, Optional
 
 import matplotlib.pyplot as plt
 
@@ -33,23 +33,17 @@ def get_metric_values(
         return [value[2] for value in self._metrics[metric]]
 
     def as_json(
-        self,
-        output_dir: str = None,
-        filename: str = None
+        self, output_dir: Optional[str] = None, filename: Optional[str] = None
     ) -> Dict[str, List[Dict[str, float]]]:
         metrics_data = {}
         for metric, values in self._metrics.items():
-            metrics_data[metric] = [
-                {'epoch': epoch, 'step': step, 'value': value}
-                for epoch, step, value
-                in values
-            ]
+            metrics_data[metric] = [{"epoch": epoch, "step": step, "value": value} for epoch, step, value in values]
 
         if output_dir and filename:
             if not os.path.exists(output_dir):
                 os.makedirs(output_dir)
             filepath = os.path.join(output_dir, filename)
-            with open(filepath, 'w') as file:
+            with open(filepath, "w") as file:
                 json.dump(metrics_data, file, indent=4)
 
         return metrics_data
@@ -59,19 +53,11 @@ def aggregate_by_epoch(metric_values: List[Tuple[int, int, float]]) -> Dict[int,
     epoch_data = defaultdict(list)
     for epoch, step, value in metric_values:
         epoch_data[epoch].append(value)
-    avg_per_epoch = {
-        epoch: sum(values) / len(values)
-        for epoch, values
-        in epoch_data.items()
-    }
+    avg_per_epoch = {epoch: sum(values) / len(values) for epoch, values in epoch_data.items()}
     return avg_per_epoch
 
 
-def save_metric_plots(
-    training_tracker: MetricsTracker,
-    validation_tracker: MetricsTracker,
-    output_dir: str
-):
+def save_metric_plots(training_tracker: MetricsTracker, validation_tracker: MetricsTracker, output_dir: str):
     if not os.path.exists(output_dir):
         os.makedirs(output_dir)
 
@@ -83,43 +69,32 @@ def save_metric_plots(
         plt.figure(figsize=(8, 6))
 
         if metric in training_metrics:
-            training_values = training_tracker.get_metric_values(
-                metric=metric, with_index=True)
+            training_values = training_tracker.get_metric_values(metric=metric, with_index=True)
             training_avg_values = aggregate_by_epoch(training_values)
             training_epochs = sorted(training_avg_values.keys())
             training_vals = [training_avg_values[epoch] for epoch in training_epochs]
             plt.plot(
-                training_epochs,
-                training_vals,
-                label=f'Training {metric}',
-                marker='o',
-                linestyle='-',
-                color='blue'
+                training_epochs, training_vals, label=f"Training {metric}", marker="o", linestyle="-", color="blue"
             )
 
         if metric in validation_metrics:
-            validation_values = validation_tracker.get_metric_values(
-                metric=metric, with_index=True)
+            validation_values = validation_tracker.get_metric_values(metric=metric, with_index=True)
             validation_avg_values = aggregate_by_epoch(validation_values)
             validation_epochs = sorted(validation_avg_values.keys())
-            validation_vals = [
-                validation_avg_values[epoch]
-                for epoch
-                in validation_epochs
-            ]
+            validation_vals = [validation_avg_values[epoch] for epoch in validation_epochs]
             plt.plot(
                 validation_epochs,
                 validation_vals,
-                label=f'Validation {metric}',
-                marker='o',
-                linestyle='--',
-                color='orange'
+                label=f"Validation {metric}",
+                marker="o",
+                linestyle="--",
+                color="orange",
             )
 
-        plt.title(f'{metric.capitalize()} over Epochs')
-        plt.xlabel('Epoch')
-        plt.ylabel(f'{metric.capitalize()} Value')
+        plt.title(f"{metric.capitalize()} over Epochs")
+        plt.xlabel("Epoch")
+        plt.ylabel(f"{metric.capitalize()} Value")
         plt.legend()
         plt.grid(True)
-        plt.savefig(f'{output_dir}/{metric}_plot.png')
+        plt.savefig(f"{output_dir}/{metric}_plot.png")
         plt.close()
diff --git a/maestro/trainer/models/florence_2/entities.py b/maestro/trainer/models/florence_2/entities.py
index e69de29..17f4019 100644
--- a/maestro/trainer/models/florence_2/entities.py
+++ b/maestro/trainer/models/florence_2/entities.py
@@ -0,0 +1,41 @@
+import os
+from dataclasses import dataclass
+from typing import Optional, Literal, Union
+
+import torch
+
+from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
+
+
+LoraInitLiteral = Literal["gaussian", "olora", "pissa", "pissa_niter_[number of iters]", "loftq"]
+
+
+DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
+DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
+DEVICE = os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
+
+
+@dataclass(frozen=True)
+class TrainingConfiguration:
+    dataset_location: str
+    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION
+    device: torch.device = torch.device(DEVICE)
+    transformers_cache_dir: Optional[str] = None
+    training_epochs: int = 10
+    optimiser: Literal["SGD", "adamw", "adam"] = "adamw"
+    learning_rate: float = 1e-5
+    lr_scheduler: Literal["linear", "cosine", "polynomial"] = "linear"
+    train_batch_size: int = 4
+    test_batch_size: Optional[int] = None
+    loaders_workers: int = 0
+    test_loaders_workers: Optional[int] = None
+    lora_r: int = 8
+    lora_alpha: int = 8
+    lora_dropout: float = 0.05
+    bias: Literal["none", "all", "lora_only"] = "none"
+    use_rslora: bool = True
+    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
+    training_dir: str = "./training/florence-2"
+    max_checkpoints_to_keep: int = 3
+    num_samples_to_visualise: int = 64
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
new file mode 100644
index 0000000..09c2d87
--- /dev/null
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -0,0 +1,152 @@
+import dataclasses
+from typing import Optional, Annotated
+
+import torch
+import typer
+import rich
+
+from maestro.trainer.models.florence_2.entities import (
+    TrainingConfiguration,
+    DEFAULT_FLORENCE2_MODEL_ID,
+    DEFAULT_FLORENCE2_MODEL_REVISION,
+    DEVICE,
+)
+
+from maestro.trainer.models.florence_2.training import train as train_fun
+
+florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
+
+
+@florence_2_app.command(
+    help="Train Florence 2 model", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
+)
+def train(
+    dataset_location: Annotated[
+        str,
+        typer.Option("--dataset_location", help="Path to directory with dataset"),
+    ],
+    model_id_or_path: Annotated[
+        str,
+        typer.Option("--model_id_or_path", help="Model to be used or path to your checkpoint"),
+    ] = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: Annotated[
+        str,
+        typer.Option("--revision", help="Revision of Florence2 HF repository"),
+    ] = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: Annotated[
+        str,
+        typer.Option("--device", help="CUDA device ID to be used (in format: 'cuda:0')"),
+    ] = DEVICE,
+    transformers_cache_dir: Annotated[
+        Optional[str],
+        typer.Option("--transformers_cache_dir", help="Cache dir for HF weights"),
+    ] = None,
+    training_epochs: Annotated[
+        int,
+        typer.Option("--training_epochs", help="Number of training epochs"),
+    ] = 10,
+    optimiser: Annotated[
+        str,
+        typer.Option("--optimiser", help="Optimiser to be used"),
+    ] = "adamw",
+    learning_rate: Annotated[
+        float,
+        typer.Option("--learning_rate", help="Learning rate"),
+    ] = 1e-5,
+    lr_scheduler: Annotated[
+        str,
+        typer.Option("--lr_scheduler", help="LR scheduler"),
+    ] = "linear",
+    train_batch_size: Annotated[
+        int,
+        typer.Option("--train_batch_size", help="Batch size for training"),
+    ] = 4,
+    test_batch_size: Annotated[
+        Optional[int],
+        typer.Option(
+            "--train_batch_size", help="Batch size for validation and test. If not given - train will be used."
+        ),
+    ] = None,
+    loaders_workers: Annotated[
+        int,
+        typer.Option("--loaders_workers", help="Number of loaders workers. 0 = # of CPU"),
+    ] = 0,
+    test_loaders_workers: Annotated[
+        Optional[int],
+        typer.Option(
+            "--test_loaders_workers",
+            help="Number of workers for test and val loaders. If not given - train will be used.",
+        ),
+    ] = None,
+    lora_r: Annotated[
+        int,
+        typer.Option("--lora_r", help="Value of Lora R"),
+    ] = 8,
+    lora_alpha: Annotated[
+        int,
+        typer.Option("--lora_alpha", help="Value of Lora Alpha"),
+    ] = 8,
+    lora_dropout: Annotated[
+        float,
+        typer.Option("--lora_dropout", help="Value of Lora Dropout"),
+    ] = 0.05,
+    bias: Annotated[
+        str,
+        typer.Option("--bias", help="Value of Lora Bias"),
+    ] = "none",
+    use_rslora: Annotated[
+        bool,
+        typer.Option(
+            "--use_rslora/--no_use_rslora",
+            help="Boolean flag to decide if rslora to be used",
+        ),
+    ] = True,
+    init_lora_weights: Annotated[
+        str,
+        typer.Option("--init_lora_weights", help="Lora weights initialisation"),
+    ] = "gaussian",
+    training_dir: Annotated[
+        str,
+        typer.Option("--training_dir", help="Path to directory where training outputs should be preserved"),
+    ] = "./training/florence-2",
+    max_checkpoints_to_keep: Annotated[
+        int,
+        typer.Option("--max_checkpoints_to_keep", help="Max checkpoints to keep"),
+    ] = 3,
+    num_samples_to_visualise: Annotated[
+        int,
+        typer.Option("--num_samples_to_visualise", help="Number of samples to visualise"),
+    ] = 64,
+) -> None:
+    configuration = TrainingConfiguration(
+        dataset_location=dataset_location,
+        model_id_or_path=model_id_or_path,
+        revision=revision,
+        device=torch.device(device),
+        transformers_cache_dir=transformers_cache_dir,
+        training_epochs=training_epochs,
+        optimiser=optimiser,  # type: ignore
+        learning_rate=learning_rate,
+        lr_scheduler=lr_scheduler,  # type: ignore
+        train_batch_size=train_batch_size,
+        test_batch_size=test_batch_size,
+        loaders_workers=loaders_workers,
+        test_loaders_workers=test_loaders_workers,
+        lora_r=lora_r,
+        lora_alpha=lora_alpha,
+        lora_dropout=lora_dropout,
+        bias=bias,  # type: ignore
+        use_rslora=use_rslora,
+        init_lora_weights=init_lora_weights,  # type: ignore
+        training_dir=training_dir,
+        max_checkpoints_to_keep=max_checkpoints_to_keep,
+        num_samples_to_visualise=num_samples_to_visualise,
+    )
+    typer.echo(typer.style("Training configuration", fg=typer.colors.BRIGHT_GREEN, bold=True))
+    rich.print(dataclasses.asdict(configuration))
+    train_fun(configuration=configuration)
+
+
+@florence_2_app.command(help="Evaluate Florence 2 model")
+def evaluate() -> None:
+    pass
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index aaa1687..7e7ae56 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -1,6 +1,6 @@
 import os
 import shutil
-from dataclasses import replace, dataclass
+from dataclasses import replace
 from glob import glob
 from typing import Optional, Tuple, List, Literal, Union
 
@@ -12,48 +12,20 @@
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
-from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, \
-    DEFAULT_CUDA_DEVICE
 from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
-from maestro.trainer.common.utils.metrics_tracing import MetricsTracker, \
-    save_metric_plots
+from maestro.trainer.common.utils.metrics_tracing import MetricsTracker, save_metric_plots
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
+from maestro.trainer.models.florence_2.entities import (
+    DEFAULT_FLORENCE2_MODEL_ID,
+    DEFAULT_FLORENCE2_MODEL_REVISION,
+    DEVICE,
+    TrainingConfiguration,
+)
 from maestro.trainer.models.florence_2.metrics import prepare_detection_training_summary
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
 
-DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
-DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
-DEVICE = torch.device("cpu") if not torch.cuda.is_available() else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
-
-
-@dataclass(frozen=True)
-class TrainingConfiguration:
-    dataset_location: str
-    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID
-    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION
-    device: torch.device = DEVICE
-    transformers_cache_dir: Optional[str] = None
-    training_epochs: int = 10
-    optimiser: Literal["SGD", "adamw", "adam"] = "adamw"
-    learning_rate: float = 1e-5
-    lr_scheduler: Literal["linear", "cosine", "polynomial"] = "linear"
-    train_batch_size: int = 4
-    test_batch_size: Optional[int] = None
-    loaders_workers: int = 0
-    test_loaders_workers: Optional[int] = None
-    lora_r: int = 8
-    lora_alpha: int = 8
-    lora_dropout: float = 0.05
-    bias: Literal["none", "all", "lora_only"] = "none"
-    use_rslora: bool = True
-    init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
-    training_dir: str = "./training/florence-2"
-    max_checkpoints_to_keep: int = 3
-    num_samples_to_visualise: int = 64
-
-
 def train(configuration: TrainingConfiguration) -> None:
     make_it_reproducible(avoid_non_deterministic_algorithms=False)
     training_run_dir = _establish_training_run_dir(
@@ -128,11 +100,11 @@ def train(configuration: TrainingConfiguration) -> None:
         output_dir=os.path.join(configuration.training_dir, "metrics"),
     )
     training_metrics_tracker.as_json(
-        output_dir=os.path.join(configuration.training_dir, "metrics"),
-        filename="training.json")
+        output_dir=os.path.join(configuration.training_dir, "metrics"), filename="training.json"
+    )
     validation_metrics_tracker.as_json(
-        output_dir=os.path.join(configuration.training_dir, "metrics"),
-        filename="validation.json")
+        output_dir=os.path.join(configuration.training_dir, "metrics"), filename="validation.json"
+    )
 
     for split_name in ["valid", "test"]:
         prepare_detection_training_summary(
diff --git a/maestro/trainer/models/paligemma/entrypoint.py b/maestro/trainer/models/paligemma/entrypoint.py
new file mode 100644
index 0000000..6ef6dc6
--- /dev/null
+++ b/maestro/trainer/models/paligemma/entrypoint.py
@@ -0,0 +1,13 @@
+import typer
+
+paligemma_app = typer.Typer(help="Fine-tune and evaluate PaliGemma model")
+
+
+@paligemma_app.command(help="Train PaliGemma model")
+def train() -> None:
+    typer.echo("🚧 Just a placeholder - to be implemented 🚧")
+
+
+@paligemma_app.command(help="Evaluate PaliGemma model")
+def evaluate() -> None:
+    typer.echo("🚧 Just a placeholder - to be implemented 🚧")
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
index a181a1a..e06e303 100644
--- a/requirements/requirements.txt
+++ b/requirements/requirements.txt
@@ -7,4 +7,5 @@ sentencepiece~=0.2.0
 peft~=0.12.0
 flash-attn~=2.6.3 # does not work on mac
 einops~=0.8.0
-timm~=1.0.9
\ No newline at end of file
+timm~=1.0.9
+typer~=0.12.5
diff --git a/setup.py b/setup.py
index 9e16b7b..293f4e9 100644
--- a/setup.py
+++ b/setup.py
@@ -42,6 +42,11 @@ def read_requirements(path: Union[str, List[str]]) -> List[str]:
         "dev": read_requirements("requirements/requirements.test.txt"),
         "docs": read_requirements("requirements/requirements.docs.txt"),
     },
+    entry_points={
+        "console_scripts": [
+            "maestro=maestro.cli.main:app",
+        ],
+    },
     classifiers=[
         "Development Status :: 3 - Alpha",
         "Intended Audience :: Developers",

From 9e52912cd9e569b94238d86109a9b5c22dff4b42 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 14:15:16 +0200
Subject: [PATCH 36/69] up

---
 maestro/trainer/models/florence_2/training.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 3d27bb6..cf3c123 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -338,6 +338,7 @@ def run_validation_epoch(
             step=1,
             value=avg_val_loss,
         )
+        print(f"Average Validation Loss: {avg_val_loss}")
 
 
 def save_model(

From 4c3fbd08e25c649a8df8f870ebfad884e8d518cf Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 15:33:26 +0200
Subject: [PATCH 37/69] test mAP metic

---
 maestro/trainer/models/florence_2/metrics.py  |  3 +--
 maestro/trainer/models/florence_2/training.py | 24 +++++++++++++++++++
 2 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 87f686b..76de43e 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -92,14 +92,13 @@ def get_ground_truths_and_predictions(
     dataset: DetectionDataset,
     processor: AutoProcessor,
     model: AutoModelForCausalLM,
-    split_name: str,
     device: torch.device,
 ) -> Tuple[List[sv.Detections], List[sv.Detections], List[str]]:
     classes = extract_classes(dataset=dataset)
     targets = []
     predictions = []
     post_processed_text_outputs = []
-    for idx in tqdm(list(range(len(dataset))), desc=f"Generating {split_name} predictions..."):
+    for idx in tqdm(list(range(len(dataset))), desc="Generating predictions..."):
         image, data = dataset.dataset[idx]
         prefix = data["prefix"]
         suffix = data["suffix"]
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index cf3c123..a79977d 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -19,6 +19,7 @@
     save_metric_plots, BaseMetric
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
+from maestro.trainer.models.florence_2.metrics import MeanAveragePrecisionMetric, get_ground_truths_and_predictions
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
 DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
@@ -340,6 +341,29 @@ def run_validation_epoch(
         )
         print(f"Average Validation Loss: {avg_val_loss}")
 
+        # TODO: standardize the calculation of metrics input to run inference only once
+
+        for metric in configuration.metrics:
+            if isinstance(metric, MeanAveragePrecisionMetric):
+                targets, predictions, _ = get_ground_truths_and_predictions(
+                    dataset=loader.dataset,
+                    processor=processor,
+                    model=model,
+                    device=configuration.device,
+                )
+                map_result = metric.compute(targets=targets, predictions=predictions)
+                for map_key, map_value in map_result.items():
+                    metrics_tracker.register(
+                        metric=map_key,
+                        epoch=epoch_number,
+                        step=1,
+                        value=map_value,
+                    )
+                    print(f"Validation {map_key}: {map_value:.4f}")
+            else:
+                # Handle other metric types
+                pass
+
 
 def save_model(
     target_dir: str,

From ad6e6c9c1edaf23e0a585a3203bee63956b31963 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 17:34:13 +0200
Subject: [PATCH 38/69] mAP refactor

---
 maestro/trainer/models/florence_2/metrics.py  | 219 +++++++++++-------
 maestro/trainer/models/florence_2/training.py |  62 +++--
 2 files changed, 177 insertions(+), 104 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 76de43e..563734b 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -11,6 +11,7 @@
 from supervision.metrics.mean_average_precision import MeanAveragePrecision
 from tqdm import tqdm
 from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
 
 from maestro.trainer.common.data_loaders.datasets import DetectionDataset
 from maestro.trainer.common.utils.file_system import save_json
@@ -38,66 +39,122 @@ def compute(
         }
 
 
-def prepare_detection_training_summary(
-    processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-    dataset_location: str,
-    split_name: str,
-    training_dir: str,
-    num_samples_to_visualise: int,
-    device: torch.device,
-) -> None:
-    dataset = prepare_detection_dataset(
-        dataset_location=dataset_location,
-        split_name=split_name,
-    )
-    if dataset is None:
-        return None
-    targets, predictions, post_processed_text_outputs = get_ground_truths_and_predictions(
-        dataset=dataset,
-        processor=processor,
-        model=model,
-        split_name=split_name,
-        device=device,
-    )
-    mean_average_precision = sv.MeanAveragePrecision.from_detections(
-        predictions=predictions,
-        targets=targets,
-    )
-    print(f"{split_name} | map50_95: {mean_average_precision.map50_95:.2f}")
-    print(f"{split_name} | map50: {mean_average_precision.map50:.2f}")
-    print(f"{split_name} | map75: {mean_average_precision.map75:.2f}")
-    dump_metrics(
-        training_dir=training_dir,
-        split_name=split_name,
-        metrics=mean_average_precision,
-    )
-    dump_post_processed_outputs(
-        dataset=dataset,
-        post_processed_text_outputs=post_processed_text_outputs,
-        training_dir=training_dir,
-        split_name=split_name,
-    )
-    dump_visualised_samples(
-        dataset=dataset,
-        targets=targets,
-        predictions=predictions,
-        num_samples_to_visualise=num_samples_to_visualise,
-        training_dir=training_dir,
-        split_name=split_name,
-    )
+# def prepare_detection_training_summary(
+#     processor: AutoProcessor,
+#     model: AutoModelForCausalLM,
+#     dataset_location: str,
+#     split_name: str,
+#     training_dir: str,
+#     num_samples_to_visualise: int,
+#     device: torch.device,
+# ) -> None:
+#     dataset = prepare_detection_dataset(
+#         dataset_location=dataset_location,
+#         split_name=split_name,
+#     )
+#     if dataset is None:
+#         return None
+#     targets, predictions, post_processed_text_outputs = get_ground_truths_and_predictions(
+#         dataset=dataset,
+#         processor=processor,
+#         model=model,
+#         split_name=split_name,
+#         device=device,
+#     )
+#     mean_average_precision = sv.MeanAveragePrecision.from_detections(
+#         predictions=predictions,
+#         targets=targets,
+#     )
+#     print(f"{split_name} | map50_95: {mean_average_precision.map50_95:.2f}")
+#     print(f"{split_name} | map50: {mean_average_precision.map50:.2f}")
+#     print(f"{split_name} | map75: {mean_average_precision.map75:.2f}")
+#     dump_metrics(
+#         training_dir=training_dir,
+#         split_name=split_name,
+#         metrics=mean_average_precision,
+#     )
+#     dump_post_processed_outputs(
+#         dataset=dataset,
+#         post_processed_text_outputs=post_processed_text_outputs,
+#         training_dir=training_dir,
+#         split_name=split_name,
+#     )
+#     dump_visualised_samples(
+#         dataset=dataset,
+#         targets=targets,
+#         predictions=predictions,
+#         num_samples_to_visualise=num_samples_to_visualise,
+#         training_dir=training_dir,
+#         split_name=split_name,
+#     )
+
+
+def postprocess_florence2_output_for_mean_average_precision(
+    expected_responses: List[str],
+    generated_texts: List[str],
+    images: List[Image.Image],
+    classes: List[str],
+    processor: AutoProcessor
+) -> Tuple[List[sv.Detections], List[sv.Detections]]:
+    """
+    Postprocess Florence-2 model output for mean average precision calculation.
+
+    Args:
+        expected_responses (List[str]): List of expected responses (ground truth).
+        generated_texts (List[str]): List of generated texts from the model.
+        images (List[Image.Image]): List of input images.
+        classes (List[str]): List of unique class names.
+        processor (AutoProcessor): The processor used for text generation.
 
+    Returns:
+        Tuple[List[sv.Detections], List[sv.Detections]]: A tuple containing two lists of Detections objects,
+        representing the targets (ground truth) and predictions respectively.
+    """
+    targets = []
+    predictions = []
+    
+    for image, suffix, generated_text in zip(images, expected_responses, generated_texts):
+        # Postprocess prediction for mean average precision calculation
+        prediction = processor.post_process_generation(generated_text, task="<OD>", image_size=image.size)
+        prediction = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, prediction, resolution_wh=image.size)
+        prediction = prediction[np.isin(prediction["class_name"], classes)]
+        prediction.class_id = np.array([classes.index(class_name) for class_name in prediction["class_name"]])
+        prediction.confidence = np.ones(len(prediction))  # Set confidence for mean average precision calculation
+        
+        # Postprocess target for mean average precision calculation
+        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
+        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
+        target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
+        
+        targets.append(target)
+        predictions.append(prediction)
+    
+    return targets, predictions
 
-def get_ground_truths_and_predictions(
+def run_predictions(
     dataset: DetectionDataset,
     processor: AutoProcessor,
     model: AutoModelForCausalLM,
     device: torch.device,
-) -> Tuple[List[sv.Detections], List[sv.Detections], List[str]]:
-    classes = extract_classes(dataset=dataset)
-    targets = []
-    predictions = []
-    post_processed_text_outputs = []
+) -> Tuple[List[str], List[str], List[str], List[Image.Image]]:
+    """
+    Run predictions on the given dataset using the provided model and processor.
+
+    Args:
+        dataset (DetectionDataset): The dataset to run predictions on.
+        processor (AutoProcessor): The processor used for text generation.
+        model (AutoModelForCausalLM): The model used for text generation.
+        device (torch.device): The device to run the model on.
+
+    Returns:
+        Tuple[List[str], List[str], List[str], List[Image.Image]]: A tuple containing lists of prompts,
+        expected responses, generated texts, and input images.
+    """
+    prompts = []
+    expected_responses = []
+    generated_texts = []
+    images = []
+    
     for idx in tqdm(list(range(len(dataset))), desc="Generating predictions..."):
         image, data = dataset.dataset[idx]
         prefix = data["prefix"]
@@ -108,21 +165,25 @@ def get_ground_truths_and_predictions(
             input_ids=inputs["input_ids"], pixel_values=inputs["pixel_values"], max_new_tokens=1024, num_beams=3
         )
         generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-        prediction = processor.post_process_generation(generated_text, task="<OD>", image_size=image.size)
-        post_processed_text_outputs.append(prediction)
-        prediction = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, prediction, resolution_wh=image.size)
-        prediction = prediction[np.isin(prediction["class_name"], classes)]
-        prediction.class_id = np.array([class_name.index(class_name) for class_name in prediction["class_name"]])
-        prediction.confidence = np.ones(len(prediction))
-        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
-        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
-        target.class_id = np.array([class_name.index(class_name) for class_name in target["class_name"]])
-        targets.append(target)
-        predictions.append(prediction)
-    return targets, predictions, post_processed_text_outputs
+        
+        prompts.append(prefix)
+        expected_responses.append(suffix)
+        generated_texts.append(generated_text)
+        images.append(image)
+    
+    return prompts, expected_responses, generated_texts, images
+
 
+def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[str]:
+    """
+    Extract unique class names from the detection dataset.
 
-def extract_classes(dataset: DetectionDataset) -> List[str]:
+    Args:
+        dataset (DetectionDataset): The dataset to extract class names from.
+
+    Returns:
+        List[str]: A sorted list of unique class names found in the dataset.
+    """
     class_set = set()
     for i in range(len(dataset.dataset)):
         image, data = dataset.dataset[i]
@@ -132,18 +193,18 @@ def extract_classes(dataset: DetectionDataset) -> List[str]:
     return sorted(class_set)
 
 
-def dump_metrics(
-    training_dir: str,
-    split_name: str,
-    metrics: sv.MeanAveragePrecision,
-) -> None:
-    target_path = os.path.join(training_dir, "metrics", split_name, f"metrics_{split_name}.json")
-    content = {
-        "map50_95": metrics.map50_95,
-        "map50": metrics.map50,
-        "map75": metrics.map75,
-    }
-    save_json(path=target_path, content=content)
+# def dump_metrics(
+#     training_dir: str,
+#     split_name: str,
+#     metrics: sv.MeanAveragePrecision,
+# ) -> None:
+#     target_path = os.path.join(training_dir, "metrics", split_name, f"metrics_{split_name}.json")
+#     content = {
+#         "map50_95": metrics.map50_95,
+#         "map50": metrics.map50,
+#         "map75": metrics.map75,
+#     }
+#     save_json(path=target_path, content=content)
 
 
 def dump_post_processed_outputs(
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index a79977d..4c3ce27 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -1,25 +1,29 @@
 import os
 import shutil
-from dataclasses import replace, dataclass, field
+from dataclasses import dataclass, field, replace
 from glob import glob
-from typing import Optional, Tuple, List, Literal, Union
+from typing import List, Literal, Optional, Tuple, Union
 
 import torch
-from peft import PeftModel, LoraConfig, get_peft_model
-from torch.optim import Optimizer, AdamW, Adam, SGD
+from peft import LoraConfig, PeftModel, get_peft_model
+from torch.optim import Adam, AdamW, Optimizer, SGD
 from torch.optim.lr_scheduler import LRScheduler
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
-from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, \
-    DEFAULT_CUDA_DEVICE
+from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
 from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
-from maestro.trainer.common.utils.metrics import MetricsTracker, \
-    save_metric_plots, BaseMetric
+from maestro.trainer.common.utils.metrics import BaseMetric, MetricsTracker, save_metric_plots
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
-from maestro.trainer.models.florence_2.metrics import MeanAveragePrecisionMetric, get_ground_truths_and_predictions
+from maestro.trainer.models.florence_2.metrics import (
+    MeanAveragePrecisionMetric,
+    extract_unique_detection_dataset_classes,
+    get_ground_truths_and_predictions,
+    postprocess_florence2_output_for_mean_average_precision,
+    run_predictions,
+)
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
 DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
@@ -339,30 +343,38 @@ def run_validation_epoch(
             step=1,
             value=avg_val_loss,
         )
-        print(f"Average Validation Loss: {avg_val_loss}")
-
-        # TODO: standardize the calculation of metrics input to run inference only once
 
+        # Run inference once for all metrics
+        prompts, expected_responses, generated_texts, images = run_predictions(
+            dataset=loader.dataset,
+            processor=processor,
+            model=model,
+            device=configuration.device,
+        )
+        
+        metrics_results = {"loss": avg_val_loss}
+        
         for metric in configuration.metrics:
             if isinstance(metric, MeanAveragePrecisionMetric):
-                targets, predictions, _ = get_ground_truths_and_predictions(
-                    dataset=loader.dataset,
-                    processor=processor,
-                    model=model,
-                    device=configuration.device,
+                classes = extract_unique_detection_dataset_classes(loader.dataset)
+                targets, predictions = postprocess_florence2_output_for_mean_average_precision(
+                    expected_responses=expected_responses,
+                    generated_texts=generated_texts,
+                    images=images,
+                    classes=classes,
+                    processor=processor
                 )
-                map_result = metric.compute(targets=targets, predictions=predictions)
-                for map_key, map_value in map_result.items():
+                result = metric.compute(targets=targets, predictions=predictions)
+                for key, value in result.items():
                     metrics_tracker.register(
-                        metric=map_key,
+                        metric=key,
                         epoch=epoch_number,
                         step=1,
-                        value=map_value,
+                        value=value,
                     )
-                    print(f"Validation {map_key}: {map_value:.4f}")
-            else:
-                # Handle other metric types
-                pass
+                    metrics_results[key] = value
+        
+        print("Validation Metrics:", ", ".join([f"{k}: {v:.4f}" for k, v in metrics_results.items()]))
 
 
 def save_model(

From 3580ec8aa78f2a17541a6f0e63ae45f2c6be5e81 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 17:42:53 +0200
Subject: [PATCH 39/69] small fix

---
 maestro/trainer/models/florence_2/training.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 4c3ce27..742f2a2 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -20,7 +20,6 @@
 from maestro.trainer.models.florence_2.metrics import (
     MeanAveragePrecisionMetric,
     extract_unique_detection_dataset_classes,
-    get_ground_truths_and_predictions,
     postprocess_florence2_output_for_mean_average_precision,
     run_predictions,
 )

From 4543159c08df27213453b42ff6304dcdf51c0a3c Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 17:55:58 +0200
Subject: [PATCH 40/69] debug

---
 maestro/trainer/common/utils/metrics.py       | 21 ++++++++
 maestro/trainer/models/florence_2/entities.py |  0
 maestro/trainer/models/florence_2/metrics.py  | 49 ++++---------------
 3 files changed, 31 insertions(+), 39 deletions(-)
 delete mode 100644 maestro/trainer/models/florence_2/entities.py

diff --git a/maestro/trainer/common/utils/metrics.py b/maestro/trainer/common/utils/metrics.py
index d5f4ae9..dcc034b 100644
--- a/maestro/trainer/common/utils/metrics.py
+++ b/maestro/trainer/common/utils/metrics.py
@@ -89,6 +89,16 @@ def as_json(
 
 
 def aggregate_by_epoch(metric_values: List[Tuple[int, int, float]]) -> Dict[int, float]:
+    """
+    Aggregates metric values by epoch, calculating the average for each epoch.
+
+    Args:
+        metric_values (List[Tuple[int, int, float]]): A list of tuples containing
+            (epoch, step, value) for each metric measurement.
+
+    Returns:
+        Dict[int, float]: A dictionary with epochs as keys and average metric values as values.
+    """
     epoch_data = defaultdict(list)
     for epoch, step, value in metric_values:
         epoch_data[epoch].append(value)
@@ -105,6 +115,17 @@ def save_metric_plots(
     validation_tracker: MetricsTracker,
     output_dir: str
 ):
+    """
+    Saves plots of training and validation metrics over epochs.
+
+    Args:
+        training_tracker (MetricsTracker): Tracker containing training metrics.
+        validation_tracker (MetricsTracker): Tracker containing validation metrics.
+        output_dir (str): Directory to save the generated plots.
+
+    Returns:
+        None
+    """
     if not os.path.exists(output_dir):
         os.makedirs(output_dir)
 
diff --git a/maestro/trainer/models/florence_2/entities.py b/maestro/trainer/models/florence_2/entities.py
deleted file mode 100644
index e69de29..0000000
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 563734b..73b70c3 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -96,20 +96,6 @@ def postprocess_florence2_output_for_mean_average_precision(
     classes: List[str],
     processor: AutoProcessor
 ) -> Tuple[List[sv.Detections], List[sv.Detections]]:
-    """
-    Postprocess Florence-2 model output for mean average precision calculation.
-
-    Args:
-        expected_responses (List[str]): List of expected responses (ground truth).
-        generated_texts (List[str]): List of generated texts from the model.
-        images (List[Image.Image]): List of input images.
-        classes (List[str]): List of unique class names.
-        processor (AutoProcessor): The processor used for text generation.
-
-    Returns:
-        Tuple[List[sv.Detections], List[sv.Detections]]: A tuple containing two lists of Detections objects,
-        representing the targets (ground truth) and predictions respectively.
-    """
     targets = []
     predictions = []
     
@@ -122,9 +108,16 @@ def postprocess_florence2_output_for_mean_average_precision(
         prediction.confidence = np.ones(len(prediction))  # Set confidence for mean average precision calculation
         
         # Postprocess target for mean average precision calculation
-        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
-        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
-        target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
+        try:
+            target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
+            target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
+            target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
+        except Exception as e:
+            print(f"Exception occurred: {e}")
+            print(f"Classes: {classes}")
+            print(f"Suffix: {suffix}")
+            print(f"Target: {target}")
+            raise
         
         targets.append(target)
         predictions.append(prediction)
@@ -137,19 +130,6 @@ def run_predictions(
     model: AutoModelForCausalLM,
     device: torch.device,
 ) -> Tuple[List[str], List[str], List[str], List[Image.Image]]:
-    """
-    Run predictions on the given dataset using the provided model and processor.
-
-    Args:
-        dataset (DetectionDataset): The dataset to run predictions on.
-        processor (AutoProcessor): The processor used for text generation.
-        model (AutoModelForCausalLM): The model used for text generation.
-        device (torch.device): The device to run the model on.
-
-    Returns:
-        Tuple[List[str], List[str], List[str], List[Image.Image]]: A tuple containing lists of prompts,
-        expected responses, generated texts, and input images.
-    """
     prompts = []
     expected_responses = []
     generated_texts = []
@@ -175,15 +155,6 @@ def run_predictions(
 
 
 def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[str]:
-    """
-    Extract unique class names from the detection dataset.
-
-    Args:
-        dataset (DetectionDataset): The dataset to extract class names from.
-
-    Returns:
-        List[str]: A sorted list of unique class names found in the dataset.
-    """
     class_set = set()
     for i in range(len(dataset.dataset)):
         image, data = dataset.dataset[i]

From 3fe24ceb9a3a3c31cdc2896bca575b9846ff2578 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 18:05:48 +0200
Subject: [PATCH 41/69] debug

---
 maestro/trainer/models/florence_2/metrics.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 73b70c3..6b729e1 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -156,10 +156,11 @@ def run_predictions(
 
 def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[str]:
     class_set = set()
-    for i in range(len(dataset.dataset)):
-        image, data = dataset.dataset[i]
+    for i in range(len(dataset)):
+        _, data = dataset[i]
         suffix = data["suffix"]
         classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
+        print(suffix, classes)
         class_set.update(classes)
     return sorted(class_set)
 

From e1554e7577f09846489d8757e175ed793c4daf60 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 18:11:04 +0200
Subject: [PATCH 42/69] debug

---
 maestro/trainer/models/florence_2/metrics.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 6b729e1..7e4c188 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -157,8 +157,7 @@ def run_predictions(
 def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[str]:
     class_set = set()
     for i in range(len(dataset)):
-        _, data = dataset[i]
-        suffix = data["suffix"]
+        _, suffix, _ = dataset[i]
         classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
         print(suffix, classes)
         class_set.update(classes)

From 5b42aece53bca9ccd5b1d709488e936fd6dabe59 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 18:37:38 +0200
Subject: [PATCH 43/69] retest

---
 maestro/trainer/models/florence_2/metrics.py | 16 ++++------------
 1 file changed, 4 insertions(+), 12 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 7e4c188..65793a4 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -18,7 +18,7 @@
 from maestro.trainer.common.utils.metrics import BaseMetric
 from maestro.trainer.models.florence_2.data_loading import prepare_detection_dataset
 
-DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 ]+ of [a-zA-Z0-9 ]+)<loc_\d+>"
+DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 ]+)<loc_\d+>"
 
 
 class MeanAveragePrecisionMetric(BaseMetric):
@@ -108,16 +108,9 @@ def postprocess_florence2_output_for_mean_average_precision(
         prediction.confidence = np.ones(len(prediction))  # Set confidence for mean average precision calculation
         
         # Postprocess target for mean average precision calculation
-        try:
-            target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
-            target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
-            target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
-        except Exception as e:
-            print(f"Exception occurred: {e}")
-            print(f"Classes: {classes}")
-            print(f"Suffix: {suffix}")
-            print(f"Target: {target}")
-            raise
+        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
+        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
+        target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
         
         targets.append(target)
         predictions.append(prediction)
@@ -159,7 +152,6 @@ def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[
     for i in range(len(dataset)):
         _, suffix, _ = dataset[i]
         classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
-        print(suffix, classes)
         class_set.update(classes)
     return sorted(class_set)
 

From 40a8bfad5bdb3a3ea1842be69610af6b939459e9 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 18:50:10 +0200
Subject: [PATCH 44/69] debug

---
 maestro/trainer/models/florence_2/metrics.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 65793a4..9355fe0 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -108,9 +108,15 @@ def postprocess_florence2_output_for_mean_average_precision(
         prediction.confidence = np.ones(len(prediction))  # Set confidence for mean average precision calculation
         
         # Postprocess target for mean average precision calculation
-        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
-        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
-        target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
+        try:
+            target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
+            target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
+            target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
+        except Exception as e:
+            print(f"Error processing target: {e}")
+            print(f"Classes: {classes}")
+            print(f"Suffix: {suffix}")
+            raise e
         
         targets.append(target)
         predictions.append(prediction)
@@ -152,6 +158,7 @@ def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[
     for i in range(len(dataset)):
         _, suffix, _ = dataset[i]
         classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
+        print(suffix, classes)
         class_set.update(classes)
     return sorted(class_set)
 

From 1db75adc0d137203f86328b7af708434ab030b86 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 18:58:09 +0200
Subject: [PATCH 45/69] ready for test

---
 maestro/trainer/models/florence_2/metrics.py | 15 ++++-----------
 1 file changed, 4 insertions(+), 11 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 9355fe0..6ad9c99 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -18,7 +18,7 @@
 from maestro.trainer.common.utils.metrics import BaseMetric
 from maestro.trainer.models.florence_2.data_loading import prepare_detection_dataset
 
-DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 ]+)<loc_\d+>"
+DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 -]+)<loc_\d+>"
 
 
 class MeanAveragePrecisionMetric(BaseMetric):
@@ -108,15 +108,9 @@ def postprocess_florence2_output_for_mean_average_precision(
         prediction.confidence = np.ones(len(prediction))  # Set confidence for mean average precision calculation
         
         # Postprocess target for mean average precision calculation
-        try:
-            target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
-            target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
-            target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
-        except Exception as e:
-            print(f"Error processing target: {e}")
-            print(f"Classes: {classes}")
-            print(f"Suffix: {suffix}")
-            raise e
+        target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)
+        target = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, target, resolution_wh=image.size)
+        target.class_id = np.array([classes.index(class_name) for class_name in target["class_name"]])
         
         targets.append(target)
         predictions.append(prediction)
@@ -158,7 +152,6 @@ def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[
     for i in range(len(dataset)):
         _, suffix, _ = dataset[i]
         classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
-        print(suffix, classes)
         class_set.update(classes)
     return sorted(class_set)
 

From bc0aff57a3517945308a156028562811cfeab6e9 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 19:07:04 +0200
Subject: [PATCH 46/69] debug

---
 maestro/trainer/models/florence_2/training.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 742f2a2..17f0669 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -363,6 +363,8 @@ def run_validation_epoch(
                     classes=classes,
                     processor=processor
                 )
+                print(f"Targets: {targets}")
+                print(f"Predictions: {predictions}")
                 result = metric.compute(targets=targets, predictions=predictions)
                 for key, value in result.items():
                     metrics_tracker.register(

From 2498de9896e726f7fe312e08bb498f2f66eccae6 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 19:43:01 +0200
Subject: [PATCH 47/69] am I this stupid?

---
 maestro/trainer/models/florence_2/metrics.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 6ad9c99..1f14ac6 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -31,7 +31,7 @@ def compute(
         targets: List[sv.Detections],
         predictions: List[sv.Detections]
     ) -> Dict[str, float]:
-        result = MeanAveragePrecision().update(targets, predictions).compute()
+        result = MeanAveragePrecision().update(targets=targets, predictions=predictions).compute()
         return {
             "map50:95": result.map50_95,
             "map50": result.map50,

From d363c663a507646510b042b056f78c5a6536a9de Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 19:51:21 +0200
Subject: [PATCH 48/69] clean up

---
 maestro/trainer/models/florence_2/training.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 17f0669..742f2a2 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -363,8 +363,6 @@ def run_validation_epoch(
                     classes=classes,
                     processor=processor
                 )
-                print(f"Targets: {targets}")
-                print(f"Predictions: {predictions}")
                 result = metric.compute(targets=targets, predictions=predictions)
                 for key, value in result.items():
                     metrics_tracker.register(

From 9aedb29467120fc71d62e03bf063345e05e071c8 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Mon, 9 Sep 2024 20:05:13 +0200
Subject: [PATCH 49/69] test validation set result render

---
 maestro/trainer/models/florence_2/training.py | 44 ++++++++++++++++++-
 1 file changed, 43 insertions(+), 1 deletion(-)

diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 742f2a2..2bbd2ea 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -342,7 +342,6 @@ def run_validation_epoch(
             step=1,
             value=avg_val_loss,
         )
-
         # Run inference once for all metrics
         prompts, expected_responses, generated_texts, images = run_predictions(
             dataset=loader.dataset,
@@ -375,6 +374,49 @@ def run_validation_epoch(
         
         print("Validation Metrics:", ", ".join([f"{k}: {v:.4f}" for k, v in metrics_results.items()]))
 
+        # Display inference results in IPython environments
+        try:
+            import IPython
+            if IPython.get_ipython() is not None:
+                import io
+                import base64
+                import html
+                import json
+                from PIL import Image
+                import supervision as sv
+                from IPython.display import display, HTML
+
+                def render_inline(image: Image.Image, resize=(128, 128)):
+                    """Convert image into inline html."""
+                    image = image.resize(resize)
+                    with io.BytesIO() as buffer:
+                        image.save(buffer, format='jpeg')
+                        image_b64 = str(base64.b64encode(buffer.getvalue()), "utf-8")
+                        return f"data:image/jpeg;base64,{image_b64}"
+
+                def render_example(image: Image.Image, response):
+                    # try:
+                    #     detections = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, response, resolution_wh=image.size)
+                    #     image = sv.BoundingBoxAnnotator(color_lookup=sv.ColorLookup.INDEX).annotate(image.copy(), detections)
+                    #     image = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX).annotate(image, detections)
+                    # except:
+                    #     print('failed to render model response')
+                    return f"""
+                <div style="display: inline-flex; align-items: center; justify-content: center;">
+                    <img style="width:256px; height:256px;" src="{render_inline(image, resize=(128, 128))}" />
+                    <p style="width:512px; margin:10px; font-size:small;">{html.escape(json.dumps(response))}</p>
+                </div>
+                """
+
+                html_out = ""
+                count = min(8, len(images))  # Display up to 8 examples
+                for i in range(count):
+                    html_out += render_example(images[i], generated_texts[i])
+
+                display(HTML(html_out))
+        except ImportError:
+            pass  # Skip visualization if required libraries are not available
+
 
 def save_model(
     target_dir: str,

From bb44d1161628cf60567a762fc828d63ddd44f77a Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 00:33:10 +0200
Subject: [PATCH 50/69] updated results display

---
 maestro/trainer/common/utils/metrics.py       | 101 +++++++++++++++++-
 maestro/trainer/models/florence_2/training.py |  44 +-------
 2 files changed, 102 insertions(+), 43 deletions(-)

diff --git a/maestro/trainer/common/utils/metrics.py b/maestro/trainer/common/utils/metrics.py
index dcc034b..e22cce4 100644
--- a/maestro/trainer/common/utils/metrics.py
+++ b/maestro/trainer/common/utils/metrics.py
@@ -1,12 +1,16 @@
 from __future__ import annotations
 
+import base64
+import html
+import io
 import json
 import os
 from abc import ABC, abstractmethod
 from collections import defaultdict
-from typing import Dict, Tuple, List, Any
+from typing import Any, Dict, List, Tuple
 
 import matplotlib.pyplot as plt
+from PIL import Image
 
 
 class BaseMetric(ABC):
@@ -177,3 +181,98 @@ def save_metric_plots(
         plt.grid(True)
         plt.savefig(f'{output_dir}/{metric}_plot.png')
         plt.close()
+
+
+
+def display_results(
+    prompts: List[str],
+    expected_responses: List[str],
+    generated_texts: List[str],
+    images: List[Image.Image]
+) -> None:
+    """
+    Display the results of model inference in IPython environments.
+
+    This function attempts to display the results (prompts, expected responses,
+    generated texts, and images) in an HTML format if running in an IPython
+    environment. If not in IPython or if there's an ImportError, it silently passes.
+
+    Args:
+        prompts (List[str]): List of input prompts.
+        expected_responses (List[str]): List of expected responses.
+        generated_texts (List[str]): List of texts generated by the model.
+        images (List[Image.Image]): List of input images.
+
+    Returns:
+        None
+    """
+    try:
+        import IPython
+        if IPython.get_ipython() is not None:
+            from IPython.display import display, HTML
+            html_out = create_html_output(prompts, expected_responses, generated_texts, images)
+            display(HTML(html_out))
+    except ImportError:
+        pass  # Skip visualization if required libraries are not available
+
+
+def create_html_output(
+    prompts: List[str],
+    expected_responses: List[str],
+    generated_texts: List[str],
+    images: List[Image.Image]
+) -> str:
+    """
+    Create an HTML string to display the results of model inference.
+
+    This function generates an HTML string that includes styled divs for each
+    result, containing the input image, prompt, expected response, and generated text.
+
+    Args:
+        prompts (List[str]): List of input prompts.
+        expected_responses (List[str]): List of expected responses.
+        generated_texts (List[str]): List of texts generated by the model.
+        images (List[Image.Image]): List of input images.
+
+    Returns:
+        str: An HTML string containing the formatted results.
+    """
+    html_out = "<style>.result-container{display:flex;margin-bottom:20px;border:1px solid #ddd;padding:10px;}.image-container{flex:0 0 256px;}.text-container{flex:1;margin-left:20px;}.prompt,.expected,.generated{margin-bottom:10px;}</style>"
+    count = min(8, len(images))  # Display up to 8 examples
+    for i in range(count):
+        html_out += f"""
+        <div class="result-container">
+            <div class="image-container">
+                <img style="width:256px; height:256px;" src="{render_inline(images[i])}" />
+            </div>
+            <div class="text-container">
+                <div class="prompt"><strong>Prompt:</strong> {html.escape(prompts[i])}</div>
+                <div class="expected"><strong>Expected:</strong> {html.escape(expected_responses[i])}</div>
+                <div class="generated"><strong>Generated:</strong> {html.escape(generated_texts[i])}</div>
+            </div>
+        </div>
+        """
+    return html_out
+
+
+def render_inline(image: Image.Image, resize: Tuple[int, int] = (256, 256)) -> str:
+    """
+    Convert an image into an inline HTML string.
+
+    This function takes an image, resizes it, and converts it to a base64-encoded
+    string that can be used as the source for an HTML img tag.
+
+    Args:
+        image (Image.Image): The input image to be converted.
+        resize (Tuple[int, int], optional): The dimensions to resize the image to.
+            Defaults to (256, 256).
+
+    Returns:
+        str: A string containing the data URI for the image, ready to be used
+        in an HTML img tag's src attribute.
+    """
+    image = image.resize(resize)
+    with io.BytesIO() as buffer:
+        image.save(buffer, format='jpeg')
+        image_b64 = base64.b64encode(buffer.getvalue()).decode()
+    return f"data:image/jpeg;base64,{image_b64}"
\ No newline at end of file
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 2bbd2ea..69eb928 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -14,7 +14,7 @@
 
 from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
 from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
-from maestro.trainer.common.utils.metrics import BaseMetric, MetricsTracker, save_metric_plots
+from maestro.trainer.common.utils.metrics import BaseMetric, MetricsTracker, display_results, save_metric_plots
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
 from maestro.trainer.models.florence_2.metrics import (
@@ -375,47 +375,7 @@ def run_validation_epoch(
         print("Validation Metrics:", ", ".join([f"{k}: {v:.4f}" for k, v in metrics_results.items()]))
 
         # Display inference results in IPython environments
-        try:
-            import IPython
-            if IPython.get_ipython() is not None:
-                import io
-                import base64
-                import html
-                import json
-                from PIL import Image
-                import supervision as sv
-                from IPython.display import display, HTML
-
-                def render_inline(image: Image.Image, resize=(128, 128)):
-                    """Convert image into inline html."""
-                    image = image.resize(resize)
-                    with io.BytesIO() as buffer:
-                        image.save(buffer, format='jpeg')
-                        image_b64 = str(base64.b64encode(buffer.getvalue()), "utf-8")
-                        return f"data:image/jpeg;base64,{image_b64}"
-
-                def render_example(image: Image.Image, response):
-                    # try:
-                    #     detections = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, response, resolution_wh=image.size)
-                    #     image = sv.BoundingBoxAnnotator(color_lookup=sv.ColorLookup.INDEX).annotate(image.copy(), detections)
-                    #     image = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX).annotate(image, detections)
-                    # except:
-                    #     print('failed to render model response')
-                    return f"""
-                <div style="display: inline-flex; align-items: center; justify-content: center;">
-                    <img style="width:256px; height:256px;" src="{render_inline(image, resize=(128, 128))}" />
-                    <p style="width:512px; margin:10px; font-size:small;">{html.escape(json.dumps(response))}</p>
-                </div>
-                """
-
-                html_out = ""
-                count = min(8, len(images))  # Display up to 8 examples
-                for i in range(count):
-                    html_out += render_example(images[i], generated_texts[i])
-
-                display(HTML(html_out))
-        except ImportError:
-            pass  # Skip visualization if required libraries are not available
+        display_results(prompts, expected_responses, generated_texts, images)
 
 
 def save_model(

From ec2a3243d6cd294e0330b39eac7cbee118d23f60 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 13:09:24 +0200
Subject: [PATCH 51/69] cleanup

---
 .../trainer/models/florence_2/data_loading.py |   4 +-
 maestro/trainer/models/florence_2/metrics.py  | 118 +++++++++---------
 2 files changed, 61 insertions(+), 61 deletions(-)

diff --git a/maestro/trainer/models/florence_2/data_loading.py b/maestro/trainer/models/florence_2/data_loading.py
index 4fa9708..4873003 100644
--- a/maestro/trainer/models/florence_2/data_loading.py
+++ b/maestro/trainer/models/florence_2/data_loading.py
@@ -76,7 +76,7 @@ def prepare_detection_data_loader(
     return DataLoader(
         dataset,
         batch_size=batch_size,
-        collate_fn=partial(_collate_fn, processor=processor, device=device),
+        collate_fn=partial(collate_fn, processor=processor, device=device),
         num_workers=num_workers,
         shuffle=shuffle,
     )
@@ -100,7 +100,7 @@ def prepare_detection_dataset(
     )
 
 
-def _collate_fn(
+def collate_fn(
     batch: Tuple[List[str], List[str], List[Image.Image]],
     processor: AutoProcessor,
     device: torch.device,
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 1f14ac6..da6ffe7 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -170,62 +170,62 @@ def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[
 #     save_json(path=target_path, content=content)
 
 
-def dump_post_processed_outputs(
-    dataset: DetectionDataset,
-    post_processed_text_outputs: List[str],
-    training_dir: str,
-    split_name: str,
-) -> None:
-    result_dict = {
-        dataset.dataset.entries[idx]["image"]: output for idx, output in enumerate(post_processed_text_outputs)
-    }
-    target_path = os.path.join(
-        training_dir,
-        "model_debug",
-        split_name,
-        f"post_processed_text_predictions_{split_name}.json",
-    )
-    save_json(path=target_path, content=result_dict)
-
-
-def dump_visualised_samples(
-    dataset: DetectionDataset,
-    targets: List[sv.Detections],
-    predictions: List[sv.Detections],
-    num_samples_to_visualise: int,
-    training_dir: str,
-    split_name: str,
-) -> None:
-    all_indices = list(range(len(dataset)))
-    random.shuffle(all_indices)
-    selected_indices = all_indices[:num_samples_to_visualise]
-    target_dir = os.path.join(training_dir, "predictions_visualised", split_name)
-    os.makedirs(target_dir, exist_ok=True)
-    boxes_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
-    label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
-    for idx in tqdm(selected_indices, desc="Preparing predictions visualisations..."):
-        image_name = dataset.dataset.entries[idx]["image"]
-        image = dataset.dataset[idx][0]
-        prediction_image = boxes_annotator.annotate(
-            image.copy(),
-            predictions[idx],
-        )
-        prediction_image = np.asarray(
-            label_annotator.annotate(
-                prediction_image,
-                predictions[idx],
-            )
-        )[:, :, ::-1]
-        target_image = boxes_annotator.annotate(
-            image.copy(),
-            targets[idx],
-        )
-        target_image = np.asarray(
-            label_annotator.annotate(
-                target_image,
-                targets[idx],
-            )
-        )[:, :, ::-1]
-        concatenated = cv2.hconcat([target_image, prediction_image])
-        target_image_path = os.path.join(target_dir, image_name)
-        cv2.imwrite(target_image_path, concatenated)
+# def dump_post_processed_outputs(
+#     dataset: DetectionDataset,
+#     post_processed_text_outputs: List[str],
+#     training_dir: str,
+#     split_name: str,
+# ) -> None:
+#     result_dict = {
+#         dataset.dataset.entries[idx]["image"]: output for idx, output in enumerate(post_processed_text_outputs)
+#     }
+#     target_path = os.path.join(
+#         training_dir,
+#         "model_debug",
+#         split_name,
+#         f"post_processed_text_predictions_{split_name}.json",
+#     )
+#     save_json(path=target_path, content=result_dict)
+
+
+# def dump_visualised_samples(
+#     dataset: DetectionDataset,
+#     targets: List[sv.Detections],
+#     predictions: List[sv.Detections],
+#     num_samples_to_visualise: int,
+#     training_dir: str,
+#     split_name: str,
+# ) -> None:
+#     all_indices = list(range(len(dataset)))
+#     random.shuffle(all_indices)
+#     selected_indices = all_indices[:num_samples_to_visualise]
+#     target_dir = os.path.join(training_dir, "predictions_visualised", split_name)
+#     os.makedirs(target_dir, exist_ok=True)
+#     boxes_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
+#     label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
+#     for idx in tqdm(selected_indices, desc="Preparing predictions visualisations..."):
+#         image_name = dataset.dataset.entries[idx]["image"]
+#         image = dataset.dataset[idx][0]
+#         prediction_image = boxes_annotator.annotate(
+#             image.copy(),
+#             predictions[idx],
+#         )
+#         prediction_image = np.asarray(
+#             label_annotator.annotate(
+#                 prediction_image,
+#                 predictions[idx],
+#             )
+#         )[:, :, ::-1]
+#         target_image = boxes_annotator.annotate(
+#             image.copy(),
+#             targets[idx],
+#         )
+#         target_image = np.asarray(
+#             label_annotator.annotate(
+#                 target_image,
+#                 targets[idx],
+#             )
+#         )[:, :, ::-1]
+#         concatenated = cv2.hconcat([target_image, prediction_image])
+#         target_image_path = os.path.join(target_dir, image_name)
+#         cv2.imwrite(target_image_path, concatenated)

From 27239875253bb9ab9c229d5f41a2cca4c6ce09c6 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 14:49:44 +0200
Subject: [PATCH 52/69] test new checkpoint management system

---
 .../trainer/models/florence_2/checkpoints.py  | 105 +++++++++++++++++
 maestro/trainer/models/florence_2/training.py | 108 +++---------------
 2 files changed, 119 insertions(+), 94 deletions(-)
 create mode 100644 maestro/trainer/models/florence_2/checkpoints.py

diff --git a/maestro/trainer/models/florence_2/checkpoints.py b/maestro/trainer/models/florence_2/checkpoints.py
new file mode 100644
index 0000000..cdffe7b
--- /dev/null
+++ b/maestro/trainer/models/florence_2/checkpoints.py
@@ -0,0 +1,105 @@
+import os
+from typing import Optional, Tuple
+
+import torch
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, \
+    DEFAULT_CUDA_DEVICE
+
+DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
+DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
+DEVICE = torch.device("cpu") \
+    if not torch.cuda.is_available() \
+    else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
+
+
+class CheckpointManager:
+    """Manages checkpoints for model training.
+
+    This class handles saving and retrieving model checkpoints during training.
+
+    Attributes:
+        training_dir (str): Directory where checkpoints will be saved.
+        best_val_loss (float): Best validation loss achieved so far.
+        latest_checkpoint_dir (str): Directory for the latest checkpoint.
+        best_checkpoint_dir (str): Directory for the best checkpoint.
+    """
+
+    def __init__(self, training_dir: str):
+        """Initializes the CheckpointManager.
+
+        Args:
+            training_dir (str): Directory where checkpoints will be saved.
+        """
+        self.training_dir = training_dir
+        self.best_val_loss = float('inf')
+        self.latest_checkpoint_dir = os.path.join(training_dir, "checkpoints", "latest")
+        self.best_checkpoint_dir = os.path.join(training_dir, "checkpoints", "best")
+
+    def save_latest(self, processor: AutoProcessor, model: AutoModelForCausalLM):
+        """Saves the latest model checkpoint.
+
+        Args:
+            processor (AutoProcessor): The processor to save.
+            model (AutoModelForCausalLM): The model to save.
+        """
+        save_model(self.latest_checkpoint_dir, processor, model)
+
+    def save_best(self, processor: AutoProcessor, model: AutoModelForCausalLM, val_loss: float):
+        """Saves the best model checkpoint if the validation loss improves.
+
+        Args:
+            processor (AutoProcessor): The processor to save.
+            model (AutoModelForCausalLM): The model to save.
+            val_loss (float): The current validation loss.
+        """
+        if val_loss < self.best_val_loss:
+            self.best_val_loss = val_loss
+            save_model(self.best_checkpoint_dir, processor, model)
+            print(f"New best model saved with validation loss: {self.best_val_loss}")
+
+    def get_best_model_path(self):
+        """Returns the path to the best model checkpoint.
+
+        Returns:
+            str: Path to the best model checkpoint.
+        """
+        return self.best_checkpoint_dir
+
+
+def save_model(
+    target_dir: str,
+    processor: AutoProcessor,
+    model: AutoModelForCausalLM,
+) -> None:
+    """Saves the model and processor to the specified directory.
+
+    Args:
+        target_dir (str): Directory where the model and processor will be saved.
+        processor (AutoProcessor): The processor to save.
+        model (AutoModelForCausalLM): The model to save.
+    """
+    os.makedirs(target_dir, exist_ok=True)
+    processor.save_pretrained(target_dir)
+    model.save_pretrained(target_dir)
+
+
+def load_model(
+    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: torch.device = DEVICE,
+    cache_dir: Optional[str] = None,
+) -> Tuple[AutoProcessor, AutoModelForCausalLM]:
+    processor = AutoProcessor.from_pretrained(
+        model_id_or_path,
+        trust_remote_code=True,
+        revision=revision,
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id_or_path,
+        trust_remote_code=True,
+        revision=revision,
+        cache_dir=cache_dir,
+    ).to(device)
+    return processor, model
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 69eb928..702546b 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -1,5 +1,4 @@
 import os
-import shutil
 from dataclasses import dataclass, field, replace
 from glob import glob
 from typing import List, Literal, Optional, Tuple, Union
@@ -12,10 +11,11 @@
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
-from maestro.trainer.common.configuration.env import CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE
-from maestro.trainer.common.utils.leaderboard import CheckpointsLeaderboard
-from maestro.trainer.common.utils.metrics import BaseMetric, MetricsTracker, display_results, save_metric_plots
+from maestro.trainer.common.utils.metrics import BaseMetric, MetricsTracker, \
+    display_results, save_metric_plots
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
+from maestro.trainer.models.florence_2.checkpoints import CheckpointManager, load_model, \
+    DEFAULT_FLORENCE2_MODEL_ID, DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
 from maestro.trainer.models.florence_2.metrics import (
     MeanAveragePrecisionMetric,
@@ -25,12 +25,6 @@
 )
 from maestro.trainer.models.paligemma.training import LoraInitLiteral
 
-DEFAULT_FLORENCE2_MODEL_ID = "microsoft/Florence-2-base-ft"
-DEFAULT_FLORENCE2_MODEL_REVISION = "refs/pr/20"
-DEVICE = torch.device("cpu") \
-    if not torch.cuda.is_available() \
-    else os.getenv(CUDA_DEVICE_ENV, DEFAULT_CUDA_DEVICE)
-
 
 @dataclass(frozen=True)
 class TrainingConfiguration:
@@ -54,7 +48,6 @@ class TrainingConfiguration:
     use_rslora: bool = True
     init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
     training_dir: str = "./training/florence-2"
-    max_checkpoints_to_keep: int = 3
     num_samples_to_visualise: int = 64
     metrics: List[BaseMetric] = field(default_factory=list)
 
@@ -68,9 +61,8 @@ def train(configuration: TrainingConfiguration) -> None:
         configuration,
         training_dir=training_run_dir,
     )
-    checkpoints_leaderboard = CheckpointsLeaderboard(
-        max_checkpoints=configuration.max_checkpoints_to_keep,
-    )
+    checkpoint_manager = CheckpointManager(training_run_dir)
+    
     processor, model = load_model(
         model_id_or_path=configuration.model_id_or_path,
         revision=configuration.revision,
@@ -85,8 +77,6 @@ def train(configuration: TrainingConfiguration) -> None:
         num_workers=configuration.loaders_workers,
         test_loaders_workers=configuration.test_loaders_workers,
     )
-    # if test_loader is None:
-    #     test_loader = val_loader
     peft_model = prepare_peft_model(
         model=model,
         r=configuration.lora_r,
@@ -108,29 +98,11 @@ def train(configuration: TrainingConfiguration) -> None:
         model=peft_model,
         data_loaders=(train_loader, val_loader),
         configuration=configuration,
-        checkpoints_leaderboard=checkpoints_leaderboard,
         training_metrics_tracker=training_metrics_tracker,
         validation_metrics_tracker=validation_metrics_tracker,
+        checkpoint_manager=checkpoint_manager
     )
 
-    best_model_path = checkpoints_leaderboard.get_best_model()
-    print(f"Loading best model from {best_model_path}")
-    processor, model = load_model(
-        model_id_or_path=best_model_path,
-    )
-    # if test_loader is not None:
-    #     run_validation_epoch(
-    #         processor=processor,
-    #         model=model,
-    #         loader=test_loader,
-    #         epoch_number=None,
-    #         configuration=configuration,
-    #         title="Test",
-    #     )
-    best_model_dir = os.path.join(configuration.training_dir, "best_model")
-    print(f"Saving best model: {best_model_dir}")
-    model.save_pretrained(best_model_dir)
-    processor.save_pretrained(best_model_dir)
     save_metric_plots(
         training_tracker=training_metrics_tracker,
         validation_tracker=validation_metrics_tracker,
@@ -143,37 +115,6 @@ def train(configuration: TrainingConfiguration) -> None:
         output_dir=os.path.join(configuration.training_dir, "metrics"),
         filename="validation.json")
 
-    # for split_name in ["valid", "test"]:
-    #     prepare_detection_training_summary(
-    #         processor=processor,
-    #         model=model,
-    #         dataset_location=configuration.dataset_location,
-    #         split_name=split_name,
-    #         training_dir=configuration.training_dir,
-    #         num_samples_to_visualise=configuration.num_samples_to_visualise,
-    #         device=configuration.device,
-    #     )
-
-
-def load_model(
-    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID,
-    revision: str = DEFAULT_FLORENCE2_MODEL_REVISION,
-    device: torch.device = DEVICE,
-    cache_dir: Optional[str] = None,
-) -> Tuple[AutoProcessor, AutoModelForCausalLM]:
-    processor = AutoProcessor.from_pretrained(
-        model_id_or_path,
-        trust_remote_code=True,
-        revision=revision,
-    )
-    model = AutoModelForCausalLM.from_pretrained(
-        model_id_or_path,
-        trust_remote_code=True,
-        revision=revision,
-        cache_dir=cache_dir,
-    ).to(device)
-    return processor, model
-
 
 def prepare_peft_model(
     model: AutoModelForCausalLM,
@@ -208,9 +149,9 @@ def run_training_loop(
     model: PeftModel,
     data_loaders: Tuple[DataLoader, Optional[DataLoader]],
     configuration: TrainingConfiguration,
-    checkpoints_leaderboard: CheckpointsLeaderboard,
     training_metrics_tracker: MetricsTracker,
     validation_metrics_tracker: MetricsTracker,
+    checkpoint_manager: CheckpointManager,
 ) -> None:
     train_loader, val_loader = data_loaders
     optimizer = _get_optimizer(model=model, configuration=configuration)
@@ -231,9 +172,9 @@ def run_training_loop(
             configuration=configuration,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
-            checkpoints_leaderboard=checkpoints_leaderboard,
             training_metrics_tracker=training_metrics_tracker,
             validation_metrics_tracker=validation_metrics_tracker,
+            checkpoint_manager=checkpoint_manager
         )
 
 
@@ -246,9 +187,9 @@ def run_training_epoch(
     configuration: TrainingConfiguration,
     optimizer: Optimizer,
     lr_scheduler: LRScheduler,
-    checkpoints_leaderboard: CheckpointsLeaderboard,
     training_metrics_tracker: MetricsTracker,
     validation_metrics_tracker: MetricsTracker,
+    checkpoint_manager: CheckpointManager,
 ) -> None:
     model.train()
     training_losses: List[float] = []
@@ -292,21 +233,10 @@ def run_training_epoch(
         configuration=configuration,
         metrics_tracker=validation_metrics_tracker,
     )
-    validation_loss = validation_metrics_tracker.get_metric_values("loss")[-1][2]
-    checkpoint_dir = os.path.join(configuration.training_dir, "checkpoints", str(epoch_number))
-    should_save, to_remove = checkpoints_leaderboard.register_checkpoint(
-        epoch=epoch_number,
-        path=checkpoint_dir,
-        loss=validation_loss,
-    )
-    if should_save:
-        print(f"Saving checkpoint under {checkpoint_dir}")
-        os.makedirs(checkpoint_dir, exist_ok=True)
-        model.save_pretrained(checkpoint_dir)
-        processor.save_pretrained(checkpoint_dir)
-    if to_remove is not None:
-        print(f"Removing checkpoint {to_remove}")
-        shutil.rmtree(to_remove, ignore_errors=True)
+    
+    val_loss = validation_metrics_tracker.get_metric_values("loss")[-1][2]
+    checkpoint_manager.save_latest(processor, model)
+    checkpoint_manager.save_best(processor, model, val_loss)
 
 
 def run_validation_epoch(
@@ -378,16 +308,6 @@ def run_validation_epoch(
         display_results(prompts, expected_responses, generated_texts, images)
 
 
-def save_model(
-    target_dir: str,
-    processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-) -> None:
-    os.makedirs(target_dir, exist_ok=True)
-    processor.save_pretrained(target_dir)
-    model.save_pretrained(target_dir)
-
-
 def _establish_training_run_dir(training_dir: str) -> str:
     training_dir = os.path.abspath(training_dir)
     existing_directory_entries = glob(os.path.join(training_dir, "*"))

From 27c3cfd40a8451838ed371efcc00fa45fa2e6d6e Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 14:57:52 +0200
Subject: [PATCH 53/69] more cleanup

---
 maestro/trainer/models/florence_2/metrics.py | 133 +------------------
 1 file changed, 2 insertions(+), 131 deletions(-)

diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index da6ffe7..ae01b38 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -1,22 +1,17 @@
-import os
-import random
 import re
 from typing import List, Dict
 from typing import Tuple
 
-import cv2
 import numpy as np
 import supervision as sv
 import torch
+from PIL import Image
 from supervision.metrics.mean_average_precision import MeanAveragePrecision
 from tqdm import tqdm
 from transformers import AutoProcessor, AutoModelForCausalLM
-from PIL import Image
 
 from maestro.trainer.common.data_loaders.datasets import DetectionDataset
-from maestro.trainer.common.utils.file_system import save_json
 from maestro.trainer.common.utils.metrics import BaseMetric
-from maestro.trainer.models.florence_2.data_loading import prepare_detection_dataset
 
 DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 -]+)<loc_\d+>"
 
@@ -39,56 +34,6 @@ def compute(
         }
 
 
-# def prepare_detection_training_summary(
-#     processor: AutoProcessor,
-#     model: AutoModelForCausalLM,
-#     dataset_location: str,
-#     split_name: str,
-#     training_dir: str,
-#     num_samples_to_visualise: int,
-#     device: torch.device,
-# ) -> None:
-#     dataset = prepare_detection_dataset(
-#         dataset_location=dataset_location,
-#         split_name=split_name,
-#     )
-#     if dataset is None:
-#         return None
-#     targets, predictions, post_processed_text_outputs = get_ground_truths_and_predictions(
-#         dataset=dataset,
-#         processor=processor,
-#         model=model,
-#         split_name=split_name,
-#         device=device,
-#     )
-#     mean_average_precision = sv.MeanAveragePrecision.from_detections(
-#         predictions=predictions,
-#         targets=targets,
-#     )
-#     print(f"{split_name} | map50_95: {mean_average_precision.map50_95:.2f}")
-#     print(f"{split_name} | map50: {mean_average_precision.map50:.2f}")
-#     print(f"{split_name} | map75: {mean_average_precision.map75:.2f}")
-#     dump_metrics(
-#         training_dir=training_dir,
-#         split_name=split_name,
-#         metrics=mean_average_precision,
-#     )
-#     dump_post_processed_outputs(
-#         dataset=dataset,
-#         post_processed_text_outputs=post_processed_text_outputs,
-#         training_dir=training_dir,
-#         split_name=split_name,
-#     )
-#     dump_visualised_samples(
-#         dataset=dataset,
-#         targets=targets,
-#         predictions=predictions,
-#         num_samples_to_visualise=num_samples_to_visualise,
-#         training_dir=training_dir,
-#         split_name=split_name,
-#     )
-
-
 def postprocess_florence2_output_for_mean_average_precision(
     expected_responses: List[str],
     generated_texts: List[str],
@@ -117,6 +62,7 @@ def postprocess_florence2_output_for_mean_average_precision(
     
     return targets, predictions
 
+
 def run_predictions(
     dataset: DetectionDataset,
     processor: AutoProcessor,
@@ -154,78 +100,3 @@ def extract_unique_detection_dataset_classes(dataset: DetectionDataset) -> List[
         classes = re.findall(DETECTION_CLASS_PATTERN, suffix)
         class_set.update(classes)
     return sorted(class_set)
-
-
-# def dump_metrics(
-#     training_dir: str,
-#     split_name: str,
-#     metrics: sv.MeanAveragePrecision,
-# ) -> None:
-#     target_path = os.path.join(training_dir, "metrics", split_name, f"metrics_{split_name}.json")
-#     content = {
-#         "map50_95": metrics.map50_95,
-#         "map50": metrics.map50,
-#         "map75": metrics.map75,
-#     }
-#     save_json(path=target_path, content=content)
-
-
-# def dump_post_processed_outputs(
-#     dataset: DetectionDataset,
-#     post_processed_text_outputs: List[str],
-#     training_dir: str,
-#     split_name: str,
-# ) -> None:
-#     result_dict = {
-#         dataset.dataset.entries[idx]["image"]: output for idx, output in enumerate(post_processed_text_outputs)
-#     }
-#     target_path = os.path.join(
-#         training_dir,
-#         "model_debug",
-#         split_name,
-#         f"post_processed_text_predictions_{split_name}.json",
-#     )
-#     save_json(path=target_path, content=result_dict)
-
-
-# def dump_visualised_samples(
-#     dataset: DetectionDataset,
-#     targets: List[sv.Detections],
-#     predictions: List[sv.Detections],
-#     num_samples_to_visualise: int,
-#     training_dir: str,
-#     split_name: str,
-# ) -> None:
-#     all_indices = list(range(len(dataset)))
-#     random.shuffle(all_indices)
-#     selected_indices = all_indices[:num_samples_to_visualise]
-#     target_dir = os.path.join(training_dir, "predictions_visualised", split_name)
-#     os.makedirs(target_dir, exist_ok=True)
-#     boxes_annotator = sv.BoxAnnotator(color_lookup=sv.ColorLookup.INDEX)
-#     label_annotator = sv.LabelAnnotator(color_lookup=sv.ColorLookup.INDEX)
-#     for idx in tqdm(selected_indices, desc="Preparing predictions visualisations..."):
-#         image_name = dataset.dataset.entries[idx]["image"]
-#         image = dataset.dataset[idx][0]
-#         prediction_image = boxes_annotator.annotate(
-#             image.copy(),
-#             predictions[idx],
-#         )
-#         prediction_image = np.asarray(
-#             label_annotator.annotate(
-#                 prediction_image,
-#                 predictions[idx],
-#             )
-#         )[:, :, ::-1]
-#         target_image = boxes_annotator.annotate(
-#             image.copy(),
-#             targets[idx],
-#         )
-#         target_image = np.asarray(
-#             label_annotator.annotate(
-#                 target_image,
-#                 targets[idx],
-#             )
-#         )[:, :, ::-1]
-#         concatenated = cv2.hconcat([target_image, prediction_image])
-#         target_image_path = os.path.join(target_dir, image_name)
-#         cv2.imwrite(target_image_path, concatenated)

From dad39bab6ff3335b8843f5fff65538e840ac866a Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 16:00:08 +0200
Subject: [PATCH 54/69] TrainingConfiguration filed names refactoer

---
 maestro/cli/introspection.py                  |   3 +-
 maestro/trainer/models/florence_2/training.py | 166 +++++++++++-------
 2 files changed, 100 insertions(+), 69 deletions(-)

diff --git a/maestro/cli/introspection.py b/maestro/cli/introspection.py
index 3a62ce3..fc6aef9 100644
--- a/maestro/cli/introspection.py
+++ b/maestro/cli/introspection.py
@@ -2,7 +2,8 @@
 
 import typer
 
-from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
+from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, \
+    DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
 from maestro.cli.utils import str2bool
 
 
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/training.py
index 702546b..1885a9f 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/training.py
@@ -28,68 +28,95 @@
 
 @dataclass(frozen=True)
 class TrainingConfiguration:
-    dataset_location: str
-    model_id_or_path: str = DEFAULT_FLORENCE2_MODEL_ID
+    """Configuration for training a Florence-2 model.
+
+    This class encapsulates all the parameters needed for training a Florence-2 model,
+    including dataset paths, model specifications, training hyperparameters, and output settings.
+
+    Attributes:
+        dataset_path (str): Path to the dataset used for training.
+        model_id (str): Identifier for the Florence-2 model. Defaults to DEFAULT_FLORENCE2_MODEL_ID.
+        revision (str): Revision of the model to use. Defaults to DEFAULT_FLORENCE2_MODEL_REVISION.
+        device (torch.device): Device to use for training. Defaults to DEVICE.
+        cache_dir (Optional[str]): Directory to cache the model. Defaults to None.
+        epochs (int): Number of training epochs. Defaults to 10.
+        optimizer (Literal["sgd", "adamw", "adam"]): Optimizer to use for training. Defaults to "adamw".
+        lr (float): Learning rate for the optimizer. Defaults to 1e-5.
+        lr_scheduler (Literal["linear", "cosine", "polynomial"]): Learning rate scheduler. Defaults to "linear".
+        batch_size (int): Batch size for training. Defaults to 4.
+        val_batch_size (Optional[int]): Batch size for validation. Defaults to None.
+        num_workers (int): Number of workers for data loading. Defaults to 0.
+        val_num_workers (Optional[int]): Number of workers for validation data loading. Defaults to None.
+        lora_r (int): Rank of the LoRA update matrices. Defaults to 8.
+        lora_alpha (int): Scaling factor for the LoRA update. Defaults to 8.
+        lora_dropout (float): Dropout probability for LoRA layers. Defaults to 0.05.
+        bias (Literal["none", "all", "lora_only"]): Which bias to train. Defaults to "none".
+        use_rslora (bool): Whether to use RSLoRA. Defaults to True.
+        init_lora_weights (Union[bool, LoraInitLiteral]): How to initialize LoRA weights. Defaults to "gaussian".
+        output_dir (str): Directory to save output files. Defaults to "./training/florence-2".
+        metrics (List[BaseMetric]): List of metrics to track during training. Defaults to an empty list.
+    """
+    dataset_path: str
+    model_id: str = DEFAULT_FLORENCE2_MODEL_ID
     revision: str = DEFAULT_FLORENCE2_MODEL_REVISION
     device: torch.device = DEVICE
-    transformers_cache_dir: Optional[str] = None
-    training_epochs: int = 10
-    optimiser: Literal["SGD", "adamw", "adam"] = "adamw"
-    learning_rate: float = 1e-5
+    cache_dir: Optional[str] = None
+    epochs: int = 10
+    optimizer: Literal["sgd", "adamw", "adam"] = "adamw"
+    lr: float = 1e-5
     lr_scheduler: Literal["linear", "cosine", "polynomial"] = "linear"
-    train_batch_size: int = 4
-    test_batch_size: Optional[int] = None
-    loaders_workers: int = 0
-    test_loaders_workers: Optional[int] = None
+    batch_size: int = 4
+    val_batch_size: Optional[int] = None
+    num_workers: int = 0
+    val_num_workers: Optional[int] = None
     lora_r: int = 8
     lora_alpha: int = 8
     lora_dropout: float = 0.05
     bias: Literal["none", "all", "lora_only"] = "none"
     use_rslora: bool = True
     init_lora_weights: Union[bool, LoraInitLiteral] = "gaussian"
-    training_dir: str = "./training/florence-2"
-    num_samples_to_visualise: int = 64
+    output_dir: str = "./training/florence-2"
     metrics: List[BaseMetric] = field(default_factory=list)
 
 
-def train(configuration: TrainingConfiguration) -> None:
+def train(config: TrainingConfiguration) -> None:
     make_it_reproducible(avoid_non_deterministic_algorithms=False)
-    training_run_dir = _establish_training_run_dir(
-        training_dir=configuration.training_dir,
+    run_dir = _establish_training_run_dir(
+        output_dir=config.output_dir,
     )
-    configuration = replace(
-        configuration,
-        training_dir=training_run_dir,
+    config = replace(
+        config,
+        output_dir=run_dir,
     )
-    checkpoint_manager = CheckpointManager(training_run_dir)
+    checkpoint_manager = CheckpointManager(run_dir)
     
     processor, model = load_model(
-        model_id_or_path=configuration.model_id_or_path,
-        revision=configuration.revision,
-        device=configuration.device,
-        cache_dir=configuration.transformers_cache_dir,
+        model_id_or_path=config.model_id,
+        revision=config.revision,
+        device=config.device,
+        cache_dir=config.cache_dir,
     )
     train_loader, val_loader, test_loader = prepare_data_loaders(
-        dataset_location=configuration.dataset_location,
-        train_batch_size=configuration.train_batch_size,
+        dataset_location=config.dataset_path,
+        train_batch_size=config.batch_size,
         processor=processor,
-        device=configuration.device,
-        num_workers=configuration.loaders_workers,
-        test_loaders_workers=configuration.test_loaders_workers,
+        device=config.device,
+        num_workers=config.num_workers,
+        test_loaders_workers=config.val_num_workers,
     )
     peft_model = prepare_peft_model(
         model=model,
-        r=configuration.lora_r,
-        lora_alpha=configuration.lora_alpha,
-        lora_dropout=configuration.lora_dropout,
-        bias=configuration.bias,
-        use_rslora=configuration.use_rslora,
-        init_lora_weights=configuration.init_lora_weights,
-        revision=configuration.revision,
+        r=config.lora_r,
+        lora_alpha=config.lora_alpha,
+        lora_dropout=config.lora_dropout,
+        bias=config.bias,
+        use_rslora=config.use_rslora,
+        init_lora_weights=config.init_lora_weights,
+        revision=config.revision,
     )
     training_metrics_tracker = MetricsTracker.init(metrics=["loss"])
     metrics = ["loss"]
-    for metric in configuration.metrics:
+    for metric in config.metrics:
         metrics += metric.describe()
     validation_metrics_tracker = MetricsTracker.init(metrics=metrics)
 
@@ -97,7 +124,7 @@ def train(configuration: TrainingConfiguration) -> None:
         processor=processor,
         model=peft_model,
         data_loaders=(train_loader, val_loader),
-        configuration=configuration,
+        config=config,
         training_metrics_tracker=training_metrics_tracker,
         validation_metrics_tracker=validation_metrics_tracker,
         checkpoint_manager=checkpoint_manager
@@ -106,13 +133,13 @@ def train(configuration: TrainingConfiguration) -> None:
     save_metric_plots(
         training_tracker=training_metrics_tracker,
         validation_tracker=validation_metrics_tracker,
-        output_dir=os.path.join(configuration.training_dir, "metrics"),
+        output_dir=os.path.join(config.output_dir, "metrics"),
     )
     training_metrics_tracker.as_json(
-        output_dir=os.path.join(configuration.training_dir, "metrics"),
+        output_dir=os.path.join(config.output_dir, "metrics"),
         filename="training.json")
     validation_metrics_tracker.as_json(
-        output_dir=os.path.join(configuration.training_dir, "metrics"),
+        output_dir=os.path.join(config.output_dir, "metrics"),
         filename="validation.json")
 
 
@@ -148,28 +175,28 @@ def run_training_loop(
     processor: AutoProcessor,
     model: PeftModel,
     data_loaders: Tuple[DataLoader, Optional[DataLoader]],
-    configuration: TrainingConfiguration,
+    config: TrainingConfiguration,
     training_metrics_tracker: MetricsTracker,
     validation_metrics_tracker: MetricsTracker,
     checkpoint_manager: CheckpointManager,
 ) -> None:
     train_loader, val_loader = data_loaders
-    optimizer = _get_optimizer(model=model, configuration=configuration)
-    total_num_training_steps = configuration.training_epochs * len(train_loader)
+    optimizer = _get_optimizer(model=model, config=config)
+    total_steps = config.epochs * len(train_loader)
     lr_scheduler = get_scheduler(
-        name=configuration.lr_scheduler,
+        name=config.lr_scheduler,
         optimizer=optimizer,
         num_warmup_steps=0,
-        num_training_steps=total_num_training_steps,
+        num_training_steps=total_steps,
     )
-    for epoch in range(configuration.training_epochs):
+    for epoch in range(config.epochs):
         run_training_epoch(
             processor=processor,
             model=model,
             train_loader=train_loader,
             val_loader=val_loader,
             epoch_number=epoch + 1,
-            configuration=configuration,
+            config=config,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
             training_metrics_tracker=training_metrics_tracker,
@@ -184,7 +211,7 @@ def run_training_epoch(
     train_loader: DataLoader,
     val_loader: Optional[DataLoader],
     epoch_number: int,
-    configuration: TrainingConfiguration,
+    config: TrainingConfiguration,
     optimizer: Optimizer,
     lr_scheduler: LRScheduler,
     training_metrics_tracker: MetricsTracker,
@@ -193,13 +220,13 @@ def run_training_epoch(
 ) -> None:
     model.train()
     training_losses: List[float] = []
-    training_iterator = tqdm(train_loader, desc=f"Epoch {epoch_number}/{configuration.training_epochs}")
+    training_iterator = tqdm(train_loader, desc=f"Epoch {epoch_number}/{config.epochs}")
     for step_id, (inputs, answers) in enumerate(training_iterator):
         input_ids = inputs["input_ids"]
         pixel_values = inputs["pixel_values"]
         labels = processor.tokenizer(
             text=answers, return_tensors="pt", padding=True, return_token_type_ids=False
-        ).input_ids.to(configuration.device)
+        ).input_ids.to(config.device)
         outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)
         loss = outputs.loss
         loss.backward()
@@ -217,7 +244,7 @@ def run_training_epoch(
         last_100_losses = training_losses[-100:]
         loss_moving_average = sum(last_100_losses) / len(last_100_losses) if len(last_100_losses) > 0 else 0.0
         training_iterator.set_description(
-            f"Epoch {epoch_number}/{configuration.training_epochs}. Loss: {round(loss_moving_average, 4)}"
+            f"Epoch {epoch_number}/{config.epochs}. Loss: {round(loss_moving_average, 4)}"
         )
     if len(training_losses) > 0:
         avg_train_loss = sum(training_losses) / len(training_losses)
@@ -230,7 +257,7 @@ def run_training_epoch(
         model=model,
         loader=val_loader,
         epoch_number=epoch_number,
-        configuration=configuration,
+        config=config,
         metrics_tracker=validation_metrics_tracker,
     )
     
@@ -243,7 +270,7 @@ def run_validation_epoch(
     processor: AutoProcessor,
     model: Union[PeftModel, AutoModelForCausalLM],
     loader: DataLoader,
-    configuration: TrainingConfiguration,
+    config: TrainingConfiguration,
     metrics_tracker: MetricsTracker,
     epoch_number: int
 ) -> None:
@@ -257,7 +284,7 @@ def run_validation_epoch(
                 return_tensors="pt",
                 padding=True,
                 return_token_type_ids=False
-            ).input_ids.to(configuration.device)
+            ).input_ids.to(config.device)
             outputs = model(
                 input_ids=input_ids,
                 pixel_values=pixel_values,
@@ -277,12 +304,12 @@ def run_validation_epoch(
             dataset=loader.dataset,
             processor=processor,
             model=model,
-            device=configuration.device,
+            device=config.device,
         )
         
         metrics_results = {"loss": avg_val_loss}
         
-        for metric in configuration.metrics:
+        for metric in config.metrics:
             if isinstance(metric, MeanAveragePrecisionMetric):
                 classes = extract_unique_detection_dataset_classes(loader.dataset)
                 targets, predictions = postprocess_florence2_output_for_mean_average_precision(
@@ -308,19 +335,22 @@ def run_validation_epoch(
         display_results(prompts, expected_responses, generated_texts, images)
 
 
-def _establish_training_run_dir(training_dir: str) -> str:
-    training_dir = os.path.abspath(training_dir)
-    existing_directory_entries = glob(os.path.join(training_dir, "*"))
+def _establish_training_run_dir(output_dir: str) -> str:
+    output_dir = os.path.abspath(output_dir)
+    existing_directory_entries = glob(os.path.join(output_dir, "*"))
     subdirectories = [path for path in existing_directory_entries if os.path.isdir(path)]
     run_id = len(subdirectories) + 1
-    training_run_dir = os.path.join(training_dir, str(run_id))
-    os.makedirs(training_run_dir, exist_ok=True)
-    return training_run_dir
+    run_dir = os.path.join(output_dir, str(run_id))
+    os.makedirs(run_dir, exist_ok=True)
+    return run_dir
 
 
-def _get_optimizer(model: PeftModel, configuration: TrainingConfiguration) -> Optimizer:
-    if configuration.optimiser == "adamw":
-        return AdamW(model.parameters(), lr=configuration.learning_rate)
-    if configuration.optimiser == "adam":
-        return Adam(model.parameters(), lr=configuration.learning_rate)
-    return SGD(model.parameters(), lr=configuration.learning_rate)
+def _get_optimizer(model: PeftModel, config: TrainingConfiguration) -> Optimizer:
+    optimizer_type = config.optimizer.lower()
+    if optimizer_type == "adamw":
+        return AdamW(model.parameters(), lr=config.lr)
+    if optimizer_type == "adam":
+        return Adam(model.parameters(), lr=config.lr)
+    if optimizer_type == "sgd":
+        return SGD(model.parameters(), lr=config.lr)
+    raise ValueError(f"Unsupported optimizer: {config.optimizer}")

From 672f27e4397112043cfefab40d3d6eef82e9a117 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 16:52:48 +0200
Subject: [PATCH 55/69] final tests before plugging in CLI

---
 maestro/trainer/common/utils/file_system.py   |  19 +++
 maestro/trainer/models/florence_2/__init__.py |   2 +
 .../florence_2/{training.py => core.py}       |  93 +++++-----
 .../trainer/models/florence_2/entrypoint.py   |  14 +-
 .../trainer/models/florence_2/inference.py    | 159 ------------------
 maestro/trainer/models/florence_2/metrics.py  |   3 +-
 6 files changed, 73 insertions(+), 217 deletions(-)
 rename maestro/trainer/models/florence_2/{training.py => core.py} (85%)
 delete mode 100644 maestro/trainer/models/florence_2/inference.py

diff --git a/maestro/trainer/common/utils/file_system.py b/maestro/trainer/common/utils/file_system.py
index e258f25..da8e5a9 100644
--- a/maestro/trainer/common/utils/file_system.py
+++ b/maestro/trainer/common/utils/file_system.py
@@ -1,5 +1,6 @@
 import json
 import os
+from glob import glob
 from typing import Union, List
 
 
@@ -38,3 +39,21 @@ def save_json(path: str, content: dict) -> None:
 def ensure_parent_dir_exists(path: str) -> None:
     parent_dir = os.path.dirname(os.path.abspath(path))
     os.makedirs(parent_dir, exist_ok=True)
+
+
+def create_new_run_directory(base_output_dir: str) -> str:
+    """
+    Creates a new numbered directory for the current training run.
+
+    Args:
+        base_output_dir (str): The base directory where all run directories are stored.
+
+    Returns:
+        str: The path to the newly created run directory.
+    """
+    base_output_dir = os.path.abspath(base_output_dir)
+    existing_run_dirs = [d for d in glob(os.path.join(base_output_dir, "*")) if os.path.isdir(d)]
+    new_run_number = len(existing_run_dirs) + 1
+    new_run_dir = os.path.join(base_output_dir, str(new_run_number))
+    os.makedirs(new_run_dir, exist_ok=True)
+    return new_run_dir
diff --git a/maestro/trainer/models/florence_2/__init__.py b/maestro/trainer/models/florence_2/__init__.py
index e69de29..836df5c 100644
--- a/maestro/trainer/models/florence_2/__init__.py
+++ b/maestro/trainer/models/florence_2/__init__.py
@@ -0,0 +1,2 @@
+from maestro.trainer.models.florence_2.core import TrainingConfiguration, train
+from maestro.trainer.models.florence_2.metrics import MeanAveragePrecisionMetric
diff --git a/maestro/trainer/models/florence_2/training.py b/maestro/trainer/models/florence_2/core.py
similarity index 85%
rename from maestro/trainer/models/florence_2/training.py
rename to maestro/trainer/models/florence_2/core.py
index 1885a9f..46ea75b 100644
--- a/maestro/trainer/models/florence_2/training.py
+++ b/maestro/trainer/models/florence_2/core.py
@@ -1,6 +1,5 @@
 import os
 from dataclasses import dataclass, field, replace
-from glob import glob
 from typing import List, Literal, Optional, Tuple, Union
 
 import torch
@@ -11,6 +10,7 @@
 from tqdm import tqdm
 from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
+from maestro.trainer.common.utils.file_system import create_new_run_directory
 from maestro.trainer.common.utils.metrics import BaseMetric, MetricsTracker, \
     display_results, save_metric_plots
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
@@ -81,8 +81,8 @@ class TrainingConfiguration:
 
 def train(config: TrainingConfiguration) -> None:
     make_it_reproducible(avoid_non_deterministic_algorithms=False)
-    run_dir = _establish_training_run_dir(
-        output_dir=config.output_dir,
+    run_dir = create_new_run_directory(
+        base_output_dir=config.output_dir,
     )
     config = replace(
         config,
@@ -181,7 +181,7 @@ def run_training_loop(
     checkpoint_manager: CheckpointManager,
 ) -> None:
     train_loader, val_loader = data_loaders
-    optimizer = _get_optimizer(model=model, config=config)
+    optimizer = get_optimizer(model=model, config=config)
     total_steps = config.epochs * len(train_loader)
     lr_scheduler = get_scheduler(
         name=config.lr_scheduler,
@@ -195,7 +195,7 @@ def run_training_loop(
             model=model,
             train_loader=train_loader,
             val_loader=val_loader,
-            epoch_number=epoch + 1,
+            epoch=epoch + 1,
             config=config,
             optimizer=optimizer,
             lr_scheduler=lr_scheduler,
@@ -210,7 +210,7 @@ def run_training_epoch(
     model: PeftModel,
     train_loader: DataLoader,
     val_loader: Optional[DataLoader],
-    epoch_number: int,
+    epoch: int,
     config: TrainingConfiguration,
     optimizer: Optimizer,
     lr_scheduler: LRScheduler,
@@ -220,43 +220,50 @@ def run_training_epoch(
 ) -> None:
     model.train()
     training_losses: List[float] = []
-    training_iterator = tqdm(train_loader, desc=f"Epoch {epoch_number}/{config.epochs}")
-    for step_id, (inputs, answers) in enumerate(training_iterator):
-        input_ids = inputs["input_ids"]
-        pixel_values = inputs["pixel_values"]
-        labels = processor.tokenizer(
-            text=answers, return_tensors="pt", padding=True, return_token_type_ids=False
-        ).input_ids.to(config.device)
-        outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)
-        loss = outputs.loss
-        loss.backward()
-        optimizer.step()
-        lr_scheduler.step()
-        optimizer.zero_grad()
-        loss = loss.item()
-        training_metrics_tracker.register(
-            metric="loss",
-            epoch=epoch_number,
-            step=step_id + 1,
-            value=loss,
-        )
-        training_losses.append(loss)
-        last_100_losses = training_losses[-100:]
-        loss_moving_average = sum(last_100_losses) / len(last_100_losses) if len(last_100_losses) > 0 else 0.0
-        training_iterator.set_description(
-            f"Epoch {epoch_number}/{config.epochs}. Loss: {round(loss_moving_average, 4)}"
-        )
-    if len(training_losses) > 0:
-        avg_train_loss = sum(training_losses) / len(training_losses)
-        print(f"Average Training Loss: {avg_train_loss}")
+    
+    with tqdm(total=len(train_loader), desc=f"Epoch {epoch}/{config.epochs}", unit="batch") as pbar:
+        for step_id, (inputs, answers) in enumerate(train_loader):
+            input_ids = inputs["input_ids"]
+            pixel_values = inputs["pixel_values"]
+            labels = processor.tokenizer(
+                text=answers, 
+                return_tensors="pt", 
+                padding=True, 
+                return_token_type_ids=False
+            ).input_ids.to(config.device)
+            outputs = model(input_ids=input_ids, pixel_values=pixel_values, labels=labels)
+            loss = outputs.loss
+            loss.backward()
+            optimizer.step()
+            lr_scheduler.step()
+            optimizer.zero_grad()
+            loss = loss.item()
+            training_metrics_tracker.register(
+                metric="loss",
+                epoch=epoch,
+                step=step_id + 1,
+                value=loss,
+            )
+            training_losses.append(loss)
+            
+            # Update progress bar
+            last_100_losses = training_losses[-100:]
+            loss_moving_average = sum(last_100_losses) / len(last_100_losses) if last_100_losses else 0.0
+            pbar.set_postfix({"Loss": f"{loss_moving_average:.4f}"})
+            pbar.update(1)
+        
+    # Save checkpoints based on training loss if no validation loader
     if val_loader is None or len(val_loader) == 0:
-        return None
+        train_loss = sum(training_losses) / len(training_losses)
+        checkpoint_manager.save_latest(processor, model)
+        checkpoint_manager.save_best(processor, model, train_loss)
+        return
 
     run_validation_epoch(
         processor=processor,
         model=model,
         loader=val_loader,
-        epoch_number=epoch_number,
+        epoch_number=epoch,
         config=config,
         metrics_tracker=validation_metrics_tracker,
     )
@@ -335,17 +342,7 @@ def run_validation_epoch(
         display_results(prompts, expected_responses, generated_texts, images)
 
 
-def _establish_training_run_dir(output_dir: str) -> str:
-    output_dir = os.path.abspath(output_dir)
-    existing_directory_entries = glob(os.path.join(output_dir, "*"))
-    subdirectories = [path for path in existing_directory_entries if os.path.isdir(path)]
-    run_id = len(subdirectories) + 1
-    run_dir = os.path.join(output_dir, str(run_id))
-    os.makedirs(run_dir, exist_ok=True)
-    return run_dir
-
-
-def _get_optimizer(model: PeftModel, config: TrainingConfiguration) -> Optimizer:
+def get_optimizer(model: PeftModel, config: TrainingConfiguration) -> Optimizer:
     optimizer_type = config.optimizer.lower()
     if optimizer_type == "adamw":
         return AdamW(model.parameters(), lr=config.lr)
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 09c2d87..fd7f7b7 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -1,18 +1,14 @@
 import dataclasses
 from typing import Optional, Annotated
 
+import rich
 import torch
 import typer
-import rich
-
-from maestro.trainer.models.florence_2.entities import (
-    TrainingConfiguration,
-    DEFAULT_FLORENCE2_MODEL_ID,
-    DEFAULT_FLORENCE2_MODEL_REVISION,
-    DEVICE,
-)
 
-from maestro.trainer.models.florence_2.training import train as train_fun
+from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
+    DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
+from maestro.trainer.models.florence_2.core import TrainingConfiguration
+from maestro.trainer.models.florence_2.core import train as train_fun
 
 florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
 
diff --git a/maestro/trainer/models/florence_2/inference.py b/maestro/trainer/models/florence_2/inference.py
deleted file mode 100644
index 4f0c154..0000000
--- a/maestro/trainer/models/florence_2/inference.py
+++ /dev/null
@@ -1,159 +0,0 @@
-from typing import Literal, Optional, Union
-
-import numpy as np
-import supervision as sv
-
-from PIL import Image
-from transformers import AutoProcessor, AutoModelForCausalLM
-
-
-def caption_image(
-    image: Image.Image,
-    processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-    task: Literal["<CAPTION>", "<DETAILED_CAPTION>", "<MORE_DETAILED_CAPTION>", "<VQA>"],
-    prompt: Optional[str] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> str:
-    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
-    model_device = model.device
-    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
-    generated_ids = model.generate(
-        input_ids=inputs["input_ids"],
-        pixel_values=inputs["pixel_values"],
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
-    )
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    response = processor.post_process_generation(generated_text, task=task, image_size=(image.width, image.height))
-    return response[task]
-
-
-TASKS_THAT_REQUIRE_PROMPT = {
-    "<CAPTION_TO_PHRASE_GROUNDING>",
-    "<REFERRING_EXPRESSION_SEGMENTATION>",
-    "<REGION_TO_SEGMENTATION>",
-    "<REGION_TO_CATEGORY>",
-    "<REGION_TO_DESCRIPTION>",
-    "<VQA>",
-}
-
-
-def segment_objects(
-    image: Image.Image,
-    processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-    task: Literal[
-        "<REFERRING_EXPRESSION_SEGMENTATION>",
-        "<REGION_TO_SEGMENTATION>",
-    ],
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> sv.Detections:
-    return _prompt_and_retrieve_detections(
-        image=image,
-        processor=processor,
-        model=model,
-        task=task,
-        prompt=prompt,
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
-    )
-
-
-def detect_objects(
-    image: Image.Image,
-    processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-    task: Literal[
-        "<OD>",
-        "<DENSE_REGION_CAPTION>",
-        "<REGION_PROPOSAL>",
-        "<OCR_WITH_REGION>",
-        "<OPEN_VOCABULARY_DETECTION>",
-        "<REGION_TO_CATEGORY>",
-        "<REGION_TO_DESCRIPTION>",
-    ],
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> sv.Detections:
-    return _prompt_and_retrieve_detections(
-        image=image,
-        processor=processor,
-        model=model,
-        task=task,
-        prompt=prompt,
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
-    )
-
-
-def _prompt_and_retrieve_detections(
-    image: Image.Image,
-    processor: AutoProcessor,
-    model: AutoModelForCausalLM,
-    task: Literal[
-        "<OD>",
-        "<DENSE_REGION_CAPTION>",
-        "<REGION_PROPOSAL>",
-        "<OCR_WITH_REGION>",
-        "<OPEN_VOCABULARY_DETECTION>",
-        "<REGION_TO_CATEGORY>",
-        "<REGION_TO_DESCRIPTION>",
-        "<REFERRING_EXPRESSION_SEGMENTATION>",
-        "<REGION_TO_SEGMENTATION>",
-    ],
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-    max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    num_beams: int = 3,
-) -> sv.Detections:
-    prompt = _pre_process_prompt(image=image, task=task, prompt=prompt)
-    model_device = model.device
-    inputs = processor(text=prompt, images=image, return_tensors="pt").to(model_device)
-    generated_ids = model.generate(
-        input_ids=inputs["input_ids"],
-        pixel_values=inputs["pixel_values"],
-        max_new_tokens=max_new_tokens,
-        do_sample=do_sample,
-        num_beams=num_beams,
-    )
-    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
-    response = processor.post_process_generation(
-        generated_text,
-        task=task,
-        image_size=(image.width, image.height),
-    )
-    return sv.Detections.from_lmm(
-        lmm=sv.LMM.FLORENCE_2,
-        result=response,
-        resolution_wh=image.size,
-    )
-
-
-def _pre_process_prompt(
-    image: Image.Image,
-    task: str,
-    prompt: Optional[Union[str, tuple, list, np.ndarray]] = None,
-) -> str:
-    if prompt is None:
-        if task in TASKS_THAT_REQUIRE_PROMPT:
-            raise ValueError(f"Task {task} requires prompt")
-        return task
-    if isinstance(prompt, tuple) or isinstance(prompt, list) or isinstance(prompt, np.ndarray):
-        if len(prompt) != 4:
-            raise ValueError("Expected sequence of 4 elements describing (x_min, y_min, x_max, y_max)")
-        x_min, y_min, x_max, y_max = prompt
-        x_min, x_max = round((x_min / image.width) * 1000), round((x_max / image.width) * 1000)
-        y_min, y_max = round((y_min / image.height) * 1000), round((y_max / image.height) * 1000)
-        return f"{task} <loc_{x_min}><loc_{y_min}><loc_{x_max}><loc_{y_max}>"
-    return f"{task} {prompt}"
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index ae01b38..117e031 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -50,7 +50,8 @@ def postprocess_florence2_output_for_mean_average_precision(
         prediction = sv.Detections.from_lmm(sv.LMM.FLORENCE_2, prediction, resolution_wh=image.size)
         prediction = prediction[np.isin(prediction["class_name"], classes)]
         prediction.class_id = np.array([classes.index(class_name) for class_name in prediction["class_name"]])
-        prediction.confidence = np.ones(len(prediction))  # Set confidence for mean average precision calculation
+        # Set confidence for mean average precision calculation
+        prediction.confidence = np.ones(len(prediction))
         
         # Postprocess target for mean average precision calculation
         target = processor.post_process_generation(suffix, task="<OD>", image_size=image.size)

From 4a339a43c6b8c8bae89ecefdecc903981faed31a Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 17:49:40 +0200
Subject: [PATCH 56/69] initial tests of CLI mode

---
 maestro/cli/__init__.py                       |   5 +
 maestro/cli/env.py                            |   4 +-
 maestro/cli/introspection.py                  |  56 +--
 maestro/cli/main.py                           |  18 +-
 maestro/cli/utils.py                          |   4 +-
 .../trainer/models/florence_2/entrypoint.py   | 346 ++++++++++--------
 6 files changed, 247 insertions(+), 186 deletions(-)

diff --git a/maestro/cli/__init__.py b/maestro/cli/__init__.py
index e69de29..d627b18 100644
--- a/maestro/cli/__init__.py
+++ b/maestro/cli/__init__.py
@@ -0,0 +1,5 @@
+import typer
+from maestro.trainer.models.florence_2.entrypoint import app as florence2_app
+
+app = typer.Typer()
+app.add_typer(florence2_app, name="florence2")
diff --git a/maestro/cli/env.py b/maestro/cli/env.py
index b95525e..5322a5d 100644
--- a/maestro/cli/env.py
+++ b/maestro/cli/env.py
@@ -1,2 +1,2 @@
-DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "DISABLE_RECIPE_IMPORTS_WARNINGS"
-DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "False"
+# DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "DISABLE_RECIPE_IMPORTS_WARNINGS"
+# DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "False"
diff --git a/maestro/cli/introspection.py b/maestro/cli/introspection.py
index fc6aef9..6223144 100644
--- a/maestro/cli/introspection.py
+++ b/maestro/cli/introspection.py
@@ -1,37 +1,37 @@
-import os
+# import os
 
-import typer
+# import typer
 
-from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, \
-    DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
-from maestro.cli.utils import str2bool
+# from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, \
+#     DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
+# from maestro.cli.utils import str2bool
 
 
-def find_training_recipes(app: typer.Typer) -> None:
-    try:
-        from maestro.trainer.models.florence_2.entrypoint import florence_2_app
+# def find_training_recipes(app: typer.Typer) -> None:
+#     try:
+#         from maestro.trainer.models.florence_2.entrypoint import florence_2_app
 
-        app.add_typer(florence_2_app, name="florence2")
-    except Exception:
-        _warn_about_recipe_import_error(model_name="Florence 2")
+#         app.add_typer(florence_2_app, name="florence2")
+#     except Exception:
+#         _warn_about_recipe_import_error(model_name="Florence 2")
 
-    try:
-        from maestro.trainer.models.paligemma.entrypoint import paligemma_app
+#     try:
+#         from maestro.trainer.models.paligemma.entrypoint import paligemma_app
 
-        app.add_typer(paligemma_app, name="paligemma")
-    except Exception:
-        _warn_about_recipe_import_error(model_name="PaliGemma")
+#         app.add_typer(paligemma_app, name="paligemma")
+#     except Exception:
+#         _warn_about_recipe_import_error(model_name="PaliGemma")
 
 
-def _warn_about_recipe_import_error(model_name: str) -> None:
-    disable_warnings = str2bool(
-        os.getenv(
-            DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
-            DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
-        )
-    )
-    if disable_warnings:
-        return None
-    warning = typer.style("WARNING", fg=typer.colors.RED, bold=True)
-    message = "🚧 " + warning + f" cannot import recipe for {model_name}"
-    typer.echo(message)
+# def _warn_about_recipe_import_error(model_name: str) -> None:
+#     disable_warnings = str2bool(
+#         os.getenv(
+#             DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+#             DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+#         )
+#     )
+#     if disable_warnings:
+#         return None
+#     warning = typer.style("WARNING", fg=typer.colors.RED, bold=True)
+#     message = "🚧 " + warning + f" cannot import recipe for {model_name}"
+#     typer.echo(message)
diff --git a/maestro/cli/main.py b/maestro/cli/main.py
index b600e3a..9212e72 100644
--- a/maestro/cli/main.py
+++ b/maestro/cli/main.py
@@ -1,15 +1,15 @@
-import typer
+# import typer
 
-from maestro.cli.introspection import find_training_recipes
+# from maestro.cli.introspection import find_training_recipes
 
-app = typer.Typer()
-find_training_recipes(app=app)
+# app = typer.Typer()
+# find_training_recipes(app=app)
 
 
-@app.command(help="Display information about maestro")
-def info():
-    typer.echo("Welcome to maestro CLI. Let's train some VLM! 🏋")
+# @app.command(help="Display information about maestro")
+# def info():
+#     typer.echo("Welcome to maestro CLI. Let's train some VLM! 🏋")
 
 
-if __name__ == "__main__":
-    app()
+# if __name__ == "__main__":
+#     app()
diff --git a/maestro/cli/utils.py b/maestro/cli/utils.py
index 0751fef..6d31bd9 100644
--- a/maestro/cli/utils.py
+++ b/maestro/cli/utils.py
@@ -1,2 +1,2 @@
-def str2bool(value: str) -> bool:
-    return value.lower() in {"y", "t", "yes", "true"}
+# def str2bool(value: str) -> bool:
+#     return value.lower() in {"y", "t", "yes", "true"}
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index fd7f7b7..80c931f 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -1,148 +1,204 @@
-import dataclasses
-from typing import Optional, Annotated
+# import dataclasses
+# from typing import Optional, Annotated
+
+# import rich
+# import torch
+# import typer
+
+# from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
+#     DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
+# from maestro.trainer.models.florence_2.core import TrainingConfiguration
+# from maestro.trainer.models.florence_2.core import train as train_fun
+
+# florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
+
+
+# @florence_2_app.command(
+#     help="Train Florence 2 model", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
+# )
+# def train(
+#     dataset_location: Annotated[
+#         str,
+#         typer.Option("--dataset_location", help="Path to directory with dataset"),
+#     ],
+#     model_id_or_path: Annotated[
+#         str,
+#         typer.Option("--model_id_or_path", help="Model to be used or path to your checkpoint"),
+#     ] = DEFAULT_FLORENCE2_MODEL_ID,
+#     revision: Annotated[
+#         str,
+#         typer.Option("--revision", help="Revision of Florence2 HF repository"),
+#     ] = DEFAULT_FLORENCE2_MODEL_REVISION,
+#     device: Annotated[
+#         str,
+#         typer.Option("--device", help="CUDA device ID to be used (in format: 'cuda:0')"),
+#     ] = DEVICE,
+#     transformers_cache_dir: Annotated[
+#         Optional[str],
+#         typer.Option("--transformers_cache_dir", help="Cache dir for HF weights"),
+#     ] = None,
+#     training_epochs: Annotated[
+#         int,
+#         typer.Option("--training_epochs", help="Number of training epochs"),
+#     ] = 10,
+#     optimiser: Annotated[
+#         str,
+#         typer.Option("--optimiser", help="Optimiser to be used"),
+#     ] = "adamw",
+#     learning_rate: Annotated[
+#         float,
+#         typer.Option("--learning_rate", help="Learning rate"),
+#     ] = 1e-5,
+#     lr_scheduler: Annotated[
+#         str,
+#         typer.Option("--lr_scheduler", help="LR scheduler"),
+#     ] = "linear",
+#     train_batch_size: Annotated[
+#         int,
+#         typer.Option("--train_batch_size", help="Batch size for training"),
+#     ] = 4,
+#     test_batch_size: Annotated[
+#         Optional[int],
+#         typer.Option(
+#             "--train_batch_size", help="Batch size for validation and test. If not given - train will be used."
+#         ),
+#     ] = None,
+#     loaders_workers: Annotated[
+#         int,
+#         typer.Option("--loaders_workers", help="Number of loaders workers. 0 = # of CPU"),
+#     ] = 0,
+#     test_loaders_workers: Annotated[
+#         Optional[int],
+#         typer.Option(
+#             "--test_loaders_workers",
+#             help="Number of workers for test and val loaders. If not given - train will be used.",
+#         ),
+#     ] = None,
+#     lora_r: Annotated[
+#         int,
+#         typer.Option("--lora_r", help="Value of Lora R"),
+#     ] = 8,
+#     lora_alpha: Annotated[
+#         int,
+#         typer.Option("--lora_alpha", help="Value of Lora Alpha"),
+#     ] = 8,
+#     lora_dropout: Annotated[
+#         float,
+#         typer.Option("--lora_dropout", help="Value of Lora Dropout"),
+#     ] = 0.05,
+#     bias: Annotated[
+#         str,
+#         typer.Option("--bias", help="Value of Lora Bias"),
+#     ] = "none",
+#     use_rslora: Annotated[
+#         bool,
+#         typer.Option(
+#             "--use_rslora/--no_use_rslora",
+#             help="Boolean flag to decide if rslora to be used",
+#         ),
+#     ] = True,
+#     init_lora_weights: Annotated[
+#         str,
+#         typer.Option("--init_lora_weights", help="Lora weights initialisation"),
+#     ] = "gaussian",
+#     training_dir: Annotated[
+#         str,
+#         typer.Option("--training_dir", help="Path to directory where training outputs should be preserved"),
+#     ] = "./training/florence-2",
+#     max_checkpoints_to_keep: Annotated[
+#         int,
+#         typer.Option("--max_checkpoints_to_keep", help="Max checkpoints to keep"),
+#     ] = 3,
+#     num_samples_to_visualise: Annotated[
+#         int,
+#         typer.Option("--num_samples_to_visualise", help="Number of samples to visualise"),
+#     ] = 64,
+# ) -> None:
+#     configuration = TrainingConfiguration(
+#         dataset_location=dataset_location,
+#         model_id_or_path=model_id_or_path,
+#         revision=revision,
+#         device=torch.device(device),
+#         transformers_cache_dir=transformers_cache_dir,
+#         training_epochs=training_epochs,
+#         optimiser=optimiser,  # type: ignore
+#         learning_rate=learning_rate,
+#         lr_scheduler=lr_scheduler,  # type: ignore
+#         train_batch_size=train_batch_size,
+#         test_batch_size=test_batch_size,
+#         loaders_workers=loaders_workers,
+#         test_loaders_workers=test_loaders_workers,
+#         lora_r=lora_r,
+#         lora_alpha=lora_alpha,
+#         lora_dropout=lora_dropout,
+#         bias=bias,  # type: ignore
+#         use_rslora=use_rslora,
+#         init_lora_weights=init_lora_weights,  # type: ignore
+#         training_dir=training_dir,
+#         max_checkpoints_to_keep=max_checkpoints_to_keep,
+#         num_samples_to_visualise=num_samples_to_visualise,
+#     )
+#     typer.echo(typer.style("Training configuration", fg=typer.colors.BRIGHT_GREEN, bold=True))
+#     rich.print(dataclasses.asdict(configuration))
+#     train_fun(configuration=configuration)
+
+
+# @florence_2_app.command(help="Evaluate Florence 2 model")
+# def evaluate() -> None:
+#     pass
 
-import rich
-import torch
 import typer
+from typing import get_type_hints, Optional, Union, Literal
+from maestro.trainer.models.florence_2.core import TrainingConfiguration, train
+
+app = typer.Typer()
+
+
+def create_dynamic_cli_options(config_class):
+    hints = get_type_hints(config_class)
+    options = {}
+    
+    for field_name, field_type in hints.items():
+        if field_name == 'metrics':  # Skip complex types like metrics
+            continue
+        
+        if field_type == bool:
+            options[field_name] = typer.Option(None, help=f"{field_name} parameter")
+        elif field_type in (int, float, str):
+            options[field_name] = typer.Option(None, help=f"{field_name} parameter")
+        elif getattr(field_type, "__origin__", None) == Union:
+            if type(None) in field_type.__args__:
+                options[field_name] = typer.Option(None, help=f"{field_name} parameter")
+        elif getattr(field_type, "__origin__", None) == Literal:
+            options[field_name] = typer.Option(None, help=f"{field_name} parameter")
+    
+    return options
+
+
+dynamic_options = create_dynamic_cli_options(TrainingConfiguration)
+
+
+@app.command()
+def florence2(
+    mode: str = typer.Option(..., help="Mode: 'train' or 'eval'"),
+    **dynamic_options
+):
+    """Train or evaluate a Florence-2 model."""
+    
+    # Filter out None values
+    config_overrides = {k: v for k, v in dynamic_options.items() if v is not None}
+    
+    # Create configuration with overrides
+    config = TrainingConfiguration(**config_overrides)
+    
+    if mode == "train":
+        train(config)
+    elif mode == "eval":
+        typer.echo("Evaluation not implemented yet.")
+    else:
+        typer.echo(f"Invalid mode: {mode}. Use 'train' or 'eval'.")
+        raise typer.Exit(code=1)
+
 
-from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
-    DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
-from maestro.trainer.models.florence_2.core import TrainingConfiguration
-from maestro.trainer.models.florence_2.core import train as train_fun
-
-florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
-
-
-@florence_2_app.command(
-    help="Train Florence 2 model", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
-)
-def train(
-    dataset_location: Annotated[
-        str,
-        typer.Option("--dataset_location", help="Path to directory with dataset"),
-    ],
-    model_id_or_path: Annotated[
-        str,
-        typer.Option("--model_id_or_path", help="Model to be used or path to your checkpoint"),
-    ] = DEFAULT_FLORENCE2_MODEL_ID,
-    revision: Annotated[
-        str,
-        typer.Option("--revision", help="Revision of Florence2 HF repository"),
-    ] = DEFAULT_FLORENCE2_MODEL_REVISION,
-    device: Annotated[
-        str,
-        typer.Option("--device", help="CUDA device ID to be used (in format: 'cuda:0')"),
-    ] = DEVICE,
-    transformers_cache_dir: Annotated[
-        Optional[str],
-        typer.Option("--transformers_cache_dir", help="Cache dir for HF weights"),
-    ] = None,
-    training_epochs: Annotated[
-        int,
-        typer.Option("--training_epochs", help="Number of training epochs"),
-    ] = 10,
-    optimiser: Annotated[
-        str,
-        typer.Option("--optimiser", help="Optimiser to be used"),
-    ] = "adamw",
-    learning_rate: Annotated[
-        float,
-        typer.Option("--learning_rate", help="Learning rate"),
-    ] = 1e-5,
-    lr_scheduler: Annotated[
-        str,
-        typer.Option("--lr_scheduler", help="LR scheduler"),
-    ] = "linear",
-    train_batch_size: Annotated[
-        int,
-        typer.Option("--train_batch_size", help="Batch size for training"),
-    ] = 4,
-    test_batch_size: Annotated[
-        Optional[int],
-        typer.Option(
-            "--train_batch_size", help="Batch size for validation and test. If not given - train will be used."
-        ),
-    ] = None,
-    loaders_workers: Annotated[
-        int,
-        typer.Option("--loaders_workers", help="Number of loaders workers. 0 = # of CPU"),
-    ] = 0,
-    test_loaders_workers: Annotated[
-        Optional[int],
-        typer.Option(
-            "--test_loaders_workers",
-            help="Number of workers for test and val loaders. If not given - train will be used.",
-        ),
-    ] = None,
-    lora_r: Annotated[
-        int,
-        typer.Option("--lora_r", help="Value of Lora R"),
-    ] = 8,
-    lora_alpha: Annotated[
-        int,
-        typer.Option("--lora_alpha", help="Value of Lora Alpha"),
-    ] = 8,
-    lora_dropout: Annotated[
-        float,
-        typer.Option("--lora_dropout", help="Value of Lora Dropout"),
-    ] = 0.05,
-    bias: Annotated[
-        str,
-        typer.Option("--bias", help="Value of Lora Bias"),
-    ] = "none",
-    use_rslora: Annotated[
-        bool,
-        typer.Option(
-            "--use_rslora/--no_use_rslora",
-            help="Boolean flag to decide if rslora to be used",
-        ),
-    ] = True,
-    init_lora_weights: Annotated[
-        str,
-        typer.Option("--init_lora_weights", help="Lora weights initialisation"),
-    ] = "gaussian",
-    training_dir: Annotated[
-        str,
-        typer.Option("--training_dir", help="Path to directory where training outputs should be preserved"),
-    ] = "./training/florence-2",
-    max_checkpoints_to_keep: Annotated[
-        int,
-        typer.Option("--max_checkpoints_to_keep", help="Max checkpoints to keep"),
-    ] = 3,
-    num_samples_to_visualise: Annotated[
-        int,
-        typer.Option("--num_samples_to_visualise", help="Number of samples to visualise"),
-    ] = 64,
-) -> None:
-    configuration = TrainingConfiguration(
-        dataset_location=dataset_location,
-        model_id_or_path=model_id_or_path,
-        revision=revision,
-        device=torch.device(device),
-        transformers_cache_dir=transformers_cache_dir,
-        training_epochs=training_epochs,
-        optimiser=optimiser,  # type: ignore
-        learning_rate=learning_rate,
-        lr_scheduler=lr_scheduler,  # type: ignore
-        train_batch_size=train_batch_size,
-        test_batch_size=test_batch_size,
-        loaders_workers=loaders_workers,
-        test_loaders_workers=test_loaders_workers,
-        lora_r=lora_r,
-        lora_alpha=lora_alpha,
-        lora_dropout=lora_dropout,
-        bias=bias,  # type: ignore
-        use_rslora=use_rslora,
-        init_lora_weights=init_lora_weights,  # type: ignore
-        training_dir=training_dir,
-        max_checkpoints_to_keep=max_checkpoints_to_keep,
-        num_samples_to_visualise=num_samples_to_visualise,
-    )
-    typer.echo(typer.style("Training configuration", fg=typer.colors.BRIGHT_GREEN, bold=True))
-    rich.print(dataclasses.asdict(configuration))
-    train_fun(configuration=configuration)
-
-
-@florence_2_app.command(help="Evaluate Florence 2 model")
-def evaluate() -> None:
-    pass
+if __name__ == "__main__":
+    app()

From c7c63b7ffb92557132c032598ea3b81cf6075d82 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 18:30:16 +0200
Subject: [PATCH 57/69] fix

---
 maestro/cli/__init__.py | 4 ----
 maestro/cli/main.py     | 9 +++++++++
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/maestro/cli/__init__.py b/maestro/cli/__init__.py
index d627b18..8b13789 100644
--- a/maestro/cli/__init__.py
+++ b/maestro/cli/__init__.py
@@ -1,5 +1 @@
-import typer
-from maestro.trainer.models.florence_2.entrypoint import app as florence2_app
 
-app = typer.Typer()
-app.add_typer(florence2_app, name="florence2")
diff --git a/maestro/cli/main.py b/maestro/cli/main.py
index 9212e72..5481b24 100644
--- a/maestro/cli/main.py
+++ b/maestro/cli/main.py
@@ -13,3 +13,12 @@
 
 # if __name__ == "__main__":
 #     app()
+
+import typer
+from maestro.trainer.models.florence_2.entrypoint import app as florence2_app
+
+app = typer.Typer()
+app.add_typer(florence2_app, name="florence2")
+
+if __name__ == "__main__":
+    app()

From 5cc422014ea9746bde67b1d1434dcc4c3ffdd5fe Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 19:15:13 +0200
Subject: [PATCH 58/69] fix `No such option: --mode `

---
 .../trainer/models/florence_2/entrypoint.py   | 26 ++++++++-----------
 1 file changed, 11 insertions(+), 15 deletions(-)

diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 80c931f..284ebb6 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -148,8 +148,8 @@
 #     pass
 
 import typer
-from typing import get_type_hints, Optional, Union, Literal
-from maestro.trainer.models.florence_2.core import TrainingConfiguration, train
+from typing import get_type_hints, Union, Literal
+from maestro.trainer.models.florence_2.core import TrainingConfiguration, train as train_florence2
 
 app = typer.Typer()
 
@@ -179,25 +179,21 @@ def create_dynamic_cli_options(config_class):
 
 
 @app.command()
-def florence2(
-    mode: str = typer.Option(..., help="Mode: 'train' or 'eval'"),
-    **dynamic_options
-):
-    """Train or evaluate a Florence-2 model."""
-    
+def train(**dynamic_options):
+    """Train a Florence-2 model."""
     # Filter out None values
     config_overrides = {k: v for k, v in dynamic_options.items() if v is not None}
     
     # Create configuration with overrides
     config = TrainingConfiguration(**config_overrides)
     
-    if mode == "train":
-        train(config)
-    elif mode == "eval":
-        typer.echo("Evaluation not implemented yet.")
-    else:
-        typer.echo(f"Invalid mode: {mode}. Use 'train' or 'eval'.")
-        raise typer.Exit(code=1)
+    train_florence2(config)
+
+
+@app.command()
+def evaluate(**dynamic_options):
+    """Evaluate a Florence-2 model."""
+    typer.echo("Evaluation not implemented yet.")
 
 
 if __name__ == "__main__":

From 518323c2a6731a665ac83ba60491d09ec54bb7d2 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 19:21:03 +0200
Subject: [PATCH 59/69] fix 2 `No such option: --mode `

---
 .../trainer/models/florence_2/entrypoint.py    | 18 +++++++++++-------
 1 file changed, 11 insertions(+), 7 deletions(-)

diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 284ebb6..da811e6 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -153,7 +153,6 @@
 
 app = typer.Typer()
 
-
 def create_dynamic_cli_options(config_class):
     hints = get_type_hints(config_class)
     options = {}
@@ -174,11 +173,19 @@ def create_dynamic_cli_options(config_class):
     
     return options
 
-
 dynamic_options = create_dynamic_cli_options(TrainingConfiguration)
 
-
 @app.command()
+def main(mode: str, **dynamic_options):
+    """Main entry point for Florence-2 model."""
+    if mode == "train":
+        train(**dynamic_options)
+    elif mode == "evaluate":
+        evaluate(**dynamic_options)
+    else:
+        typer.echo(f"Unknown mode: {mode}")
+        raise typer.Exit(code=1)
+
 def train(**dynamic_options):
     """Train a Florence-2 model."""
     # Filter out None values
@@ -189,12 +196,9 @@ def train(**dynamic_options):
     
     train_florence2(config)
 
-
-@app.command()
 def evaluate(**dynamic_options):
     """Evaluate a Florence-2 model."""
     typer.echo("Evaluation not implemented yet.")
 
-
 if __name__ == "__main__":
-    app()
+    app()
\ No newline at end of file

From fb212ead9e03c0842e3a83d0bd3bfe8d1d69f877 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 19:39:17 +0200
Subject: [PATCH 60/69] fix 3 `No such option: --mode `

---
 .../trainer/models/florence_2/entrypoint.py   | 81 ++++++++++++-------
 1 file changed, 52 insertions(+), 29 deletions(-)

diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index da811e6..3778f72 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -148,55 +148,78 @@
 #     pass
 
 import typer
-from typing import get_type_hints, Union, Literal
+from typing import Optional, List, Union, Literal
 from maestro.trainer.models.florence_2.core import TrainingConfiguration, train as train_florence2
 
 app = typer.Typer()
 
-def create_dynamic_cli_options(config_class):
-    hints = get_type_hints(config_class)
-    options = {}
-    
-    for field_name, field_type in hints.items():
-        if field_name == 'metrics':  # Skip complex types like metrics
-            continue
-        
-        if field_type == bool:
-            options[field_name] = typer.Option(None, help=f"{field_name} parameter")
-        elif field_type in (int, float, str):
-            options[field_name] = typer.Option(None, help=f"{field_name} parameter")
-        elif getattr(field_type, "__origin__", None) == Union:
-            if type(None) in field_type.__args__:
-                options[field_name] = typer.Option(None, help=f"{field_name} parameter")
-        elif getattr(field_type, "__origin__", None) == Literal:
-            options[field_name] = typer.Option(None, help=f"{field_name} parameter")
-    
-    return options
-
-dynamic_options = create_dynamic_cli_options(TrainingConfiguration)
-
 @app.command()
-def main(mode: str, **dynamic_options):
+def main(
+    mode: str = typer.Option(..., help="Mode to run: train or evaluate"),
+    dataset_path: str = typer.Option(..., help="Path to the dataset used for training"),
+    model_id: str = typer.Option(None, help="Identifier for the Florence-2 model"),
+    revision: str = typer.Option(None, help="Revision of the model to use"),
+    device: str = typer.Option(None, help="Device to use for training"),
+    cache_dir: Optional[str] = typer.Option(None, help="Directory to cache the model"),
+    epochs: int = typer.Option(10, help="Number of training epochs"),
+    optimizer: str = typer.Option("adamw", help="Optimizer to use for training"),
+    lr: float = typer.Option(1e-5, help="Learning rate for the optimizer"),
+    lr_scheduler: str = typer.Option("linear", help="Learning rate scheduler"),
+    batch_size: int = typer.Option(4, help="Batch size for training"),
+    val_batch_size: Optional[int] = typer.Option(None, help="Batch size for validation"),
+    num_workers: int = typer.Option(0, help="Number of workers for data loading"),
+    val_num_workers: Optional[int] = typer.Option(None, help="Number of workers for validation data loading"),
+    lora_r: int = typer.Option(8, help="Rank of the LoRA update matrices"),
+    lora_alpha: int = typer.Option(8, help="Scaling factor for the LoRA update"),
+    lora_dropout: float = typer.Option(0.05, help="Dropout probability for LoRA layers"),
+    bias: str = typer.Option("none", help="Which bias to train"),
+    use_rslora: bool = typer.Option(True, help="Whether to use RSLoRA"),
+    init_lora_weights: str = typer.Option("gaussian", help="How to initialize LoRA weights"),
+    output_dir: str = typer.Option("./training/florence-2", help="Directory to save output files"),
+    metrics: List[str] = typer.Option([], help="List of metrics to track during training")
+):
     """Main entry point for Florence-2 model."""
     if mode == "train":
-        train(**dynamic_options)
+        train(
+            dataset_path=dataset_path,
+            model_id=model_id,
+            revision=revision,
+            device=device,
+            cache_dir=cache_dir,
+            epochs=epochs,
+            optimizer=optimizer,
+            lr=lr,
+            lr_scheduler=lr_scheduler,
+            batch_size=batch_size,
+            val_batch_size=val_batch_size,
+            num_workers=num_workers,
+            val_num_workers=val_num_workers,
+            lora_r=lora_r,
+            lora_alpha=lora_alpha,
+            lora_dropout=lora_dropout,
+            bias=bias,
+            use_rslora=use_rslora,
+            init_lora_weights=init_lora_weights,
+            output_dir=output_dir,
+            metrics=metrics
+        )
     elif mode == "evaluate":
-        evaluate(**dynamic_options)
+        evaluate()
     else:
         typer.echo(f"Unknown mode: {mode}")
         raise typer.Exit(code=1)
 
-def train(**dynamic_options):
+def train(**kwargs):
     """Train a Florence-2 model."""
     # Filter out None values
-    config_overrides = {k: v for k, v in dynamic_options.items() if v is not None}
+    config_overrides = {k: v for k, v in kwargs.items() if v is not None}
     
     # Create configuration with overrides
     config = TrainingConfiguration(**config_overrides)
     
     train_florence2(config)
 
-def evaluate(**dynamic_options):
+def evaluate():
     """Evaluate a Florence-2 model."""
     typer.echo("Evaluation not implemented yet.")
 

From f15b7a9134b2a9befc76ed31f184cdd236a6f59c Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 19:45:30 +0200
Subject: [PATCH 61/69] fix 4 `No such option: --mode `

---
 maestro/trainer/models/florence_2/entrypoint.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 3778f72..92f83d5 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -148,12 +148,13 @@
 #     pass
 
 import typer
-from typing import Optional, List, Union, Literal
+from typing import Optional, List
 from maestro.trainer.models.florence_2.core import TrainingConfiguration, train as train_florence2
 
 app = typer.Typer()
 
-@app.command()
+
+@app.callback()
 def main(
     mode: str = typer.Option(..., help="Mode to run: train or evaluate"),
     dataset_path: str = typer.Option(..., help="Path to the dataset used for training"),
@@ -209,6 +210,7 @@ def main(
         typer.echo(f"Unknown mode: {mode}")
         raise typer.Exit(code=1)
 
+
 def train(**kwargs):
     """Train a Florence-2 model."""
     # Filter out None values
@@ -219,9 +221,11 @@ def train(**kwargs):
     
     train_florence2(config)
 
+
 def evaluate():
     """Evaluate a Florence-2 model."""
     typer.echo("Evaluation not implemented yet.")
 
+
 if __name__ == "__main__":
     app()
\ No newline at end of file

From 566d9ca773295f3d6711c197135a0106488bf8ab Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 19:51:54 +0200
Subject: [PATCH 62/69] fix 5 `No such option: --mode `

---
 maestro/cli/main.py                             |  2 +-
 maestro/trainer/models/florence_2/entrypoint.py | 13 +++----------
 2 files changed, 4 insertions(+), 11 deletions(-)

diff --git a/maestro/cli/main.py b/maestro/cli/main.py
index 5481b24..3ef8500 100644
--- a/maestro/cli/main.py
+++ b/maestro/cli/main.py
@@ -15,7 +15,7 @@
 #     app()
 
 import typer
-from maestro.trainer.models.florence_2.entrypoint import app as florence2_app
+from maestro.trainer.models.florence_2.entrypoint import florence2_app
 
 app = typer.Typer()
 app.add_typer(florence2_app, name="florence2")
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 92f83d5..31ea38c 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -148,13 +148,12 @@
 #     pass
 
 import typer
-from typing import Optional, List
+from typing import Optional, List, Union, Literal
 from maestro.trainer.models.florence_2.core import TrainingConfiguration, train as train_florence2
 
-app = typer.Typer()
+florence2_app = typer.Typer()
 
-
-@app.callback()
+@florence2_app.command()
 def main(
     mode: str = typer.Option(..., help="Mode to run: train or evaluate"),
     dataset_path: str = typer.Option(..., help="Path to the dataset used for training"),
@@ -210,7 +209,6 @@ def main(
         typer.echo(f"Unknown mode: {mode}")
         raise typer.Exit(code=1)
 
-
 def train(**kwargs):
     """Train a Florence-2 model."""
     # Filter out None values
@@ -221,11 +219,6 @@ def train(**kwargs):
     
     train_florence2(config)
 
-
 def evaluate():
     """Evaluate a Florence-2 model."""
     typer.echo("Evaluation not implemented yet.")
-
-
-if __name__ == "__main__":
-    app()
\ No newline at end of file

From fb1c8268a66b78c68c2942dc4711e304e169915a Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Tue, 10 Sep 2024 19:57:14 +0200
Subject: [PATCH 63/69] fix 6 `No such option: --mode `

---
 maestro/cli/main.py                             | 6 ++++--
 maestro/trainer/models/florence_2/entrypoint.py | 9 ++++++---
 2 files changed, 10 insertions(+), 5 deletions(-)

diff --git a/maestro/cli/main.py b/maestro/cli/main.py
index 3ef8500..8e2edc9 100644
--- a/maestro/cli/main.py
+++ b/maestro/cli/main.py
@@ -15,10 +15,12 @@
 #     app()
 
 import typer
-from maestro.trainer.models.florence_2.entrypoint import florence2_app
+from maestro.trainer.models.florence_2.entrypoint import florence2
 
 app = typer.Typer()
-app.add_typer(florence2_app, name="florence2")
+
+# Add the florence2 command to the main app
+app.command()(florence2)
 
 if __name__ == "__main__":
     app()
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 31ea38c..30204ce 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -151,10 +151,10 @@
 from typing import Optional, List, Union, Literal
 from maestro.trainer.models.florence_2.core import TrainingConfiguration, train as train_florence2
 
-florence2_app = typer.Typer()
+app = typer.Typer()
 
-@florence2_app.command()
-def main(
+@app.command()
+def florence2(
     mode: str = typer.Option(..., help="Mode to run: train or evaluate"),
     dataset_path: str = typer.Option(..., help="Path to the dataset used for training"),
     model_id: str = typer.Option(None, help="Identifier for the Florence-2 model"),
@@ -222,3 +222,6 @@ def train(**kwargs):
 def evaluate():
     """Evaluate a Florence-2 model."""
     typer.echo("Evaluation not implemented yet.")
+
+if __name__ == "__main__":
+    app()

From d556a884e4ca7da9d7cdd29984a9e2e9bb243aee Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 11 Sep 2024 00:01:21 +0200
Subject: [PATCH 64/69] bring back Pawel's code with improvements

---
 maestro/cli/env.py                            |   4 +-
 maestro/cli/introspection.py                  |  56 +--
 maestro/cli/main.py                           |  25 +-
 maestro/cli/utils.py                          |   4 +-
 maestro/trainer/models/florence_2/core.py     |  45 +--
 .../trainer/models/florence_2/entrypoint.py   | 353 +++++++-----------
 6 files changed, 196 insertions(+), 291 deletions(-)

diff --git a/maestro/cli/env.py b/maestro/cli/env.py
index 5322a5d..b95525e 100644
--- a/maestro/cli/env.py
+++ b/maestro/cli/env.py
@@ -1,2 +1,2 @@
-# DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "DISABLE_RECIPE_IMPORTS_WARNINGS"
-# DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "False"
+DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "DISABLE_RECIPE_IMPORTS_WARNINGS"
+DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV = "False"
diff --git a/maestro/cli/introspection.py b/maestro/cli/introspection.py
index 6223144..fc6aef9 100644
--- a/maestro/cli/introspection.py
+++ b/maestro/cli/introspection.py
@@ -1,37 +1,37 @@
-# import os
+import os
 
-# import typer
+import typer
 
-# from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, \
-#     DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
-# from maestro.cli.utils import str2bool
+from maestro.cli.env import DISABLE_RECIPE_IMPORTS_WARNINGS_ENV, \
+    DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV
+from maestro.cli.utils import str2bool
 
 
-# def find_training_recipes(app: typer.Typer) -> None:
-#     try:
-#         from maestro.trainer.models.florence_2.entrypoint import florence_2_app
+def find_training_recipes(app: typer.Typer) -> None:
+    try:
+        from maestro.trainer.models.florence_2.entrypoint import florence_2_app
 
-#         app.add_typer(florence_2_app, name="florence2")
-#     except Exception:
-#         _warn_about_recipe_import_error(model_name="Florence 2")
+        app.add_typer(florence_2_app, name="florence2")
+    except Exception:
+        _warn_about_recipe_import_error(model_name="Florence 2")
 
-#     try:
-#         from maestro.trainer.models.paligemma.entrypoint import paligemma_app
+    try:
+        from maestro.trainer.models.paligemma.entrypoint import paligemma_app
 
-#         app.add_typer(paligemma_app, name="paligemma")
-#     except Exception:
-#         _warn_about_recipe_import_error(model_name="PaliGemma")
+        app.add_typer(paligemma_app, name="paligemma")
+    except Exception:
+        _warn_about_recipe_import_error(model_name="PaliGemma")
 
 
-# def _warn_about_recipe_import_error(model_name: str) -> None:
-#     disable_warnings = str2bool(
-#         os.getenv(
-#             DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
-#             DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
-#         )
-#     )
-#     if disable_warnings:
-#         return None
-#     warning = typer.style("WARNING", fg=typer.colors.RED, bold=True)
-#     message = "🚧 " + warning + f" cannot import recipe for {model_name}"
-#     typer.echo(message)
+def _warn_about_recipe_import_error(model_name: str) -> None:
+    disable_warnings = str2bool(
+        os.getenv(
+            DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+            DEFAULT_DISABLE_RECIPE_IMPORTS_WARNINGS_ENV,
+        )
+    )
+    if disable_warnings:
+        return None
+    warning = typer.style("WARNING", fg=typer.colors.RED, bold=True)
+    message = "🚧 " + warning + f" cannot import recipe for {model_name}"
+    typer.echo(message)
diff --git a/maestro/cli/main.py b/maestro/cli/main.py
index 8e2edc9..b600e3a 100644
--- a/maestro/cli/main.py
+++ b/maestro/cli/main.py
@@ -1,26 +1,15 @@
-# import typer
-
-# from maestro.cli.introspection import find_training_recipes
-
-# app = typer.Typer()
-# find_training_recipes(app=app)
-
-
-# @app.command(help="Display information about maestro")
-# def info():
-#     typer.echo("Welcome to maestro CLI. Let's train some VLM! 🏋")
+import typer
 
+from maestro.cli.introspection import find_training_recipes
 
-# if __name__ == "__main__":
-#     app()
+app = typer.Typer()
+find_training_recipes(app=app)
 
-import typer
-from maestro.trainer.models.florence_2.entrypoint import florence2
 
-app = typer.Typer()
+@app.command(help="Display information about maestro")
+def info():
+    typer.echo("Welcome to maestro CLI. Let's train some VLM! 🏋")
 
-# Add the florence2 command to the main app
-app.command()(florence2)
 
 if __name__ == "__main__":
     app()
diff --git a/maestro/cli/utils.py b/maestro/cli/utils.py
index 6d31bd9..0751fef 100644
--- a/maestro/cli/utils.py
+++ b/maestro/cli/utils.py
@@ -1,2 +1,2 @@
-# def str2bool(value: str) -> bool:
-#     return value.lower() in {"y", "t", "yes", "true"}
+def str2bool(value: str) -> bool:
+    return value.lower() in {"y", "t", "yes", "true"}
diff --git a/maestro/trainer/models/florence_2/core.py b/maestro/trainer/models/florence_2/core.py
index 46ea75b..c4ca286 100644
--- a/maestro/trainer/models/florence_2/core.py
+++ b/maestro/trainer/models/florence_2/core.py
@@ -31,30 +31,33 @@ class TrainingConfiguration:
     """Configuration for training a Florence-2 model.
 
     This class encapsulates all the parameters needed for training a Florence-2 model,
-    including dataset paths, model specifications, training hyperparameters, and output settings.
+    including dataset paths, model specifications, training hyperparameters, and output
+    settings.
 
     Attributes:
         dataset_path (str): Path to the dataset used for training.
-        model_id (str): Identifier for the Florence-2 model. Defaults to DEFAULT_FLORENCE2_MODEL_ID.
-        revision (str): Revision of the model to use. Defaults to DEFAULT_FLORENCE2_MODEL_REVISION.
-        device (torch.device): Device to use for training. Defaults to DEVICE.
-        cache_dir (Optional[str]): Directory to cache the model. Defaults to None.
-        epochs (int): Number of training epochs. Defaults to 10.
-        optimizer (Literal["sgd", "adamw", "adam"]): Optimizer to use for training. Defaults to "adamw".
-        lr (float): Learning rate for the optimizer. Defaults to 1e-5.
-        lr_scheduler (Literal["linear", "cosine", "polynomial"]): Learning rate scheduler. Defaults to "linear".
-        batch_size (int): Batch size for training. Defaults to 4.
-        val_batch_size (Optional[int]): Batch size for validation. Defaults to None.
-        num_workers (int): Number of workers for data loading. Defaults to 0.
-        val_num_workers (Optional[int]): Number of workers for validation data loading. Defaults to None.
-        lora_r (int): Rank of the LoRA update matrices. Defaults to 8.
-        lora_alpha (int): Scaling factor for the LoRA update. Defaults to 8.
-        lora_dropout (float): Dropout probability for LoRA layers. Defaults to 0.05.
-        bias (Literal["none", "all", "lora_only"]): Which bias to train. Defaults to "none".
-        use_rslora (bool): Whether to use RSLoRA. Defaults to True.
-        init_lora_weights (Union[bool, LoraInitLiteral]): How to initialize LoRA weights. Defaults to "gaussian".
-        output_dir (str): Directory to save output files. Defaults to "./training/florence-2".
-        metrics (List[BaseMetric]): List of metrics to track during training. Defaults to an empty list.
+        model_id (str): Identifier for the Florence-2 model.
+        revision (str): Revision of the model to use.
+        device (torch.device): Device to use for training.
+        cache_dir (Optional[str]): Directory to cache the model.
+        epochs (int): Number of training epochs.
+        optimizer (Literal["sgd", "adamw", "adam"]): Optimizer to use for training.
+        lr (float): Learning rate for the optimizer.
+        lr_scheduler (Literal["linear", "cosine", "polynomial"]): Learning rate
+            scheduler.
+        batch_size (int): Batch size for training.
+        val_batch_size (Optional[int]): Batch size for validation.
+        num_workers (int): Number of workers for data loading.
+        val_num_workers (Optional[int]): Number of workers for validation data loading.
+        lora_r (int): Rank of the LoRA update matrices.
+        lora_alpha (int): Scaling factor for the LoRA update.
+        lora_dropout (float): Dropout probability for LoRA layers.
+        bias (Literal["none", "all", "lora_only"]): Which bias to train.
+        use_rslora (bool): Whether to use RSLoRA.
+        init_lora_weights (Union[bool, LoraInitLiteral]): How to initialize LoRA
+            weights.
+        output_dir (str): Directory to save output files.
+        metrics (List[BaseMetric]): List of metrics to track during training.
     """
     dataset_path: str
     model_id: str = DEFAULT_FLORENCE2_MODEL_ID
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 30204ce..acab19e 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -1,227 +1,140 @@
-# import dataclasses
-# from typing import Optional, Annotated
-
-# import rich
-# import torch
-# import typer
-
-# from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
-#     DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
-# from maestro.trainer.models.florence_2.core import TrainingConfiguration
-# from maestro.trainer.models.florence_2.core import train as train_fun
-
-# florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
-
-
-# @florence_2_app.command(
-#     help="Train Florence 2 model", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
-# )
-# def train(
-#     dataset_location: Annotated[
-#         str,
-#         typer.Option("--dataset_location", help="Path to directory with dataset"),
-#     ],
-#     model_id_or_path: Annotated[
-#         str,
-#         typer.Option("--model_id_or_path", help="Model to be used or path to your checkpoint"),
-#     ] = DEFAULT_FLORENCE2_MODEL_ID,
-#     revision: Annotated[
-#         str,
-#         typer.Option("--revision", help="Revision of Florence2 HF repository"),
-#     ] = DEFAULT_FLORENCE2_MODEL_REVISION,
-#     device: Annotated[
-#         str,
-#         typer.Option("--device", help="CUDA device ID to be used (in format: 'cuda:0')"),
-#     ] = DEVICE,
-#     transformers_cache_dir: Annotated[
-#         Optional[str],
-#         typer.Option("--transformers_cache_dir", help="Cache dir for HF weights"),
-#     ] = None,
-#     training_epochs: Annotated[
-#         int,
-#         typer.Option("--training_epochs", help="Number of training epochs"),
-#     ] = 10,
-#     optimiser: Annotated[
-#         str,
-#         typer.Option("--optimiser", help="Optimiser to be used"),
-#     ] = "adamw",
-#     learning_rate: Annotated[
-#         float,
-#         typer.Option("--learning_rate", help="Learning rate"),
-#     ] = 1e-5,
-#     lr_scheduler: Annotated[
-#         str,
-#         typer.Option("--lr_scheduler", help="LR scheduler"),
-#     ] = "linear",
-#     train_batch_size: Annotated[
-#         int,
-#         typer.Option("--train_batch_size", help="Batch size for training"),
-#     ] = 4,
-#     test_batch_size: Annotated[
-#         Optional[int],
-#         typer.Option(
-#             "--train_batch_size", help="Batch size for validation and test. If not given - train will be used."
-#         ),
-#     ] = None,
-#     loaders_workers: Annotated[
-#         int,
-#         typer.Option("--loaders_workers", help="Number of loaders workers. 0 = # of CPU"),
-#     ] = 0,
-#     test_loaders_workers: Annotated[
-#         Optional[int],
-#         typer.Option(
-#             "--test_loaders_workers",
-#             help="Number of workers for test and val loaders. If not given - train will be used.",
-#         ),
-#     ] = None,
-#     lora_r: Annotated[
-#         int,
-#         typer.Option("--lora_r", help="Value of Lora R"),
-#     ] = 8,
-#     lora_alpha: Annotated[
-#         int,
-#         typer.Option("--lora_alpha", help="Value of Lora Alpha"),
-#     ] = 8,
-#     lora_dropout: Annotated[
-#         float,
-#         typer.Option("--lora_dropout", help="Value of Lora Dropout"),
-#     ] = 0.05,
-#     bias: Annotated[
-#         str,
-#         typer.Option("--bias", help="Value of Lora Bias"),
-#     ] = "none",
-#     use_rslora: Annotated[
-#         bool,
-#         typer.Option(
-#             "--use_rslora/--no_use_rslora",
-#             help="Boolean flag to decide if rslora to be used",
-#         ),
-#     ] = True,
-#     init_lora_weights: Annotated[
-#         str,
-#         typer.Option("--init_lora_weights", help="Lora weights initialisation"),
-#     ] = "gaussian",
-#     training_dir: Annotated[
-#         str,
-#         typer.Option("--training_dir", help="Path to directory where training outputs should be preserved"),
-#     ] = "./training/florence-2",
-#     max_checkpoints_to_keep: Annotated[
-#         int,
-#         typer.Option("--max_checkpoints_to_keep", help="Max checkpoints to keep"),
-#     ] = 3,
-#     num_samples_to_visualise: Annotated[
-#         int,
-#         typer.Option("--num_samples_to_visualise", help="Number of samples to visualise"),
-#     ] = 64,
-# ) -> None:
-#     configuration = TrainingConfiguration(
-#         dataset_location=dataset_location,
-#         model_id_or_path=model_id_or_path,
-#         revision=revision,
-#         device=torch.device(device),
-#         transformers_cache_dir=transformers_cache_dir,
-#         training_epochs=training_epochs,
-#         optimiser=optimiser,  # type: ignore
-#         learning_rate=learning_rate,
-#         lr_scheduler=lr_scheduler,  # type: ignore
-#         train_batch_size=train_batch_size,
-#         test_batch_size=test_batch_size,
-#         loaders_workers=loaders_workers,
-#         test_loaders_workers=test_loaders_workers,
-#         lora_r=lora_r,
-#         lora_alpha=lora_alpha,
-#         lora_dropout=lora_dropout,
-#         bias=bias,  # type: ignore
-#         use_rslora=use_rslora,
-#         init_lora_weights=init_lora_weights,  # type: ignore
-#         training_dir=training_dir,
-#         max_checkpoints_to_keep=max_checkpoints_to_keep,
-#         num_samples_to_visualise=num_samples_to_visualise,
-#     )
-#     typer.echo(typer.style("Training configuration", fg=typer.colors.BRIGHT_GREEN, bold=True))
-#     rich.print(dataclasses.asdict(configuration))
-#     train_fun(configuration=configuration)
-
-
-# @florence_2_app.command(help="Evaluate Florence 2 model")
-# def evaluate() -> None:
-#     pass
+import dataclasses
+from typing import Optional, Annotated, Literal, Union
 
+import rich
+import torch
 import typer
-from typing import Optional, List, Union, Literal
-from maestro.trainer.models.florence_2.core import TrainingConfiguration, train as train_florence2
 
-app = typer.Typer()
+from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
+    DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
+from maestro.trainer.models.florence_2.core import TrainingConfiguration, \
+    LoraInitLiteral
+from maestro.trainer.models.florence_2.core import train as train_fun
+
+florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
 
-@app.command()
-def florence2(
-    mode: str = typer.Option(..., help="Mode to run: train or evaluate"),
-    dataset_path: str = typer.Option(..., help="Path to the dataset used for training"),
-    model_id: str = typer.Option(None, help="Identifier for the Florence-2 model"),
-    revision: str = typer.Option(None, help="Revision of the model to use"),
-    device: str = typer.Option(None, help="Device to use for training"),
-    cache_dir: Optional[str] = typer.Option(None, help="Directory to cache the model"),
-    epochs: int = typer.Option(10, help="Number of training epochs"),
-    optimizer: str = typer.Option("adamw", help="Optimizer to use for training"),
-    lr: float = typer.Option(1e-5, help="Learning rate for the optimizer"),
-    lr_scheduler: str = typer.Option("linear", help="Learning rate scheduler"),
-    batch_size: int = typer.Option(4, help="Batch size for training"),
-    val_batch_size: Optional[int] = typer.Option(None, help="Batch size for validation"),
-    num_workers: int = typer.Option(0, help="Number of workers for data loading"),
-    val_num_workers: Optional[int] = typer.Option(None, help="Number of workers for validation data loading"),
-    lora_r: int = typer.Option(8, help="Rank of the LoRA update matrices"),
-    lora_alpha: int = typer.Option(8, help="Scaling factor for the LoRA update"),
-    lora_dropout: float = typer.Option(0.05, help="Dropout probability for LoRA layers"),
-    bias: str = typer.Option("none", help="Which bias to train"),
-    use_rslora: bool = typer.Option(True, help="Whether to use RSLoRA"),
-    init_lora_weights: str = typer.Option("gaussian", help="How to initialize LoRA weights"),
-    output_dir: str = typer.Option("./training/florence-2", help="Directory to save output files"),
-    metrics: List[str] = typer.Option([], help="List of metrics to track during training")
-):
-    """Main entry point for Florence-2 model."""
-    if mode == "train":
-        train(
-            dataset_path=dataset_path,
-            model_id=model_id,
-            revision=revision,
-            device=device,
-            cache_dir=cache_dir,
-            epochs=epochs,
-            optimizer=optimizer,
-            lr=lr,
-            lr_scheduler=lr_scheduler,
-            batch_size=batch_size,
-            val_batch_size=val_batch_size,
-            num_workers=num_workers,
-            val_num_workers=val_num_workers,
-            lora_r=lora_r,
-            lora_alpha=lora_alpha,
-            lora_dropout=lora_dropout,
-            bias=bias,
-            use_rslora=use_rslora,
-            init_lora_weights=init_lora_weights,
-            output_dir=output_dir,
-            metrics=metrics
-        )
-    elif mode == "evaluate":
-        evaluate()
-    else:
-        typer.echo(f"Unknown mode: {mode}")
-        raise typer.Exit(code=1)
 
-def train(**kwargs):
-    """Train a Florence-2 model."""
-    # Filter out None values
-    config_overrides = {k: v for k, v in kwargs.items() if v is not None}
-    
-    # Create configuration with overrides
-    config = TrainingConfiguration(**config_overrides)
-    
-    train_florence2(config)
+@florence_2_app.command(
+    help="Train Florence 2 model",
+    context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
+)
+def train(
+    dataset: Annotated[
+        str,
+        typer.Option("--dataset", help="Path to the dataset used for training"),
+    ],
+    model_id: Annotated[
+        str,
+        typer.Option("--model_id", help="Identifier for the Florence-2 model"),
+    ] = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: Annotated[
+        str,
+        typer.Option("--revision", help="Revision of the model to use"),
+    ] = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: Annotated[
+        str,
+        typer.Option("--device", help="Device to use for training"),
+    ] = DEVICE,
+    cache_dir: Annotated[
+        Optional[str],
+        typer.Option("--cache_dir", help="Directory to cache the model"),
+    ] = None,
+    epochs: Annotated[
+        int,
+        typer.Option("--epochs", help="Number of training epochs"),
+    ] = 10,
+    optimizer: Annotated[
+        Literal["sgd", "adamw", "adam"],
+        typer.Option("--optimizer", help="Optimizer to use for training"),
+    ] = "adamw",
+    lr: Annotated[
+        float,
+        typer.Option("--lr", help="Learning rate for the optimizer"),
+    ] = 1e-5,
+    lr_scheduler: Annotated[
+        Literal["linear", "cosine", "polynomial"],
+        typer.Option("--lr_scheduler", help="Learning rate scheduler"),
+    ] = "linear",
+    batch_size: Annotated[
+        int,
+        typer.Option("--batch_size", help="Batch size for training"),
+    ] = 4,
+    val_batch_size: Annotated[
+        Optional[int],
+        typer.Option("--val_batch_size", help="Batch size for validation"),
+    ] = None,
+    num_workers: Annotated[
+        int,
+        typer.Option("--num_workers", help="Number of workers for data loading"),
+    ] = 0,
+    val_num_workers: Annotated[
+        Optional[int],
+        typer.Option("--val_num_workers", help="Number of workers for validation data loading"),
+    ] = None,
+    lora_r: Annotated[
+        int,
+        typer.Option("--lora_r", help="Rank of the LoRA update matrices"),
+    ] = 8,
+    lora_alpha: Annotated[
+        int,
+        typer.Option("--lora_alpha", help="Scaling factor for the LoRA update"),
+    ] = 8,
+    lora_dropout: Annotated[
+        float,
+        typer.Option("--lora_dropout", help="Dropout probability for LoRA layers"),
+    ] = 0.05,
+    bias: Annotated[
+        Literal["none", "all", "lora_only"],
+        typer.Option("--bias", help="Which bias to train"),
+    ] = "none",
+    use_rslora: Annotated[
+        bool,
+        typer.Option("--use_rslora/--no_use_rslora", help="Whether to use RSLoRA"),
+    ] = True,
+    init_lora_weights: Annotated[
+        Union[bool, LoraInitLiteral],
+        typer.Option("--init_lora_weights", help="How to initialize LoRA weights"),
+    ] = "gaussian",
+    output_dir: Annotated[
+        str,
+        typer.Option("--output_dir", help="Directory to save output files"),
+    ] = "./training/florence-2",
+) -> None:
+    config = TrainingConfiguration(
+        dataset=dataset,
+        model_id=model_id,
+        revision=revision,
+        device=torch.device(device),
+        cache_dir=cache_dir,
+        epochs=epochs,
+        optimizer=optimizer,
+        lr=lr,
+        lr_scheduler=lr_scheduler,
+        batch_size=batch_size,
+        val_batch_size=val_batch_size,
+        num_workers=num_workers,
+        val_num_workers=val_num_workers,
+        lora_r=lora_r,
+        lora_alpha=lora_alpha,
+        lora_dropout=lora_dropout,
+        bias=bias,
+        use_rslora=use_rslora,
+        init_lora_weights=init_lora_weights,
+        output_dir=output_dir
+    )
+    typer.echo(typer.style(
+        text="Training configuration",
+        fg=typer.colors.BRIGHT_GREEN,
+        bold=True
+    ))
+    rich.print(dataclasses.asdict(config))
+    train_fun(config=config)
 
-def evaluate():
-    """Evaluate a Florence-2 model."""
-    typer.echo("Evaluation not implemented yet.")
 
-if __name__ == "__main__":
-    app()
+@florence_2_app.command(help="Evaluate Florence 2 model")
+def evaluate() -> None:
+    typer.echo(typer.style(
+        "Evaluation command for Florence 2 is not yet implemented.",
+        fg=typer.colors.YELLOW,
+        bold=True
+    ))

From f46049ed9a780f8dcca87d4b97de831e661d89bc Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 11 Sep 2024 00:14:02 +0200
Subject: [PATCH 65/69] remove Literal from command definitions

---
 maestro/trainer/models/florence_2/core.py       | 6 +++---
 maestro/trainer/models/florence_2/entrypoint.py | 8 ++++----
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/maestro/trainer/models/florence_2/core.py b/maestro/trainer/models/florence_2/core.py
index c4ca286..068199d 100644
--- a/maestro/trainer/models/florence_2/core.py
+++ b/maestro/trainer/models/florence_2/core.py
@@ -35,7 +35,7 @@ class TrainingConfiguration:
     settings.
 
     Attributes:
-        dataset_path (str): Path to the dataset used for training.
+        dataset (str): Path to the dataset used for training.
         model_id (str): Identifier for the Florence-2 model.
         revision (str): Revision of the model to use.
         device (torch.device): Device to use for training.
@@ -59,7 +59,7 @@ class TrainingConfiguration:
         output_dir (str): Directory to save output files.
         metrics (List[BaseMetric]): List of metrics to track during training.
     """
-    dataset_path: str
+    dataset: str
     model_id: str = DEFAULT_FLORENCE2_MODEL_ID
     revision: str = DEFAULT_FLORENCE2_MODEL_REVISION
     device: torch.device = DEVICE
@@ -100,7 +100,7 @@ def train(config: TrainingConfiguration) -> None:
         cache_dir=config.cache_dir,
     )
     train_loader, val_loader, test_loader = prepare_data_loaders(
-        dataset_location=config.dataset_path,
+        dataset_location=config.dataset,
         train_batch_size=config.batch_size,
         processor=processor,
         device=config.device,
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index acab19e..9310046 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -44,7 +44,7 @@ def train(
         typer.Option("--epochs", help="Number of training epochs"),
     ] = 10,
     optimizer: Annotated[
-        Literal["sgd", "adamw", "adam"],
+        str,
         typer.Option("--optimizer", help="Optimizer to use for training"),
     ] = "adamw",
     lr: Annotated[
@@ -52,7 +52,7 @@ def train(
         typer.Option("--lr", help="Learning rate for the optimizer"),
     ] = 1e-5,
     lr_scheduler: Annotated[
-        Literal["linear", "cosine", "polynomial"],
+        str,
         typer.Option("--lr_scheduler", help="Learning rate scheduler"),
     ] = "linear",
     batch_size: Annotated[
@@ -84,7 +84,7 @@ def train(
         typer.Option("--lora_dropout", help="Dropout probability for LoRA layers"),
     ] = 0.05,
     bias: Annotated[
-        Literal["none", "all", "lora_only"],
+        str,
         typer.Option("--bias", help="Which bias to train"),
     ] = "none",
     use_rslora: Annotated[
@@ -92,7 +92,7 @@ def train(
         typer.Option("--use_rslora/--no_use_rslora", help="Whether to use RSLoRA"),
     ] = True,
     init_lora_weights: Annotated[
-        Union[bool, LoraInitLiteral],
+        Union[bool, str],
         typer.Option("--init_lora_weights", help="How to initialize LoRA weights"),
     ] = "gaussian",
     output_dir: Annotated[

From a2850ac9d45fb0282266f2a3d6c4394576659f11 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 11 Sep 2024 00:26:06 +0200
Subject: [PATCH 66/69] remove Union from command definitions

---
 maestro/trainer/models/florence_2/entrypoint.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index 9310046..a686350 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -1,5 +1,5 @@
 import dataclasses
-from typing import Optional, Annotated, Literal, Union
+from typing import Optional, Annotated
 
 import rich
 import torch
@@ -7,8 +7,7 @@
 
 from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
     DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
-from maestro.trainer.models.florence_2.core import TrainingConfiguration, \
-    LoraInitLiteral
+from maestro.trainer.models.florence_2.core import TrainingConfiguration
 from maestro.trainer.models.florence_2.core import train as train_fun
 
 florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
@@ -92,7 +91,7 @@ def train(
         typer.Option("--use_rslora/--no_use_rslora", help="Whether to use RSLoRA"),
     ] = True,
     init_lora_weights: Annotated[
-        Union[bool, str],
+        str,
         typer.Option("--init_lora_weights", help="How to initialize LoRA weights"),
     ] = "gaussian",
     output_dir: Annotated[

From d614d254986f44c3506e76412c5f5ab9de2a7e66 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 11 Sep 2024 16:59:42 +0200
Subject: [PATCH 67/69] initial evaluate implementation

---
 .../trainer/models/florence_2/checkpoints.py  | 14 +++++
 maestro/trainer/models/florence_2/core.py     | 54 ++++++++++++++++
 .../trainer/models/florence_2/entrypoint.py   | 61 +++++++++++++++++--
 3 files changed, 124 insertions(+), 5 deletions(-)

diff --git a/maestro/trainer/models/florence_2/checkpoints.py b/maestro/trainer/models/florence_2/checkpoints.py
index cdffe7b..a489f2b 100644
--- a/maestro/trainer/models/florence_2/checkpoints.py
+++ b/maestro/trainer/models/florence_2/checkpoints.py
@@ -91,6 +91,20 @@ def load_model(
     device: torch.device = DEVICE,
     cache_dir: Optional[str] = None,
 ) -> Tuple[AutoProcessor, AutoModelForCausalLM]:
+    """Loads a Florence-2 model and its associated processor.
+
+    Args:
+        model_id_or_path: The identifier or path of the model to load.
+        revision: The specific model revision to use.
+        device: The device to load the model onto.
+        cache_dir: Directory to cache the downloaded model files.
+
+    Returns:
+        A tuple containing the loaded processor and model.
+
+    Raises:
+        ValueError: If the model or processor cannot be loaded.
+    """
     processor = AutoProcessor.from_pretrained(
         model_id_or_path,
         trust_remote_code=True,
diff --git a/maestro/trainer/models/florence_2/core.py b/maestro/trainer/models/florence_2/core.py
index 068199d..73f1c56 100644
--- a/maestro/trainer/models/florence_2/core.py
+++ b/maestro/trainer/models/florence_2/core.py
@@ -354,3 +354,57 @@ def get_optimizer(model: PeftModel, config: TrainingConfiguration) -> Optimizer:
     if optimizer_type == "sgd":
         return SGD(model.parameters(), lr=config.lr)
     raise ValueError(f"Unsupported optimizer: {config.optimizer}")
+
+
+def evaluate(config: TrainingConfiguration) -> None:
+    processor, model = load_model(
+        model_id_or_path=config.model_id,
+        revision=config.revision,
+        device=config.device,
+        cache_dir=config.cache_dir,
+    )
+    train_loader, val_loader, test_loader = prepare_data_loaders(
+        dataset_location=config.dataset,
+        train_batch_size=config.batch_size,
+        processor=processor,
+        device=config.device,
+        num_workers=config.num_workers,
+        test_loaders_workers=config.val_num_workers,
+    )
+    evaluation_loader = test_loader if test_loader is not None else val_loader
+    
+    metrics = []
+    for metric in config.metrics:
+        metrics += metric.describe()
+    evaluation_metrics_tracker = MetricsTracker.init(metrics=metrics)
+
+    # Run inference once for all metrics
+    _, expected_responses, generated_texts, images = run_predictions(
+        dataset=evaluation_loader.dataset,
+        processor=processor,
+        model=model,
+        device=config.device,
+    )
+
+    for metric in config.metrics:
+        if isinstance(metric, MeanAveragePrecisionMetric):
+            classes = extract_unique_detection_dataset_classes(train_loader.dataset)
+            targets, predictions = postprocess_florence2_output_for_mean_average_precision(
+                expected_responses=expected_responses,
+                generated_texts=generated_texts,
+                images=images,
+                classes=classes,
+                processor=processor
+            )
+            result = metric.compute(targets=targets, predictions=predictions)
+            for key, value in result.items():
+                evaluation_metrics_tracker.register(
+                    metric=key,
+                    epoch=1,
+                    step=1,
+                    value=value,
+                )
+
+    evaluation_metrics_tracker.as_json(
+        output_dir=os.path.join(config.output_dir, "metrics"),
+        filename="evaluation.json")
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index a686350..eb8983c 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -8,7 +8,8 @@
 from maestro.trainer.models.florence_2.checkpoints import DEFAULT_FLORENCE2_MODEL_ID, \
     DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
 from maestro.trainer.models.florence_2.core import TrainingConfiguration
-from maestro.trainer.models.florence_2.core import train as train_fun
+from maestro.trainer.models.florence_2.core import train as florence2_train
+from maestro.trainer.models.florence_2.core import evaluate as florence2_evaluate
 
 florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
 
@@ -127,13 +128,63 @@ def train(
         bold=True
     ))
     rich.print(dataclasses.asdict(config))
-    train_fun(config=config)
+    florence2_train(config=config)
 
 
 @florence_2_app.command(help="Evaluate Florence 2 model")
-def evaluate() -> None:
+def evaluate(
+    dataset: Annotated[
+        str,
+        typer.Option("--dataset", help="Path to the dataset used for evaluation"),
+    ],
+    model_id: Annotated[
+        str,
+        typer.Option("--model_id", help="Identifier for the Florence-2 model"),
+    ] = DEFAULT_FLORENCE2_MODEL_ID,
+    revision: Annotated[
+        str,
+        typer.Option("--revision", help="Revision of the model to use"),
+    ] = DEFAULT_FLORENCE2_MODEL_REVISION,
+    device: Annotated[
+        str,
+        typer.Option("--device", help="Device to use for evaluation"),
+    ] = DEVICE,
+    cache_dir: Annotated[
+        Optional[str],
+        typer.Option("--cache_dir", help="Directory to cache the model"),
+    ] = None,
+    batch_size: Annotated[
+        int,
+        typer.Option("--batch_size", help="Batch size for evaluation"),
+    ] = 4,
+    num_workers: Annotated[
+        int,
+        typer.Option("--num_workers", help="Number of workers for data loading"),
+    ] = 0,
+    val_num_workers: Annotated[
+        Optional[int],
+        typer.Option("--val_num_workers", help="Number of workers for validation data loading"),
+    ] = None,
+    output_dir: Annotated[
+        str,
+        typer.Option("--output_dir", help="Directory to save output files"),
+    ] = "./evaluation/florence-2",
+) -> None:
+    config = TrainingConfiguration(
+        dataset=dataset,
+        model_id=model_id,
+        revision=revision,
+        device=torch.device(device),
+        cache_dir=cache_dir,
+        batch_size=batch_size,
+        num_workers=num_workers,
+        val_num_workers=val_num_workers,
+        output_dir=output_dir
+    )
     typer.echo(typer.style(
-        "Evaluation command for Florence 2 is not yet implemented.",
-        fg=typer.colors.YELLOW,
+        text="Evaluation configuration",
+        fg=typer.colors.BRIGHT_GREEN,
         bold=True
     ))
+    rich.print(dataclasses.asdict(config))
+    florence2_evaluate(config=config)

From 5aba6605c128020742b99698ef9bc0a862296d94 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 11 Sep 2024 21:38:21 +0200
Subject: [PATCH 68/69] adding `quickstart` section to `README.md`

---
 README.md                                 | 45 +++++++++++++++++++----
 maestro/trainer/models/florence_2/core.py |  4 ++
 2 files changed, 41 insertions(+), 8 deletions(-)

diff --git a/README.md b/README.md
index 9814a8e..68371fb 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,3 @@
-
 <div align="center">
 
   <h1>maestro</h1>
@@ -23,13 +22,43 @@ Pip install the supervision package in a
 pip install maestro
 ```
 
-## 🚀 example
+## 🔥 quickstart
+
+### CLI
+
+VLMs can be fine-tuned on downstream tasks directly from the command line with 
+`maestro` command:
+
+```bash
+maestro florence2 train --dataset='<DATASET_PATH>' --epochs=10 --batch-size=8
+```
 
-Documentation and Florence-2 fine-tuning examples for object detection and VQA coming 
-soon.
+### SDK
+
+Alternatively, you can fine-tune VLMs using the Python SDK, which accepts the same 
+arguments as the CLI example above:
+
+```python
+from maestro.trainer.models.florence_2 import (
+    train,
+    TrainingConfiguration,
+    MeanAveragePrecisionMetric
+)
+
+config = TrainingConfiguration(
+    dataset='<DATASET_PATH>',
+    epochs=10,
+    batch_size=8,
+    metrics=[MeanAveragePrecisionMetric()]
+)
+
+train(config)
+```
 
-## 🚧 roadmap
+## 🦸 contribution
 
-- [ ] Release a CLI for predefined fine-tuning recipes.
-- [ ] Multi-GPU fine-tuning support.
-- [ ] Allow multi-dataset fine-tuning and support multiple tasks at the same time.
+We would love your help in making this repository even better! We are especially 
+looking for contributors with experience in fine-tuning vision-language models (VLMs). 
+If you notice any bugs or have suggestions for improvement, feel free to open an 
+[issue](https://github.com/roboflow/multimodal-maestro/issues) or submit a 
+[pull request](https://github.com/roboflow/multimodal-maestro/pulls).
diff --git a/maestro/trainer/models/florence_2/core.py b/maestro/trainer/models/florence_2/core.py
index 73f1c56..1be9991 100644
--- a/maestro/trainer/models/florence_2/core.py
+++ b/maestro/trainer/models/florence_2/core.py
@@ -144,6 +144,10 @@ def train(config: TrainingConfiguration) -> None:
     validation_metrics_tracker.as_json(
         output_dir=os.path.join(config.output_dir, "metrics"),
         filename="validation.json")
+    
+    # Log out paths for latest and best checkpoints
+    print(f"Latest checkpoint saved at: {checkpoint_manager.get_latest_checkpoint_path()}")
+    print(f"Best checkpoint saved at: {checkpoint_manager.get_best_checkpoint_path()}")
 
 
 def prepare_peft_model(

From 50751d5d76e820a29cae01924bb2565f550c1439 Mon Sep 17 00:00:00 2001
From: SkalskiP <piotr.skalski92@gmail.com>
Date: Wed, 11 Sep 2024 22:10:31 +0200
Subject: [PATCH 69/69] plug in `mean_average_precision` to Florence-2 CLI

---
 README.md                                     |  7 +---
 maestro/trainer/common/__init__.py            |  1 +
 maestro/trainer/common/utils/metrics.py       | 41 +++++++++++++++++++
 maestro/trainer/models/florence_2/__init__.py |  1 -
 maestro/trainer/models/florence_2/core.py     | 23 +++++++----
 .../trainer/models/florence_2/entrypoint.py   | 35 ++++++++++++++--
 maestro/trainer/models/florence_2/metrics.py  | 22 +---------
 7 files changed, 93 insertions(+), 37 deletions(-)

diff --git a/README.md b/README.md
index 68371fb..26ea4f9 100644
--- a/README.md
+++ b/README.md
@@ -39,11 +39,8 @@ Alternatively, you can fine-tune VLMs using the Python SDK, which accepts the sa
 arguments as the CLI example above:
 
 ```python
-from maestro.trainer.models.florence_2 import (
-    train,
-    TrainingConfiguration,
-    MeanAveragePrecisionMetric
-)
+from maestro.trainer.common import MeanAveragePrecisionMetric
+from maestro.trainer.models.florence_2 import train, TrainingConfiguration
 
 config = TrainingConfiguration(
     dataset='<DATASET_PATH>',
diff --git a/maestro/trainer/common/__init__.py b/maestro/trainer/common/__init__.py
index e69de29..0bfe6b2 100644
--- a/maestro/trainer/common/__init__.py
+++ b/maestro/trainer/common/__init__.py
@@ -0,0 +1 @@
+from maestro.trainer.common.utils.metrics import MeanAveragePrecisionMetric
diff --git a/maestro/trainer/common/utils/metrics.py b/maestro/trainer/common/utils/metrics.py
index e22cce4..0d1fb9b 100644
--- a/maestro/trainer/common/utils/metrics.py
+++ b/maestro/trainer/common/utils/metrics.py
@@ -10,7 +10,9 @@
 from typing import Any, Dict, List, Tuple
 
 import matplotlib.pyplot as plt
+import supervision as sv
 from PIL import Image
+from supervision.metrics.mean_average_precision import MeanAveragePrecision
 
 
 class BaseMetric(ABC):
@@ -45,6 +47,45 @@ def compute(self, targets: List[Any], predictions: List[Any]) -> Dict[str, float
         pass
 
 
+class MeanAveragePrecisionMetric(BaseMetric):
+    """
+    A class used to compute the Mean Average Precision (mAP) metric.
+    """
+
+    def describe(self) -> List[str]:
+        """
+        Returns a list of metric names that this class will compute.
+
+        Returns:
+            List[str]: A list of metric names.
+        """
+        return ["map50:95", "map50", "map75"]
+
+    def compute(
+        self,
+        targets: List[sv.Detections],
+        predictions: List[sv.Detections]
+    ) -> Dict[str, float]:
+        """
+        Computes the mAP metrics based on the targets and predictions.
+
+        Args:
+            targets (List[sv.Detections]): The ground truth detections.
+            predictions (List[sv.Detections]): The predicted detections.
+
+        Returns:
+            Dict[str, float]: A dictionary of computed mAP metrics with metric names as
+                keys and their values.
+        """
+        result = MeanAveragePrecision().update(
+            targets=targets, predictions=predictions).compute()
+        return {
+            "map50:95": result.map50_95,
+            "map50": result.map50,
+            "map75": result.map75
+        }
+
+
 class MetricsTracker:
 
     @classmethod
diff --git a/maestro/trainer/models/florence_2/__init__.py b/maestro/trainer/models/florence_2/__init__.py
index 836df5c..77d45b3 100644
--- a/maestro/trainer/models/florence_2/__init__.py
+++ b/maestro/trainer/models/florence_2/__init__.py
@@ -1,2 +1 @@
 from maestro.trainer.models.florence_2.core import TrainingConfiguration, train
-from maestro.trainer.models.florence_2.metrics import MeanAveragePrecisionMetric
diff --git a/maestro/trainer/models/florence_2/core.py b/maestro/trainer/models/florence_2/core.py
index 1be9991..8ef20ce 100644
--- a/maestro/trainer/models/florence_2/core.py
+++ b/maestro/trainer/models/florence_2/core.py
@@ -11,14 +11,23 @@
 from transformers import AutoModelForCausalLM, AutoProcessor, get_scheduler
 
 from maestro.trainer.common.utils.file_system import create_new_run_directory
-from maestro.trainer.common.utils.metrics import BaseMetric, MetricsTracker, \
-    display_results, save_metric_plots
+from maestro.trainer.common.utils.metrics import (
+    BaseMetric,
+    MetricsTracker,
+    display_results,
+    save_metric_plots,
+    MeanAveragePrecisionMetric
+)
 from maestro.trainer.common.utils.reproducibility import make_it_reproducible
-from maestro.trainer.models.florence_2.checkpoints import CheckpointManager, load_model, \
-    DEFAULT_FLORENCE2_MODEL_ID, DEFAULT_FLORENCE2_MODEL_REVISION, DEVICE
+from maestro.trainer.models.florence_2.checkpoints import (
+    CheckpointManager,
+    load_model,
+    DEFAULT_FLORENCE2_MODEL_ID,
+    DEFAULT_FLORENCE2_MODEL_REVISION,
+    DEVICE
+)
 from maestro.trainer.models.florence_2.data_loading import prepare_data_loaders
 from maestro.trainer.models.florence_2.metrics import (
-    MeanAveragePrecisionMetric,
     extract_unique_detection_dataset_classes,
     postprocess_florence2_output_for_mean_average_precision,
     run_predictions,
@@ -146,8 +155,8 @@ def train(config: TrainingConfiguration) -> None:
         filename="validation.json")
     
     # Log out paths for latest and best checkpoints
-    print(f"Latest checkpoint saved at: {checkpoint_manager.get_latest_checkpoint_path()}")
-    print(f"Best checkpoint saved at: {checkpoint_manager.get_best_checkpoint_path()}")
+    print(f"Latest checkpoint saved at: {checkpoint_manager.latest_checkpoint_dir}")
+    print(f"Best checkpoint saved at: {checkpoint_manager.best_checkpoint_dir}")
 
 
 def prepare_peft_model(
diff --git a/maestro/trainer/models/florence_2/entrypoint.py b/maestro/trainer/models/florence_2/entrypoint.py
index eb8983c..f18d81e 100644
--- a/maestro/trainer/models/florence_2/entrypoint.py
+++ b/maestro/trainer/models/florence_2/entrypoint.py
@@ -1,5 +1,5 @@
 import dataclasses
-from typing import Optional, Annotated
+from typing import Optional, Annotated, List, Dict, Type
 
 import rich
 import torch
@@ -10,10 +10,27 @@
 from maestro.trainer.models.florence_2.core import TrainingConfiguration
 from maestro.trainer.models.florence_2.core import train as florence2_train
 from maestro.trainer.models.florence_2.core import evaluate as florence2_evaluate
+from maestro.trainer.common.utils.metrics import BaseMetric, MeanAveragePrecisionMetric
 
 florence_2_app = typer.Typer(help="Fine-tune and evaluate Florence 2 model")
 
 
+METRIC_CLASSES: Dict[str, Type[BaseMetric]] = {
+    "mean_average_precision": MeanAveragePrecisionMetric,
+}
+
+
+def parse_metrics(metrics: List[str]) -> List[BaseMetric]:
+    metric_objects = []
+    for metric_name in metrics:
+        metric_class = METRIC_CLASSES.get(metric_name.lower())
+        if metric_class:
+            metric_objects.append(metric_class())
+        else:
+            raise ValueError(f"Unsupported metric: {metric_name}")
+    return metric_objects
+
+
 @florence_2_app.command(
     help="Train Florence 2 model",
     context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
@@ -99,7 +116,12 @@ def train(
         str,
         typer.Option("--output_dir", help="Directory to save output files"),
     ] = "./training/florence-2",
+    metrics: Annotated[
+        List[str],
+        typer.Option("--metrics", help="List of metrics to track during training"),
+    ] = [],
 ) -> None:
+    metric_objects = parse_metrics(metrics)
     config = TrainingConfiguration(
         dataset=dataset,
         model_id=model_id,
@@ -120,7 +142,8 @@ def train(
         bias=bias,
         use_rslora=use_rslora,
         init_lora_weights=init_lora_weights,
-        output_dir=output_dir
+        output_dir=output_dir,
+        metrics=metric_objects
     )
     typer.echo(typer.style(
         text="Training configuration",
@@ -169,7 +192,12 @@ def evaluate(
         str,
         typer.Option("--output_dir", help="Directory to save output files"),
     ] = "./evaluation/florence-2",
+    metrics: Annotated[
+        List[str],
+        typer.Option("--metrics", help="List of metrics to track during evaluation"),
+    ] = [],
 ) -> None:
+    metric_objects = parse_metrics(metrics)
     config = TrainingConfiguration(
         dataset=dataset,
         model_id=model_id,
@@ -179,7 +207,8 @@ def evaluate(
         batch_size=batch_size,
         num_workers=num_workers,
         val_num_workers=val_num_workers,
-        output_dir=output_dir
+        output_dir=output_dir,
+        metrics=metric_objects
     )
     typer.echo(typer.style(
         text="Evaluation configuration",
diff --git a/maestro/trainer/models/florence_2/metrics.py b/maestro/trainer/models/florence_2/metrics.py
index 117e031..09e02bd 100644
--- a/maestro/trainer/models/florence_2/metrics.py
+++ b/maestro/trainer/models/florence_2/metrics.py
@@ -1,39 +1,19 @@
 import re
-from typing import List, Dict
+from typing import List
 from typing import Tuple
 
 import numpy as np
 import supervision as sv
 import torch
 from PIL import Image
-from supervision.metrics.mean_average_precision import MeanAveragePrecision
 from tqdm import tqdm
 from transformers import AutoProcessor, AutoModelForCausalLM
 
 from maestro.trainer.common.data_loaders.datasets import DetectionDataset
-from maestro.trainer.common.utils.metrics import BaseMetric
 
 DETECTION_CLASS_PATTERN = r"([a-zA-Z0-9 -]+)<loc_\d+>"
 
 
-class MeanAveragePrecisionMetric(BaseMetric):
-
-    def describe(self) -> List[str]:
-        return ["map50:95", "map50", "map75"]
-
-    def compute(
-        self,
-        targets: List[sv.Detections],
-        predictions: List[sv.Detections]
-    ) -> Dict[str, float]:
-        result = MeanAveragePrecision().update(targets=targets, predictions=predictions).compute()
-        return {
-            "map50:95": result.map50_95,
-            "map50": result.map50,
-            "map75": result.map75
-        }
-
-
 def postprocess_florence2_output_for_mean_average_precision(
     expected_responses: List[str],
     generated_texts: List[str],