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Encodechka

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Roman Solomatin commited on Jun 13

Commit

c7ac965

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1 Parent(s): 43fec8c

init

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Files changed (25) hide show

.gitignore +1 -0
.pre-commit-config.yaml +12 -3
Makefile +6 -6
README.md +5 -5
pdm.lock +1358 -0
pyproject.toml +56 -10
requirements.txt +540 -18
src/__init__.py +0 -0
src/encodechka/__init__.py +0 -0
src/{about.py → encodechka/about.py} +13 -8
app.py → src/encodechka/app.py +73 -48
src/encodechka/display/__init__.py +0 -0
src/{display → encodechka/display}/css_html_js.py +2 -2
src/{display → encodechka/display}/formatting.py +0 -0
src/{display → encodechka/display}/utils.py +37 -12
src/{envs.py → encodechka/envs.py} +3 -3
src/encodechka/leaderboard/__init__.py +0 -0
src/{leaderboard → encodechka/leaderboard}/read_evals.py +24 -25
src/{populate.py → encodechka/populate.py} +3 -4
src/encodechka/submission/__init__.py +0 -0
src/encodechka/submission/check_validity.py +124 -0
src/encodechka/submission/submit.py +122 -0
src/submission/check_validity.py +0 -99
src/submission/submit.py +0 -119
tests/__init__.py +0 -0

.gitignore CHANGED Viewed

@@ -11,3 +11,4 @@ eval-results/
 eval-queue-bk/
 eval-results-bk/
 logs/

 eval-queue-bk/
 eval-results-bk/
 logs/
+/.pdm-python

.pre-commit-config.yaml CHANGED Viewed

@@ -46,8 +46,17 @@ repos:
         name: Format code
         additional_dependencies: ['click==8.0.2']
-  - repo: https://github.com/charliermarsh/ruff-pre-commit
-    # Ruff version.
-    rev: 'v0.0.267'
     hooks:
       - id: ruff

         name: Format code
         additional_dependencies: ['click==8.0.2']
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.4.4
     hooks:
       - id: ruff
+        types_or: [ python, pyi, jupyter ]
+        args: [ --config, pyproject.toml, --fix, --output-format=github]
+      - id: ruff
+        types_or: [ python, pyi, jupyter ]
+        args: [ --config, pyproject.toml, --fix ]
+      # Run the formatter.
+      - id: ruff-format
+        types_or: [ python, pyi, jupyter ]
+        args: [ --config, pyproject.toml ]

Makefile CHANGED Viewed

@@ -1,13 +1,13 @@
 .PHONY: style format
 style:
-	python -m black --line-length 119 .
-	python -m isort .
-	ruff check --fix .
 quality:
-	python -m black --check --line-length 119 .
-	python -m isort --check-only .
-	ruff check .

 .PHONY: style format
+.DEFAULT_GOAL := all
 style:
+	ruff format
+	pre-commit run --all-files
 quality:
+	ruff check
+all: style quality

README.md CHANGED Viewed

@@ -1,12 +1,12 @@
 ---
-title: E
 emoji: 🥇
 colorFrom: green
 colorTo: indigo
 sdk: gradio
-app_file: app.py
 pinned: true
-license: apache-2.0
 ---
 # Start the configuration
@@ -38,7 +38,7 @@ If you encounter problem on the space, don't hesitate to restart it to remove th
 # Code logic for more complex edits
-You'll find
 - the main table' columns names and properties in `src/display/utils.py`
 - the logic to read all results and request files, then convert them in dataframe lines, in `src/leaderboard/read_evals.py`, and `src/populate.py`
-- teh logic to allow or filter submissions in `src/submission/submit.py` and `src/submission/check_validity.py`

 ---
+title: Encodechka Leaderboard
 emoji: 🥇
 colorFrom: green
 colorTo: indigo
 sdk: gradio
+app_file: src/encodechka/app.py
 pinned: true
+license: MIT
 ---
 # Start the configuration
 # Code logic for more complex edits
+You'll find
 - the main table' columns names and properties in `src/display/utils.py`
 - the logic to read all results and request files, then convert them in dataframe lines, in `src/leaderboard/read_evals.py`, and `src/populate.py`
+- teh logic to allow or filter submissions in `src/submission/submit.py` and `src/submission/check_validity.py`

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+    {file = "websockets-11.0.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:92b2065d642bf8c0a82d59e59053dd2fdde64d4ed44efe4870fa816c1232647b"},
+    {file = "websockets-11.0.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0ee68fe502f9031f19d495dae2c268830df2760c0524cbac5d759921ba8c8e82"},
+    {file = "websockets-11.0.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dcacf2c7a6c3a84e720d1bb2b543c675bf6c40e460300b628bab1b1efc7c034c"},
+    {file = "websockets-11.0.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b67c6f5e5a401fc56394f191f00f9b3811fe843ee93f4a70df3c389d1adf857d"},
+    {file = "websockets-11.0.3-pp39-pypy39_pp73-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d5023a4b6a5b183dc838808087033ec5df77580485fc533e7dab2567851b0a4"},
+    {file = "websockets-11.0.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ed058398f55163a79bb9f06a90ef9ccc063b204bb346c4de78efc5d15abfe602"},
+    {file = "websockets-11.0.3-py3-none-any.whl", hash = "sha256:6681ba9e7f8f3b19440921e99efbb40fc89f26cd71bf539e45d8c8a25c976dc6"},
+    {file = "websockets-11.0.3.tar.gz", hash = "sha256:88fc51d9a26b10fc331be344f1781224a375b78488fc343620184e95a4b27016"},
+]

pyproject.toml CHANGED Viewed

@@ -1,13 +1,59 @@
 [tool.ruff]
 # Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
-select = ["E", "F"]
-ignore = ["E501"] # line too long (black is taking care of this)
-line-length = 119
 fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]
-[tool.isort]
-profile = "black"
-line_length = 119
-[tool.black]
-line-length = 119

+[project]
+name = "Encodechka"
+version = "0.1.0"
+description = "Default template for PDM package"
+authors = [
+    {name = "Roman Solomatin", email = "[email protected]"},
+]
+dependencies = [
+    "APScheduler>=3.10.4",
+    #    "black",
+    "click>=8.1.7",
+    #    "datasets",
+    "gradio>=4.36.1",
+    "gradio-client>=1.0.1",
+    "huggingface-hub>=0.18.0",
+    "matplotlib>=3.9.0",
+    "numpy>=1.26.4",
+    "pandas>=2.2.2",
+    "python-dateutil>=2.9.0.post0",
+    "requests>=2.32.3",
+    "tqdm>=4.66.4",
+    #    "transformers",
+    #    "tokenizers>=0.15.0",
+    #    "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@b281b0921b636bc36ad05c0b0b0763bd6dd43463",
+    #    "accelerate",
+    #    "sentencepiece",
+]
+requires-python = "==3.10.*"
+readme = "README.md"
+license = {text = "MIT"}
+[tool.pdm]
+distribution = false
+[tool.pdm.dev-dependencies]
+lint = [
+    "ruff>=0.4.8",
+]
 [tool.ruff]
+fix = true
+target-version = "py310"
+line-length = 120
+extend-include = ["*.ipynb"]
+[tool.ruff.lint]
 # Enable pycodestyle (`E`) and Pyflakes (`F`) codes by default.
+select= [
+    "E",   # pycodestyle errors
+    "W",   # pycodestyle warnings
+    "F",   # pyflakes
+    "I",   # isort
+    "B",   # flake8-bugbear
+    "UP",  # pyupgrade
+    "RUF", # ruff
+    #"D",   # pydocstyle
+]
 fixable = ["A", "B", "C", "D", "E", "F", "G", "I", "N", "Q", "S", "T", "W", "ANN", "ARG", "BLE", "COM", "DJ", "DTZ", "EM", "ERA", "EXE", "FBT", "ICN", "INP", "ISC", "NPY", "PD", "PGH", "PIE", "PL", "PT", "PTH", "PYI", "RET", "RSE", "RUF", "SIM", "SLF", "TCH", "TID", "TRY", "UP", "YTT"]

requirements.txt CHANGED Viewed

@@ -1,18 +1,540 @@
-APScheduler
-black
-click
-datasets
-gradio
-gradio_client
-huggingface-hub>=0.18.0
-matplotlib
-numpy
-pandas
-python-dateutil
-requests
-tqdm
-transformers
-tokenizers>=0.15.0
-git+https://github.com/EleutherAI/lm-evaluation-harness.git@b281b0921b636bc36ad05c0b0b0763bd6dd43463#egg=lm-eval
-accelerate
-sentencepiece

+# This file is @generated by PDM.
+# Please do not edit it manually.
+aiofiles==23.2.1 \
+    --hash=sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107 \
+    --hash=sha256:84ec2218d8419404abcb9f0c02df3f34c6e0a68ed41072acfb1cef5cbc29051a
+altair==5.3.0 \
+    --hash=sha256:5a268b1a0983b23d8f9129f819f956174aa7aea2719ed55a52eba9979b9f6675 \
+    --hash=sha256:7084a1dab4d83c5e7e5246b92dc1b4451a6c68fd057f3716ee9d315c8980e59a
+annotated-types==0.7.0 \
+    --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \
+    --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89
+anyio==4.4.0 \
+    --hash=sha256:5aadc6a1bbb7cdb0bede386cac5e2940f5e2ff3aa20277e991cf028e0585ce94 \
+    --hash=sha256:c1b2d8f46a8a812513012e1107cb0e68c17159a7a594208005a57dc776e1bdc7
+apscheduler==3.10.4 \
+    --hash=sha256:e6df071b27d9be898e486bc7940a7be50b4af2e9da7c08f0744a96d4bd4cef4a \
+    --hash=sha256:fb91e8a768632a4756a585f79ec834e0e27aad5860bac7eaa523d9ccefd87661
+attrs==23.2.0 \
+    --hash=sha256:935dc3b529c262f6cf76e50877d35a4bd3c1de194fd41f47a2b7ae8f19971f30 \
+    --hash=sha256:99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1
+certifi==2024.6.2 \
+    --hash=sha256:3cd43f1c6fa7dedc5899d69d3ad0398fd018ad1a17fba83ddaf78aa46c747516 \
+    --hash=sha256:ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56
+charset-normalizer==3.3.2 \
+    --hash=sha256:06435b539f889b1f6f4ac1758871aae42dc3a8c0e24ac9e60c2384973ad73027 \
+    --hash=sha256:10955842570876604d404661fbccbc9c7e684caf432c09c715ec38fbae45ae09 \
+    --hash=sha256:1d3193f4a680c64b4b6a9115943538edb896edc190f0b222e73761716519268e \
+    --hash=sha256:25baf083bf6f6b341f4121c2f3c548875ee6f5339300e08be3f2b2ba1721cdd3 \
+    --hash=sha256:2e81c7b9c8979ce92ed306c249d46894776a909505d8f5a4ba55b14206e3222f \
+    --hash=sha256:3d47fa203a7bd9c5b6cee4736ee84ca03b8ef23193c0d1ca99b5089f72645c73 \
+    --hash=sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc \
+    --hash=sha256:572c3763a264ba47b3cf708a44ce965d98555f618ca42c926a9c1616d8f34269 \
+    --hash=sha256:6897af51655e3691ff853668779c7bad41579facacf5fd7253b0133308cf000d \
+    --hash=sha256:8465322196c8b4d7ab6d1e049e4c5cb460d0394da4a27d23cc242fbf0034b6b5 \
+    --hash=sha256:9063e24fdb1e498ab71cb7419e24622516c4a04476b17a2dab57e8baa30d6e03 \
+    --hash=sha256:a9a8e9031d613fd2009c182b69c7b2c1ef8239a0efb1df3f7c8da66d5dd3d537 \
+    --hash=sha256:beb58fe5cdb101e3a055192ac291b7a21e3b7ef4f67fa1d74e331a7f2124341c \
+    --hash=sha256:cd70574b12bb8a4d2aaa0094515df2463cb429d8536cfb6c7ce983246983e5a6 \
+    --hash=sha256:e06ed3eb3218bc64786f7db41917d4e686cc4856944f53d5bdf83a6884432e12 \
+    --hash=sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5 \
+    --hash=sha256:fd1abc0d89e30cc4e02e4064dc67fcc51bd941eb395c502aac3ec19fab46b519
+click==8.1.7 \
+    --hash=sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28 \
+    --hash=sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de
+colorama==0.4.6; platform_system == "Windows" or sys_platform == "win32" \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+contourpy==1.2.1 \
+    --hash=sha256:11959f0ce4a6f7b76ec578576a0b61a28bdc0696194b6347ba3f1c53827178b9 \
+    --hash=sha256:39f3ecaf76cd98e802f094e0d4fbc6dc9c45a8d0c4d185f0f6c2234e14e5f75b \
+    --hash=sha256:457499c79fa84593f22454bbd27670227874cd2ff5d6c84e60575c8b50a69619 \
+    --hash=sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480 \
+    --hash=sha256:4d8908b3bee1c889e547867ca4cdc54e5ab6be6d3e078556814a22457f49423c \
+    --hash=sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd \
+    --hash=sha256:94b34f32646ca0414237168d68a9157cb3889f06b096612afdd296003fdd32fd \
+    --hash=sha256:9cffe0f850e89d7c0012a1fb8730f75edd4320a0a731ed0c183904fe6ecfc3a9 \
+    --hash=sha256:a31f94983fecbac95e58388210427d68cd30fe8a36927980fab9c20062645609 \
+    --hash=sha256:ac58bdee53cbeba2ecad824fa8159493f0bf3b8ea4e93feb06c9a465d6c87da8 \
+    --hash=sha256:b33d2bc4f69caedcd0a275329eb2198f560b325605810895627be5d4b876bf7f \
+    --hash=sha256:bd7c23df857d488f418439686d3b10ae2fbf9bc256cd045b37a8c16575ea1040 \
+    --hash=sha256:eb3315a8a236ee19b6df481fc5f997436e8ade24a9f03dfdc6bd490fea20c6da \
+    --hash=sha256:ef2b055471c0eb466033760a521efb9d8a32b99ab907fc8358481a1dd29e3bd3
+cycler==0.12.1 \
+    --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \
+    --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c
+dnspython==2.6.1 \
+    --hash=sha256:5ef3b9680161f6fa89daf8ad451b5f1a33b18ae8a1c6778cdf4b43f08c0a6e50 \
+    --hash=sha256:e8f0f9c23a7b7cb99ded64e6c3a6f3e701d78f50c55e002b839dea7225cff7cc
+email-validator==2.1.1 \
+    --hash=sha256:200a70680ba08904be6d1eef729205cc0d687634399a5924d842533efb824b84 \
+    --hash=sha256:97d882d174e2a65732fb43bfce81a3a834cbc1bde8bf419e30ef5ea976370a05
+exceptiongroup==1.2.1; python_version < "3.11" \
+    --hash=sha256:5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad \
+    --hash=sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16
+fastapi==0.111.0 \
+    --hash=sha256:97ecbf994be0bcbdadedf88c3150252bed7b2087075ac99735403b1b76cc8fc0 \
+    --hash=sha256:b9db9dd147c91cb8b769f7183535773d8741dd46f9dc6676cd82eab510228cd7
+fastapi-cli==0.0.4 \
+    --hash=sha256:a2552f3a7ae64058cdbb530be6fa6dbfc975dc165e4fa66d224c3d396e25e809 \
+    --hash=sha256:e2e9ffaffc1f7767f488d6da34b6f5a377751c996f397902eb6abb99a67bde32
+ffmpy==0.3.2 \
+    --hash=sha256:475ebfff1044661b8d969349dbcd2db9bf56d3ee78c0627e324769b49a27a78f
+filelock==3.15.1 \
+    --hash=sha256:58a2549afdf9e02e10720eaa4d4470f56386d7a6f72edd7d0596337af8ed7ad8 \
+    --hash=sha256:71b3102950e91dfc1bb4209b64be4dc8854f40e5f534428d8684f953ac847fac
+fonttools==4.53.0 \
+    --hash=sha256:099634631b9dd271d4a835d2b2a9e042ccc94ecdf7e2dd9f7f34f7daf333358d \
+    --hash=sha256:52a6e0a7a0bf611c19bc8ec8f7592bdae79c8296c70eb05917fd831354699b20 \
+    --hash=sha256:6b4f04b1fbc01a3569d63359f2227c89ab294550de277fd09d8fca6185669fa4 \
+    --hash=sha256:715b41c3e231f7334cbe79dfc698213dcb7211520ec7a3bc2ba20c8515e8a3b5 \
+    --hash=sha256:74ae2441731a05b44d5988d3ac2cf784d3ee0a535dbed257cbfff4be8bb49eb9 \
+    --hash=sha256:95db0c6581a54b47c30860d013977b8a14febc206c8b5ff562f9fe32738a8aca \
+    --hash=sha256:9cd7a6beec6495d1dffb1033d50a3f82dfece23e9eb3c20cd3c2444d27514068 \
+    --hash=sha256:c93ed66d32de1559b6fc348838c7572d5c0ac1e4a258e76763a5caddd8944002 \
+    --hash=sha256:daaef7390e632283051e3cf3e16aff2b68b247e99aea916f64e578c0449c9c68 \
+    --hash=sha256:e40013572bfb843d6794a3ce076c29ef4efd15937ab833f520117f8eccc84fd6
+fsspec==2024.6.0 \
+    --hash=sha256:58d7122eb8a1a46f7f13453187bfea4972d66bf01618d37366521b1998034cee \
+    --hash=sha256:f579960a56e6d8038a9efc8f9c77279ec12e6299aa86b0769a7e9c46b94527c2
+gradio==4.36.1 \
+    --hash=sha256:31edb504c88c1db06c08daf750dcdaa072087ada59aa4ff83c1a3f4c2075912d \
+    --hash=sha256:72b2d21156d3467123bae6f30f463f002ef06e272766274308f5ed3cac37563b
+gradio-client==1.0.1 \
+    --hash=sha256:b3fa4d1c626067cc866d6172caa75d373e114bacfba650e49e293646d786646a \
+    --hash=sha256:fe3f527349ac38cbc5deb6d629a15c06fa3b4a68d1e04dc5ca9fbb1896318629
+h11==0.14.0 \
+    --hash=sha256:8f19fbbe99e72420ff35c00b27a34cb9937e902a8b810e2c88300c6f0a3b699d \
+    --hash=sha256:e3fe4ac4b851c468cc8363d500db52c2ead036020723024a109d37346efaa761
+httpcore==1.0.5 \
+    --hash=sha256:34a38e2f9291467ee3b44e89dd52615370e152954ba21721378a87b2960f7a61 \
+    --hash=sha256:421f18bac248b25d310f3cacd198d55b8e6125c107797b609ff9b7a6ba7991b5
+httptools==0.6.1 \
+    --hash=sha256:00d5d4b68a717765b1fabfd9ca755bd12bf44105eeb806c03d1962acd9b8e563 \
+    --hash=sha256:0ac5a0ae3d9f4fe004318d64b8a854edd85ab76cffbf7ef5e32920faef62f142 \
+    --hash=sha256:1ed99a373e327f0107cb513b61820102ee4f3675656a37a50083eda05dc9541b \
+    --hash=sha256:3f30d3ce413088a98b9db71c60a6ada2001a08945cb42dd65a9a9fe228627658 \
+    --hash=sha256:639dc4f381a870c9ec860ce5c45921db50205a37cc3334e756269736ff0aac58 \
+    --hash=sha256:c6e26c30455600b95d94b1b836085138e82f177351454ee841c148f93a9bad5a \
+    --hash=sha256:d2f6c3c4cb1948d912538217838f6e9960bc4a521d7f9b323b3da579cd14532f \
+    --hash=sha256:e57997ac7fb7ee43140cc03664de5f268813a481dff6245e0075925adc6aa185
+httpx==0.27.0 \
+    --hash=sha256:71d5465162c13681bff01ad59b2cc68dd838ea1f10e51574bac27103f00c91a5 \
+    --hash=sha256:a0cb88a46f32dc874e04ee956e4c2764aba2aa228f650b06788ba6bda2962ab5
+huggingface-hub==0.23.3 \
+    --hash=sha256:1a1118a0b3dea3bab6c325d71be16f5ffe441d32f3ac7c348d6875911b694b5b \
+    --hash=sha256:22222c41223f1b7c209ae5511d2d82907325a0e3cdbce5f66949d43c598ff3bc
+idna==3.7 \
+    --hash=sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc \
+    --hash=sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0
+importlib-resources==6.4.0 \
+    --hash=sha256:50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c \
+    --hash=sha256:cdb2b453b8046ca4e3798eb1d84f3cce1446a0e8e7b5ef4efb600f19fc398145
+jinja2==3.1.4 \
+    --hash=sha256:4a3aee7acbbe7303aede8e9648d13b8bf88a429282aa6122a993f0ac800cb369 \
+    --hash=sha256:bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d
+jsonschema==4.22.0 \
+    --hash=sha256:5b22d434a45935119af990552c862e5d6d564e8f6601206b305a61fdf661a2b7 \
+    --hash=sha256:ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802
+jsonschema-specifications==2023.12.1 \
+    --hash=sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc \
+    --hash=sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c
+kiwisolver==1.4.5 \
+    --hash=sha256:05703cf211d585109fcd72207a31bb170a0f22144d68298dc5e61b3c946518af \
+    --hash=sha256:11d011a7574eb3b82bcc9c1a1d35c1d7075677fdd15de527d91b46bd35e935ee \
+    --hash=sha256:146d14bebb7f1dc4d5fbf74f8a6cb15ac42baadee8912eb84ac0b3b2a3dc6ac3 \
+    --hash=sha256:19df6e621f6d8b4b9c4d45f40a66839294ff2bb235e64d2178f7522d9170ac5b \
+    --hash=sha256:210ef2c3a1f03272649aff1ef992df2e724748918c4bc2d5a90352849eb40bea \
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+    --hash=sha256:b810a2c7878cbdecca12feae2c2ae8af59bea016a78bc353c184fa1e09f76b68 \
+    --hash=sha256:bbf8a20266136507abf88b0df2328e6a9a7c7309e8daff124dda3803306a9fdb \
+    --hash=sha256:c2444dc7cb9d8cc5ab88ebe792a8d75709d96eeef47f4c8fccb6df7c7bc5be71 \
+    --hash=sha256:c5af2347d17ab0bd59366db8752d9e037982e259cacb2ba06f2c41c08af02c39 \
+    --hash=sha256:da1e0a8caebf17976e2ffd00fa15f258e14749db5e014660f53114b676e68538 \
+    --hash=sha256:f7e1f9c5d1160d03b93fc4b68a0aeb82fe25563e12fbcdc8507f8434ab6f823c
+websockets==11.0.3 \
+    --hash=sha256:0ac56b661e60edd453585f4bd68eb6a29ae25b5184fd5ba51e97652580458998 \
+    --hash=sha256:0ee68fe502f9031f19d495dae2c268830df2760c0524cbac5d759921ba8c8e82 \
+    --hash=sha256:1d2256283fa4b7f4c7d7d3e84dc2ece74d341bce57d5b9bf385df109c2a1a82f \
+    --hash=sha256:1d5023a4b6a5b183dc838808087033ec5df77580485fc533e7dab2567851b0a4 \
+    --hash=sha256:2d903ad4419f5b472de90cd2d40384573b25da71e33519a67797de17ef849b69 \
+    --hash=sha256:3ccc8a0c387629aec40f2fc9fdcb4b9d5431954f934da3eaf16cdc94f67dbfac \
+    --hash=sha256:41f696ba95cd92dc047e46b41b26dd24518384749ed0d99bea0a941ca87404c4 \
+    --hash=sha256:42cc5452a54a8e46a032521d7365da775823e21bfba2895fb7b77633cce031bb \
+    --hash=sha256:54c6e5b3d3a8936a4ab6870d46bdd6ec500ad62bde9e44462c32d18f1e9a8e54 \
+    --hash=sha256:6681ba9e7f8f3b19440921e99efbb40fc89f26cd71bf539e45d8c8a25c976dc6 \
+    --hash=sha256:7622a89d696fc87af8e8d280d9b421db5133ef5b29d3f7a1ce9f1a7bf7fcfa11 \
+    --hash=sha256:84d27a4832cc1a0ee07cdcf2b0629a8a72db73f4cf6de6f0904f6661227f256f \
+    --hash=sha256:8531fdcad636d82c517b26a448dcfe62f720e1922b33c81ce695d0edb91eb931 \
+    --hash=sha256:86d2a77fd490ae3ff6fae1c6ceaecad063d3cc2320b44377efdde79880e11526 \
+    --hash=sha256:88fc51d9a26b10fc331be344f1781224a375b78488fc343620184e95a4b27016 \
+    --hash=sha256:92b2065d642bf8c0a82d59e59053dd2fdde64d4ed44efe4870fa816c1232647b \
+    --hash=sha256:b67c6f5e5a401fc56394f191f00f9b3811fe843ee93f4a70df3c389d1adf857d \
+    --hash=sha256:bceab846bac555aff6427d060f2fcfff71042dba6f5fca7dc4f75cac815e57ca \
+    --hash=sha256:c114e8da9b475739dde229fd3bc6b05a6537a88a578358bc8eb29b4030fac9c9 \
+    --hash=sha256:d67ac60a307f760c6e65dad586f556dde58e683fab03323221a4e530ead6f74d \
+    --hash=sha256:dcacf2c7a6c3a84e720d1bb2b543c675bf6c40e460300b628bab1b1efc7c034c \
+    --hash=sha256:de36fe9c02995c7e6ae6efe2e205816f5f00c22fd1fbf343d4d18c3d5ceac2f5 \
+    --hash=sha256:e052b8467dd07d4943936009f46ae5ce7b908ddcac3fda581656b1b19c083d9b \
+    --hash=sha256:e063b1865974611313a3849d43f2c3f5368093691349cf3c7c8f8f75ad7cb280 \
+    --hash=sha256:e6316827e3e79b7b8e7d8e3b08f4e331af91a48e794d5d8b099928b6f0b85f20 \
+    --hash=sha256:ed058398f55163a79bb9f06a90ef9ccc063b204bb346c4de78efc5d15abfe602 \
+    --hash=sha256:f2e58f2c36cc52d41f2659e4c0cbf7353e28c8c9e63e30d8c6d3494dc9fdedcf \
+    --hash=sha256:ffd7dcaf744f25f82190856bc26ed81721508fc5cbf2a330751e135ff1283564

src/__init__.py ADDED Viewed

File without changes

src/encodechka/__init__.py ADDED Viewed

File without changes

src/{about.py → encodechka/about.py} RENAMED Viewed

@@ -1,6 +1,7 @@
 from dataclasses import dataclass
 from enum import Enum
 @dataclass
 class Task:
     benchmark: str
@@ -11,13 +12,13 @@ class Task:
 # Select your tasks here
 # ---------------------------------------------------
 class Tasks(Enum):
-    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
     task0 = Task("anli_r1", "acc", "ANLI")
     task1 = Task("logiqa", "acc_norm", "LogiQA")
-NUM_FEWSHOT = 0 # Change with your few shot
-# ---------------------------------------------------
 # Your leaderboard name
@@ -29,7 +30,7 @@ Intro text
 """
 # Which evaluations are you running? how can people reproduce what you have?
-LLM_BENCHMARKS_TEXT = f"""
 ## How it works
 ## Reproducibility
@@ -47,13 +48,16 @@ config = AutoConfig.from_pretrained("your model name", revision=revision)
 model = AutoModel.from_pretrained("your model name", revision=revision)
 tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
 ```
-If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been improperly uploaded.
 Note: make sure your model is public!
-Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it, stay posted!
 ### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
-It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number of parameters of your model to the `Extended Viewer`!
 ### 3) Make sure your model has an open license!
 This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
@@ -64,7 +68,8 @@ When we add extra information about models to the leaderboard, it will be automa
 ## In case of model failure
 If your model is displayed in the `FAILED` category, its execution stopped.
 Make sure you have followed the above steps first.
-If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without modifications (you can add `--limit` to limit the number of examples per task).
 """
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"

 from dataclasses import dataclass
 from enum import Enum
 @dataclass
 class Task:
     benchmark: str
 # Select your tasks here
 # ---------------------------------------------------
 class Tasks(Enum):
+    # task_key in the json file, metric_key in the json file, name to display in the leaderboard
     task0 = Task("anli_r1", "acc", "ANLI")
     task1 = Task("logiqa", "acc_norm", "LogiQA")
+NUM_FEWSHOT = 0  # Change with your few shot
+# ---------------------------------------------------
 # Your leaderboard name
 """
 # Which evaluations are you running? how can people reproduce what you have?
+LLM_BENCHMARKS_TEXT = """
 ## How it works
 ## Reproducibility
 model = AutoModel.from_pretrained("your model name", revision=revision)
 tokenizer = AutoTokenizer.from_pretrained("your model name", revision=revision)
 ```
+If this step fails, follow the error messages to debug your model before submitting it. It's likely your model has been
+improperly uploaded.
 Note: make sure your model is public!
+Note: if your model needs `use_remote_code=True`, we do not support this option yet but we are working on adding it,
+stay posted!
 ### 2) Convert your model weights to [safetensors](https://huggingface.co/docs/safetensors/index)
+It's a new format for storing weights which is safer and faster to load and use. It will also allow us to add the number
+ of parameters of your model to the `Extended Viewer`!
 ### 3) Make sure your model has an open license!
 This is a leaderboard for Open LLMs, and we'd love for as many people as possible to know they can use your model 🤗
 ## In case of model failure
 If your model is displayed in the `FAILED` category, its execution stopped.
 Make sure you have followed the above steps first.
+If everything is done, check you can launch the EleutherAIHarness on your model locally, using the above command without
+ modifications (you can add `--limit` to limit the number of examples per task).
 """
 CITATION_BUTTON_LABEL = "Copy the following snippet to cite these results"

app.py → src/encodechka/app.py RENAMED Viewed

@@ -1,10 +1,6 @@
-import subprocess
 import gradio as gr
 import pandas as pd
-from apscheduler.schedulers.background import BackgroundScheduler
-from huggingface_hub import snapshot_download
-from src.about import (
     CITATION_BUTTON_LABEL,
     CITATION_BUTTON_TEXT,
     EVALUATION_QUEUE_TEXT,
@@ -12,8 +8,9 @@ from src.about import (
     LLM_BENCHMARKS_TEXT,
     TITLE,
 )
-from src.display.css_html_js import custom_css
-from src.display.utils import (
     BENCHMARK_COLS,
     COLS,
     EVAL_COLS,
@@ -22,29 +19,50 @@ from src.display.utils import (
     TYPES,
     AutoEvalColumn,
     ModelType,
-    fields,
     WeightType,
-    Precision
 )
-from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, QUEUE_REPO, REPO_ID, RESULTS_REPO, TOKEN
-from src.populate import get_evaluation_queue_df, get_leaderboard_df
-from src.submission.submit import add_new_eval
 def restart_space():
     API.restart_space(repo_id=REPO_ID)
 try:
     print(EVAL_REQUESTS_PATH)
     snapshot_download(
-        repo_id=QUEUE_REPO, local_dir=EVAL_REQUESTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
     )
 except Exception:
     restart_space()
 try:
     print(EVAL_RESULTS_PATH)
     snapshot_download(
-        repo_id=RESULTS_REPO, local_dir=EVAL_RESULTS_PATH, repo_type="dataset", tqdm_class=None, etag_timeout=30, token=TOKEN
     )
 except Exception:
     restart_space()
@@ -86,9 +104,7 @@ def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
         AutoEvalColumn.model.name,
     ]
     # We use COLS to maintain sorting
-    filtered_df = df[
-        always_here_cols + [c for c in COLS if c in df.columns and c in columns]
-    ]
     return filtered_df
@@ -105,24 +121,32 @@ def filter_queries(query: str, filtered_df: pd.DataFrame) -> pd.DataFrame:
         if len(final_df) > 0:
             filtered_df = pd.concat(final_df)
             filtered_df = filtered_df.drop_duplicates(
-                subset=[AutoEvalColumn.model.name, AutoEvalColumn.precision.name, AutoEvalColumn.revision.name]
             )
     return filtered_df
 def filter_models(
-    df: pd.DataFrame, type_query: list, size_query: list, precision_query: list, show_deleted: bool
 ) -> pd.DataFrame:
     # Show all models
     if show_deleted:
         filtered_df = df
     else:  # Show only still on the hub models
-        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] == True]
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
-    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin(precision_query + ["None"])]
     numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
     params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
@@ -149,11 +173,7 @@ with demo:
                         )
                     with gr.Row():
                         shown_columns = gr.CheckboxGroup(
-                            choices=[
-                                c.name
-                                for c in fields(AutoEvalColumn)
-                                if not c.hidden and not c.never_hidden
-                            ],
                             value=[
                                 c.name
                                 for c in fields(AutoEvalColumn)
@@ -165,10 +185,12 @@ with demo:
                         )
                     with gr.Row():
                         deleted_models_visibility = gr.Checkbox(
-                            value=False, label="Show gated/private/deleted models", interactive=True
                         )
                 with gr.Column(min_width=320):
-                    #with gr.Box(elem_id="box-filter"):
                     filter_columns_type = gr.CheckboxGroup(
                         label="Model types",
                         choices=[t.to_str() for t in ModelType],
@@ -192,10 +214,7 @@ with demo:
                     )
             leaderboard_table = gr.components.Dataframe(
-                value=leaderboard_df[
-                    [c.name for c in fields(AutoEvalColumn) if c.never_hidden]
-                    + shown_columns.value
-                ],
                 headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                 datatype=TYPES,
                 elem_id="leaderboard-table",
@@ -223,7 +242,13 @@ with demo:
                 ],
                 leaderboard_table,
             )
-            for selector in [shown_columns, filter_columns_type, filter_columns_precision, filter_columns_size, deleted_models_visibility]:
                 selector.change(
                     update_table,
                     [
@@ -314,20 +339,20 @@ with demo:
                     )
                     base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
-            submit_button = gr.Button("Submit Eval")
-            submission_result = gr.Markdown()
-            submit_button.click(
-                add_new_eval,
-                [
-                    model_name_textbox,
-                    base_model_name_textbox,
-                    revision_name_textbox,
-                    precision,
-                    weight_type,
-                    model_type,
-                ],
-                submission_result,
-            )
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
@@ -342,4 +367,4 @@ with demo:
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=1800)
 scheduler.start()
-demo.queue(default_concurrency_limit=40).launch()

 import gradio as gr
 import pandas as pd
+from about import (
     CITATION_BUTTON_LABEL,
     CITATION_BUTTON_TEXT,
     EVALUATION_QUEUE_TEXT,
     LLM_BENCHMARKS_TEXT,
     TITLE,
 )
+from apscheduler.schedulers.background import BackgroundScheduler
+from display.css_html_js import custom_css
+from display.utils import (
     BENCHMARK_COLS,
     COLS,
     EVAL_COLS,
     TYPES,
     AutoEvalColumn,
     ModelType,
+    Precision,
     WeightType,
+    fields,
 )
+from envs import (
+    API,
+    EVAL_REQUESTS_PATH,
+    EVAL_RESULTS_PATH,
+    QUEUE_REPO,
+    REPO_ID,
+    RESULTS_REPO,
+    TOKEN,
+)
+from huggingface_hub import snapshot_download
+from populate import get_evaluation_queue_df, get_leaderboard_df
+# from submission.submit import add_new_eval
 def restart_space():
     API.restart_space(repo_id=REPO_ID)
 try:
     print(EVAL_REQUESTS_PATH)
     snapshot_download(
+        repo_id=QUEUE_REPO,
+        local_dir=EVAL_REQUESTS_PATH,
+        repo_type="dataset",
+        tqdm_class=None,
+        etag_timeout=30,
+        token=TOKEN,
     )
 except Exception:
     restart_space()
 try:
     print(EVAL_RESULTS_PATH)
     snapshot_download(
+        repo_id=RESULTS_REPO,
+        local_dir=EVAL_RESULTS_PATH,
+        repo_type="dataset",
+        tqdm_class=None,
+        etag_timeout=30,
+        token=TOKEN,
     )
 except Exception:
     restart_space()
         AutoEvalColumn.model.name,
     ]
     # We use COLS to maintain sorting
+    filtered_df = df[always_here_cols + [c for c in COLS if c in df.columns and c in columns]]
     return filtered_df
         if len(final_df) > 0:
             filtered_df = pd.concat(final_df)
             filtered_df = filtered_df.drop_duplicates(
+                subset=[
+                    AutoEvalColumn.model.name,
+                    AutoEvalColumn.precision.name,
+                    AutoEvalColumn.revision.name,
+                ]
             )
     return filtered_df
 def filter_models(
+    df: pd.DataFrame,
+    type_query: list,
+    size_query: list,
+    precision_query: list,
+    show_deleted: bool,
 ) -> pd.DataFrame:
     # Show all models
     if show_deleted:
         filtered_df = df
     else:  # Show only still on the hub models
+        filtered_df = df[df[AutoEvalColumn.still_on_hub.name] is True]
     type_emoji = [t[0] for t in type_query]
     filtered_df = filtered_df.loc[df[AutoEvalColumn.model_type_symbol.name].isin(type_emoji)]
+    filtered_df = filtered_df.loc[df[AutoEvalColumn.precision.name].isin([*precision_query, "None"])]
     numeric_interval = pd.IntervalIndex(sorted([NUMERIC_INTERVALS[s] for s in size_query]))
     params_column = pd.to_numeric(df[AutoEvalColumn.params.name], errors="coerce")
                         )
                     with gr.Row():
                         shown_columns = gr.CheckboxGroup(
+                            choices=[c.name for c in fields(AutoEvalColumn) if not c.hidden and not c.never_hidden],
                             value=[
                                 c.name
                                 for c in fields(AutoEvalColumn)
                         )
                     with gr.Row():
                         deleted_models_visibility = gr.Checkbox(
+                            value=False,
+                            label="Show gated/private/deleted models",
+                            interactive=True,
                         )
                 with gr.Column(min_width=320):
+                    # with gr.Box(elem_id="box-filter"):
                     filter_columns_type = gr.CheckboxGroup(
                         label="Model types",
                         choices=[t.to_str() for t in ModelType],
                     )
             leaderboard_table = gr.components.Dataframe(
+                value=leaderboard_df[[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value],
                 headers=[c.name for c in fields(AutoEvalColumn) if c.never_hidden] + shown_columns.value,
                 datatype=TYPES,
                 elem_id="leaderboard-table",
                 ],
                 leaderboard_table,
             )
+            for selector in [
+                shown_columns,
+                filter_columns_type,
+                filter_columns_precision,
+                filter_columns_size,
+                deleted_models_visibility,
+            ]:
                 selector.change(
                     update_table,
                     [
                     )
                     base_model_name_textbox = gr.Textbox(label="Base model (for delta or adapter weights)")
+            # submit_button = gr.Button("Submit Eval")
+            # submission_result = gr.Markdown()
+            # submit_button.click(
+            #     add_new_eval,
+            #     [
+            #         model_name_textbox,
+            #         base_model_name_textbox,
+            #         revision_name_textbox,
+            #         precision,
+            #         weight_type,
+            #         model_type,
+            #     ],
+            #     submission_result,
+            # )
     with gr.Row():
         with gr.Accordion("📙 Citation", open=False):
 scheduler = BackgroundScheduler()
 scheduler.add_job(restart_space, "interval", seconds=1800)
 scheduler.start()
+demo.queue(default_concurrency_limit=40).launch()

src/encodechka/display/__init__.py ADDED Viewed

File without changes

src/{display → encodechka/display}/css_html_js.py RENAMED Viewed

@@ -33,7 +33,7 @@ custom_css = """
     background: none;
     border: none;
 }
 #search-bar {
     padding: 0px;
 }
@@ -77,7 +77,7 @@ table th:first-child {
 #filter_type label > .wrap{
     width: 103px;
 }
-#filter_type label > .wrap .wrap-inner{
     padding: 2px;
 }
 #filter_type label > .wrap .wrap-inner input{

     background: none;
     border: none;
 }
 #search-bar {
     padding: 0px;
 }
 #filter_type label > .wrap{
     width: 103px;
 }
+#filter_type label > .wrap .wrap-inner{
     padding: 2px;
 }
 #filter_type label > .wrap .wrap-inner input{

src/{display → encodechka/display}/formatting.py RENAMED Viewed

File without changes

src/{display → encodechka/display}/utils.py RENAMED Viewed

@@ -3,7 +3,8 @@ from enum import Enum
 import pandas as pd
-from src.about import Tasks
 def fields(raw_class):
     return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
@@ -20,12 +21,25 @@ class ColumnContent:
     hidden: bool = False
     never_hidden: bool = False
 ## Leaderboard columns
 auto_eval_column_dict = []
 # Init
-auto_eval_column_dict.append(["model_type_symbol", ColumnContent, ColumnContent("T", "str", True, never_hidden=True)])
-auto_eval_column_dict.append(["model", ColumnContent, ColumnContent("Model", "markdown", True, never_hidden=True)])
-#Scores
 auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
@@ -37,12 +51,19 @@ auto_eval_column_dict.append(["precision", ColumnContent, ColumnContent("Precisi
 auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
 auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
 auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
-auto_eval_column_dict.append(["still_on_hub", ColumnContent, ColumnContent("Available on the hub", "bool", False)])
 auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
 # We use make dataclass to dynamically fill the scores from Tasks
 AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
 ## For the queue columns in the submission tab
 @dataclass(frozen=True)
 class EvalQueueColumn:  # Queue column
@@ -53,12 +74,13 @@ class EvalQueueColumn:  # Queue column
     weight_type = ColumnContent("weight_type", "str", "Original")
     status = ColumnContent("status", "str", True)
 ## All the model information that we might need
 @dataclass
 class ModelDetails:
     name: str
     display_name: str = ""
-    symbol: str = "" # emoji
 class ModelType(Enum):
@@ -83,18 +105,20 @@ class ModelType(Enum):
             return ModelType.IFT
         return ModelType.Unknown
 class WeightType(Enum):
     Adapter = ModelDetails("Adapter")
     Original = ModelDetails("Original")
     Delta = ModelDetails("Delta")
 class Precision(Enum):
     float16 = ModelDetails("float16")
     bfloat16 = ModelDetails("bfloat16")
     float32 = ModelDetails("float32")
-    #qt_8bit = ModelDetails("8bit")
-    #qt_4bit = ModelDetails("4bit")
-    #qt_GPTQ = ModelDetails("GPTQ")
     Unknown = ModelDetails("?")
     def from_str(precision):
@@ -104,14 +128,15 @@ class Precision(Enum):
             return Precision.bfloat16
         if precision in ["float32"]:
             return Precision.float32
-        #if precision in ["8bit"]:
         #    return Precision.qt_8bit
-        #if precision in ["4bit"]:
         #    return Precision.qt_4bit
-        #if precision in ["GPTQ", "None"]:
         #    return Precision.qt_GPTQ
         return Precision.Unknown
 # Column selection
 COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
 TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]

 import pandas as pd
+from ..about import Tasks
 def fields(raw_class):
     return [v for k, v in raw_class.__dict__.items() if k[:2] != "__" and k[-2:] != "__"]
     hidden: bool = False
     never_hidden: bool = False
 ## Leaderboard columns
 auto_eval_column_dict = []
 # Init
+auto_eval_column_dict.append(
+    [
+        "model_type_symbol",
+        ColumnContent,
+        ColumnContent("T", "str", True, never_hidden=True),
+    ]
+)
+auto_eval_column_dict.append(
+    [
+        "model",
+        ColumnContent,
+        ColumnContent("Model", "markdown", True, never_hidden=True),
+    ]
+)
+# Scores
 auto_eval_column_dict.append(["average", ColumnContent, ColumnContent("Average ⬆️", "number", True)])
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, ColumnContent(task.value.col_name, "number", True)])
 auto_eval_column_dict.append(["license", ColumnContent, ColumnContent("Hub License", "str", False)])
 auto_eval_column_dict.append(["params", ColumnContent, ColumnContent("#Params (B)", "number", False)])
 auto_eval_column_dict.append(["likes", ColumnContent, ColumnContent("Hub ❤️", "number", False)])
+auto_eval_column_dict.append(
+    [
+        "still_on_hub",
+        ColumnContent,
+        ColumnContent("Available on the hub", "bool", False),
+    ]
+)
 auto_eval_column_dict.append(["revision", ColumnContent, ColumnContent("Model sha", "str", False, False)])
 # We use make dataclass to dynamically fill the scores from Tasks
 AutoEvalColumn = make_dataclass("AutoEvalColumn", auto_eval_column_dict, frozen=True)
 ## For the queue columns in the submission tab
 @dataclass(frozen=True)
 class EvalQueueColumn:  # Queue column
     weight_type = ColumnContent("weight_type", "str", "Original")
     status = ColumnContent("status", "str", True)
 ## All the model information that we might need
 @dataclass
 class ModelDetails:
     name: str
     display_name: str = ""
+    symbol: str = ""  # emoji
 class ModelType(Enum):
             return ModelType.IFT
         return ModelType.Unknown
 class WeightType(Enum):
     Adapter = ModelDetails("Adapter")
     Original = ModelDetails("Original")
     Delta = ModelDetails("Delta")
 class Precision(Enum):
     float16 = ModelDetails("float16")
     bfloat16 = ModelDetails("bfloat16")
     float32 = ModelDetails("float32")
+    # qt_8bit = ModelDetails("8bit")
+    # qt_4bit = ModelDetails("4bit")
+    # qt_GPTQ = ModelDetails("GPTQ")
     Unknown = ModelDetails("?")
     def from_str(precision):
             return Precision.bfloat16
         if precision in ["float32"]:
             return Precision.float32
+        # if precision in ["8bit"]:
         #    return Precision.qt_8bit
+        # if precision in ["4bit"]:
         #    return Precision.qt_4bit
+        # if precision in ["GPTQ", "None"]:
         #    return Precision.qt_GPTQ
         return Precision.Unknown
 # Column selection
 COLS = [c.name for c in fields(AutoEvalColumn) if not c.hidden]
 TYPES = [c.type for c in fields(AutoEvalColumn) if not c.hidden]

src/{envs.py → encodechka/envs.py} RENAMED Viewed

@@ -4,9 +4,9 @@ from huggingface_hub import HfApi
 # Info to change for your repository
 # ----------------------------------
-TOKEN = os.environ.get("TOKEN") # A read/write token for your org
-OWNER = "demo-leaderboard-backend" # Change to your org - don't forget to create a results and request dataset, with the correct format!
 # ----------------------------------
 REPO_ID = f"{OWNER}/leaderboard"
@@ -14,7 +14,7 @@ QUEUE_REPO = f"{OWNER}/requests"
 RESULTS_REPO = f"{OWNER}/results"
 # If you setup a cache later, just change HF_HOME
-CACHE_PATH=os.getenv("HF_HOME", ".")
 # Local caches
 EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")

 # Info to change for your repository
 # ----------------------------------
+TOKEN = os.environ.get("TOKEN")  # A read/write token for your org
+OWNER = "demo-leaderboard-backend"  # Change to your org - don't forget to create a results and request dataset, with the correct format!
 # ----------------------------------
 REPO_ID = f"{OWNER}/leaderboard"
 RESULTS_REPO = f"{OWNER}/results"
 # If you setup a cache later, just change HF_HOME
+CACHE_PATH = os.getenv("HF_HOME", ".")
 # Local caches
 EVAL_REQUESTS_PATH = os.path.join(CACHE_PATH, "eval-queue")

src/encodechka/leaderboard/__init__.py ADDED Viewed

File without changes

src/{leaderboard → encodechka/leaderboard}/read_evals.py RENAMED Viewed

@@ -1,35 +1,34 @@
 import glob
 import json
-import math
 import os
 from dataclasses import dataclass
 import dateutil
 import numpy as np
-from src.display.formatting import make_clickable_model
-from src.display.utils import AutoEvalColumn, ModelType, Tasks, Precision, WeightType
-from src.submission.check_validity import is_model_on_hub
 @dataclass
 class EvalResult:
-    """Represents one full evaluation. Built from a combination of the result and request file for a given run.
-    """
-    eval_name: str # org_model_precision (uid)
-    full_model: str # org/model (path on hub)
-    org: str
     model: str
-    revision: str # commit hash, "" if main
     results: dict
     precision: Precision = Precision.Unknown
-    model_type: ModelType = ModelType.Unknown # Pretrained, fine tuned, ...
-    weight_type: WeightType = WeightType.Original # Original or Adapter
-    architecture: str = "Unknown"
     license: str = "?"
     likes: int = 0
     num_params: int = 0
-    date: str = "" # submission date of request file
     still_on_hub: bool = False
     @classmethod
@@ -58,7 +57,10 @@ class EvalResult:
         full_model = "/".join(org_and_model)
         still_on_hub, _, model_config = is_model_on_hub(
-            full_model, config.get("model_sha", "main"), trust_remote_code=True, test_tokenizer=False
         )
         architecture = "?"
         if model_config is not None:
@@ -85,10 +87,10 @@ class EvalResult:
             org=org,
             model=model,
             results=results,
-            precision=precision,
-            revision= config.get("model_sha", ""),
             still_on_hub=still_on_hub,
-            architecture=architecture
         )
     def update_with_request_file(self, requests_path):
@@ -96,7 +98,7 @@ class EvalResult:
         request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
         try:
-            with open(request_file, "r") as f:
                 request = json.load(f)
             self.model_type = ModelType.from_str(request.get("model_type", ""))
             self.weight_type = WeightType[request.get("weight_type", "Original")]
@@ -144,12 +146,9 @@ def get_request_file_for_model(requests_path, model_name, precision):
     request_file = ""
     request_files = sorted(request_files, reverse=True)
     for tmp_request_file in request_files:
-        with open(tmp_request_file, "r") as f:
             req_content = json.load(f)
-            if (
-                req_content["status"] in ["FINISHED"]
-                and req_content["precision"] == precision.split(".")[-1]
-            ):
                 request_file = tmp_request_file
     return request_file
@@ -188,7 +187,7 @@ def get_raw_eval_results(results_path: str, requests_path: str) -> list[EvalResu
     results = []
     for v in eval_results.values():
         try:
-            v.to_dict() # we test if the dict version is complete
             results.append(v)
         except KeyError:  # not all eval values present
             continue

 import glob
 import json
 import os
 from dataclasses import dataclass
 import dateutil
 import numpy as np
+from ..display.formatting import make_clickable_model
+from ..display.utils import AutoEvalColumn, ModelType, Precision, Tasks, WeightType
+from ..submission.check_validity import is_model_on_hub
 @dataclass
 class EvalResult:
+    """Represents one full evaluation. Built from a combination of the result and request file for a given run."""
+    eval_name: str  # org_model_precision (uid)
+    full_model: str  # org/model (path on hub)
+    org: str
     model: str
+    revision: str  # commit hash, "" if main
     results: dict
     precision: Precision = Precision.Unknown
+    model_type: ModelType = ModelType.Unknown  # Pretrained, fine tuned, ...
+    weight_type: WeightType = WeightType.Original  # Original or Adapter
+    architecture: str = "Unknown"
     license: str = "?"
     likes: int = 0
     num_params: int = 0
+    date: str = ""  # submission date of request file
     still_on_hub: bool = False
     @classmethod
         full_model = "/".join(org_and_model)
         still_on_hub, _, model_config = is_model_on_hub(
+            full_model,
+            config.get("model_sha", "main"),
+            trust_remote_code=True,
+            test_tokenizer=False,
         )
         architecture = "?"
         if model_config is not None:
             org=org,
             model=model,
             results=results,
+            precision=precision,
+            revision=config.get("model_sha", ""),
             still_on_hub=still_on_hub,
+            architecture=architecture,
         )
     def update_with_request_file(self, requests_path):
         request_file = get_request_file_for_model(requests_path, self.full_model, self.precision.value.name)
         try:
+            with open(request_file) as f:
                 request = json.load(f)
             self.model_type = ModelType.from_str(request.get("model_type", ""))
             self.weight_type = WeightType[request.get("weight_type", "Original")]
     request_file = ""
     request_files = sorted(request_files, reverse=True)
     for tmp_request_file in request_files:
+        with open(tmp_request_file) as f:
             req_content = json.load(f)
+            if req_content["status"] in ["FINISHED"] and req_content["precision"] == precision.split(".")[-1]:
                 request_file = tmp_request_file
     return request_file
     results = []
     for v in eval_results.values():
         try:
+            v.to_dict()  # we test if the dict version is complete
             results.append(v)
         except KeyError:  # not all eval values present
             continue

src/{populate.py → encodechka/populate.py} RENAMED Viewed

@@ -2,10 +2,9 @@ import json
 import os
 import pandas as pd
-from src.display.formatting import has_no_nan_values, make_clickable_model
-from src.display.utils import AutoEvalColumn, EvalQueueColumn
-from src.leaderboard.read_evals import get_raw_eval_results
 def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:

 import os
 import pandas as pd
+from display.formatting import has_no_nan_values, make_clickable_model
+from display.utils import AutoEvalColumn, EvalQueueColumn
+from leaderboard.read_evals import get_raw_eval_results
 def get_leaderboard_df(results_path: str, requests_path: str, cols: list, benchmark_cols: list) -> pd.DataFrame:

src/encodechka/submission/__init__.py ADDED Viewed

File without changes

src/encodechka/submission/check_validity.py ADDED Viewed

	@@ -0,0 +1,124 @@

+# import json
+# import os
+# from collections import defaultdict
+#
+# import huggingface_hub
+# from huggingface_hub import ModelCard
+# from huggingface_hub.hf_api import ModelInfo
+# from transformers import AutoConfig
+# from transformers.models.auto.tokenization_auto import AutoTokenizer
+#
+#
+# def check_model_card(repo_id: str) -> tuple[bool, str]:
+#     """Checks if the model card and license exist and have been filled"""
+#     try:
+#         card = ModelCard.load(repo_id)
+#     except huggingface_hub.utils.EntryNotFoundError:
+#         return (
+#             False,
+#             "Please add a model card to your model to explain how you trained/fine-tuned it.",
+#         )
+#
+#     # Enforce license metadata
+#     if card.data.license is None:
+#         if not ("license_name" in card.data and "license_link" in card.data):
+#             return False, (
+#                 "License not found. Please add a license to your model card using the `license` metadata or a"
+#                 " `license_name`/`license_link` pair."
+#             )
+#
+#     # Enforce card content
+#     if len(card.text) < 200:
+#         return False, "Please add a description to your model card, it is too short."
+#
+#     return True, ""
+#
+#
+# def is_model_on_hub(
+#     model_name: str,
+#     revision: str,
+#     token: str | None = None,
+#     trust_remote_code=False,
+#     test_tokenizer=False,
+# ) -> tuple[bool, str]:
+#     """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
+#     try:
+#         config = AutoConfig.from_pretrained(
+#             model_name,
+#             revision=revision,
+#             trust_remote_code=trust_remote_code,
+#             token=token,
+#         )
+#         if test_tokenizer:
+#             try:
+#                 tk = AutoTokenizer.from_pretrained(
+#                     model_name,
+#                     revision=revision,
+#                     trust_remote_code=trust_remote_code,
+#                     token=token,
+#                 )
+#             except ValueError as e:
+#                 return (
+#                     False,
+#                     f"uses a tokenizer which is not in a transformers release: {e}",
+#                     None,
+#                 )
+#             except Exception:
+#                 return (
+#                     False,
+#                     "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?",
+#                     None,
+#                 )
+#         return True, None, config
+#
+#     except ValueError:
+#         return (
+#             False,
+#             "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
+#             None,
+#         )
+#
+#     except Exception:
+#         return False, "was not found on hub!", None
+#
+#
+# def get_model_size(model_info: ModelInfo, precision: str):
+#     """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
+#     try:
+#         model_size = round(model_info.safetensors["total"] / 1e9, 3)
+#     except (AttributeError, TypeError):
+#         return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
+#
+#     size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
+#     model_size = size_factor * model_size
+#     return model_size
+#
+#
+# def get_model_arch(model_info: ModelInfo):
+#     """Gets the model architecture from the configuration"""
+#     return model_info.config.get("architectures", "Unknown")
+#
+#
+# def already_submitted_models(requested_models_dir: str) -> set[str]:
+#     """Gather a list of already submitted models to avoid duplicates"""
+#     depth = 1
+#     file_names = []
+#     users_to_submission_dates = defaultdict(list)
+#
+#     for root, _, files in os.walk(requested_models_dir):
+#         current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
+#         if current_depth == depth:
+#             for file in files:
+#                 if not file.endswith(".json"):
+#                     continue
+#                 with open(os.path.join(root, file)) as f:
+#                     info = json.load(f)
+#                     file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
+#
+#                     # Select organisation
+#                     if info["model"].count("/") == 0 or "submitted_time" not in info:
+#                         continue
+#                     organisation, _ = info["model"].split("/")
+#                     users_to_submission_dates[organisation].append(info["submitted_time"])
+#
+#     return set(file_names), users_to_submission_dates

src/encodechka/submission/submit.py ADDED Viewed

	@@ -0,0 +1,122 @@

+# import json
+# import os
+# from datetime import datetime, timezone
+#
+# from ..display.formatting import styled_error, styled_message, styled_warning
+# from ..envs import API, EVAL_REQUESTS_PATH, QUEUE_REPO, TOKEN
+# from .check_validity import (
+#     already_submitted_models,
+#     check_model_card,
+#     get_model_size,
+#     is_model_on_hub,
+# )
+#
+# REQUESTED_MODELS = None
+# USERS_TO_SUBMISSION_DATES = None
+#
+#
+# def add_new_eval(
+#     model: str,
+#     base_model: str,
+#     revision: str,
+#     precision: str,
+#     weight_type: str,
+#     model_type: str,
+# ):
+#     global REQUESTED_MODELS
+#     global USERS_TO_SUBMISSION_DATES
+#     if not REQUESTED_MODELS:
+#         REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
+#
+#     user_name = ""
+#     model_path = model
+#     if "/" in model:
+#         user_name = model.split("/")[0]
+#         model_path = model.split("/")[1]
+#
+#     precision = precision.split(" ")[0]
+#     current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
+#
+#     if model_type is None or model_type == "":
+#         return styled_error("Please select a model type.")
+#
+#     # Does the model actually exist?
+#     if revision == "":
+#         revision = "main"
+#
+#     # Is the model on the hub?
+#     if weight_type in ["Delta", "Adapter"]:
+#         base_model_on_hub, error, _ = is_model_on_hub(
+#             model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True
+#         )
+#         if not base_model_on_hub:
+#             return styled_error(f'Base model "{base_model}" {error}')
+#
+#     if not weight_type == "Adapter":
+#         model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
+#         if not model_on_hub:
+#             return styled_error(f'Model "{model}" {error}')
+#
+#     # Is the model info correctly filled?
+#     try:
+#         model_info = API.model_info(repo_id=model, revision=revision)
+#     except Exception:
+#         return styled_error("Could not get your model information. Please fill it up properly.")
+#
+#     model_size = get_model_size(model_info=model_info, precision=precision)
+#
+#     # Were the model card and license filled?
+#     try:
+#         license = model_info.cardData["license"]
+#     except Exception:
+#         return styled_error("Please select a license for your model")
+#
+#     modelcard_OK, error_msg = check_model_card(model)
+#     if not modelcard_OK:
+#         return styled_error(error_msg)
+#
+#     # Seems good, creating the eval
+#     print("Adding new eval")
+#
+#     eval_entry = {
+#         "model": model,
+#         "base_model": base_model,
+#         "revision": revision,
+#         "precision": precision,
+#         "weight_type": weight_type,
+#         "status": "PENDING",
+#         "submitted_time": current_time,
+#         "model_type": model_type,
+#         "likes": model_info.likes,
+#         "params": model_size,
+#         "license": license,
+#         "private": False,
+#     }
+#
+#     # Check for duplicate submission
+#     if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
+#         return styled_warning("This model has been already submitted.")
+#
+#     print("Creating eval file")
+#     OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
+#     os.makedirs(OUT_DIR, exist_ok=True)
+#     out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
+#
+#     with open(out_path, "w") as f:
+#         f.write(json.dumps(eval_entry))
+#
+#     print("Uploading eval file")
+#     API.upload_file(
+#         path_or_fileobj=out_path,
+#         path_in_repo=out_path.split("eval-queue/")[1],
+#         repo_id=QUEUE_REPO,
+#         repo_type="dataset",
+#         commit_message=f"Add {model} to eval queue",
+#     )
+#
+#     # Remove the local file
+#     os.remove(out_path)
+#
+#     return styled_message(
+#         "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
+#     )

src/submission/check_validity.py DELETED Viewed

@@ -1,99 +0,0 @@
-import json
-import os
-import re
-from collections import defaultdict
-from datetime import datetime, timedelta, timezone
-import huggingface_hub
-from huggingface_hub import ModelCard
-from huggingface_hub.hf_api import ModelInfo
-from transformers import AutoConfig
-from transformers.models.auto.tokenization_auto import AutoTokenizer
-def check_model_card(repo_id: str) -> tuple[bool, str]:
-    """Checks if the model card and license exist and have been filled"""
-    try:
-        card = ModelCard.load(repo_id)
-    except huggingface_hub.utils.EntryNotFoundError:
-        return False, "Please add a model card to your model to explain how you trained/fine-tuned it."
-    # Enforce license metadata
-    if card.data.license is None:
-        if not ("license_name" in card.data and "license_link" in card.data):
-            return False, (
-                "License not found. Please add a license to your model card using the `license` metadata or a"
-                " `license_name`/`license_link` pair."
-            )
-    # Enforce card content
-    if len(card.text) < 200:
-        return False, "Please add a description to your model card, it is too short."
-    return True, ""
-def is_model_on_hub(model_name: str, revision: str, token: str = None, trust_remote_code=False, test_tokenizer=False) -> tuple[bool, str]:
-    """Checks if the model model_name is on the hub, and whether it (and its tokenizer) can be loaded with AutoClasses."""
-    try:
-        config = AutoConfig.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
-        if test_tokenizer:
-            try:
-                tk = AutoTokenizer.from_pretrained(model_name, revision=revision, trust_remote_code=trust_remote_code, token=token)
-            except ValueError as e:
-                return (
-                    False,
-                    f"uses a tokenizer which is not in a transformers release: {e}",
-                    None
-                )
-            except Exception as e:
-                return (False, "'s tokenizer cannot be loaded. Is your tokenizer class in a stable transformers release, and correctly configured?", None)
-        return True, None, config
-    except ValueError:
-        return (
-            False,
-            "needs to be launched with `trust_remote_code=True`. For safety reason, we do not allow these models to be automatically submitted to the leaderboard.",
-            None
-        )
-    except Exception as e:
-        return False, "was not found on hub!", None
-def get_model_size(model_info: ModelInfo, precision: str):
-    """Gets the model size from the configuration, or the model name if the configuration does not contain the information."""
-    try:
-        model_size = round(model_info.safetensors["total"] / 1e9, 3)
-    except (AttributeError, TypeError):
-        return 0  # Unknown model sizes are indicated as 0, see NUMERIC_INTERVALS in app.py
-    size_factor = 8 if (precision == "GPTQ" or "gptq" in model_info.modelId.lower()) else 1
-    model_size = size_factor * model_size
-    return model_size
-def get_model_arch(model_info: ModelInfo):
-    """Gets the model architecture from the configuration"""
-    return model_info.config.get("architectures", "Unknown")
-def already_submitted_models(requested_models_dir: str) -> set[str]:
-    """Gather a list of already submitted models to avoid duplicates"""
-    depth = 1
-    file_names = []
-    users_to_submission_dates = defaultdict(list)
-    for root, _, files in os.walk(requested_models_dir):
-        current_depth = root.count(os.sep) - requested_models_dir.count(os.sep)
-        if current_depth == depth:
-            for file in files:
-                if not file.endswith(".json"):
-                    continue
-                with open(os.path.join(root, file), "r") as f:
-                    info = json.load(f)
-                    file_names.append(f"{info['model']}_{info['revision']}_{info['precision']}")
-                    # Select organisation
-                    if info["model"].count("/") == 0 or "submitted_time" not in info:
-                        continue
-                    organisation, _ = info["model"].split("/")
-                    users_to_submission_dates[organisation].append(info["submitted_time"])
-    return set(file_names), users_to_submission_dates

src/submission/submit.py DELETED Viewed

@@ -1,119 +0,0 @@
-import json
-import os
-from datetime import datetime, timezone
-from src.display.formatting import styled_error, styled_message, styled_warning
-from src.envs import API, EVAL_REQUESTS_PATH, TOKEN, QUEUE_REPO
-from src.submission.check_validity import (
-    already_submitted_models,
-    check_model_card,
-    get_model_size,
-    is_model_on_hub,
-)
-REQUESTED_MODELS = None
-USERS_TO_SUBMISSION_DATES = None
-def add_new_eval(
-    model: str,
-    base_model: str,
-    revision: str,
-    precision: str,
-    weight_type: str,
-    model_type: str,
-):
-    global REQUESTED_MODELS
-    global USERS_TO_SUBMISSION_DATES
-    if not REQUESTED_MODELS:
-        REQUESTED_MODELS, USERS_TO_SUBMISSION_DATES = already_submitted_models(EVAL_REQUESTS_PATH)
-    user_name = ""
-    model_path = model
-    if "/" in model:
-        user_name = model.split("/")[0]
-        model_path = model.split("/")[1]
-    precision = precision.split(" ")[0]
-    current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ")
-    if model_type is None or model_type == "":
-        return styled_error("Please select a model type.")
-    # Does the model actually exist?
-    if revision == "":
-        revision = "main"
-    # Is the model on the hub?
-    if weight_type in ["Delta", "Adapter"]:
-        base_model_on_hub, error, _ = is_model_on_hub(model_name=base_model, revision=revision, token=TOKEN, test_tokenizer=True)
-        if not base_model_on_hub:
-            return styled_error(f'Base model "{base_model}" {error}')
-    if not weight_type == "Adapter":
-        model_on_hub, error, _ = is_model_on_hub(model_name=model, revision=revision, token=TOKEN, test_tokenizer=True)
-        if not model_on_hub:
-            return styled_error(f'Model "{model}" {error}')
-    # Is the model info correctly filled?
-    try:
-        model_info = API.model_info(repo_id=model, revision=revision)
-    except Exception:
-        return styled_error("Could not get your model information. Please fill it up properly.")
-    model_size = get_model_size(model_info=model_info, precision=precision)
-    # Were the model card and license filled?
-    try:
-        license = model_info.cardData["license"]
-    except Exception:
-        return styled_error("Please select a license for your model")
-    modelcard_OK, error_msg = check_model_card(model)
-    if not modelcard_OK:
-        return styled_error(error_msg)
-    # Seems good, creating the eval
-    print("Adding new eval")
-    eval_entry = {
-        "model": model,
-        "base_model": base_model,
-        "revision": revision,
-        "precision": precision,
-        "weight_type": weight_type,
-        "status": "PENDING",
-        "submitted_time": current_time,
-        "model_type": model_type,
-        "likes": model_info.likes,
-        "params": model_size,
-        "license": license,
-        "private": False,
-    }
-    # Check for duplicate submission
-    if f"{model}_{revision}_{precision}" in REQUESTED_MODELS:
-        return styled_warning("This model has been already submitted.")
-    print("Creating eval file")
-    OUT_DIR = f"{EVAL_REQUESTS_PATH}/{user_name}"
-    os.makedirs(OUT_DIR, exist_ok=True)
-    out_path = f"{OUT_DIR}/{model_path}_eval_request_False_{precision}_{weight_type}.json"
-    with open(out_path, "w") as f:
-        f.write(json.dumps(eval_entry))
-    print("Uploading eval file")
-    API.upload_file(
-        path_or_fileobj=out_path,
-        path_in_repo=out_path.split("eval-queue/")[1],
-        repo_id=QUEUE_REPO,
-        repo_type="dataset",
-        commit_message=f"Add {model} to eval queue",
-    )
-    # Remove the local file
-    os.remove(out_path)
-    return styled_message(
-        "Your request has been submitted to the evaluation queue!\nPlease wait for up to an hour for the model to show in the PENDING list."
-    )

tests/__init__.py ADDED Viewed

File without changes