583 lines
24 KiB
Python
583 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2020 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import glob
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import importlib.util
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import os
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import re
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import black
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from doc_builder.style_doc import style_docstrings_in_code
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# All paths are set with the intent you should run this script from the root of the repo with the command
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# python utils/check_copies.py
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TRANSFORMERS_PATH = "src/transformers"
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PATH_TO_DOCS = "docs/source/en"
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REPO_PATH = "."
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# Mapping for files that are full copies of others (keys are copies, values the file to keep them up to data with)
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FULL_COPIES = {
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"examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
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"examples/flax/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py",
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}
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LOCALIZED_READMES = {
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# If the introduction or the conclusion of the list change, the prompts may need to be updated.
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"README.md": {
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"start_prompt": "🤗 Transformers currently provides the following architectures",
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"end_prompt": "1. Want to contribute a new model?",
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"format_model_list": (
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"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
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" {paper_authors}.{supplements}"
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),
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},
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"README_zh-hans.md": {
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"start_prompt": "🤗 Transformers 目前支持如下的架构",
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"end_prompt": "1. 想要贡献新的模型?",
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"format_model_list": (
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"**[{title}]({model_link})** (来自 {paper_affiliations}) 伴随论文 {paper_title_link} 由 {paper_authors}"
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" 发布。{supplements}"
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),
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},
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"README_zh-hant.md": {
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"start_prompt": "🤗 Transformers 目前支援以下的架構",
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"end_prompt": "1. 想要貢獻新的模型?",
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"format_model_list": (
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"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
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" {paper_authors}.{supplements}"
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),
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},
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"README_ko.md": {
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"start_prompt": "🤗 Transformers는 다음 모델들을 제공합니다",
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"end_prompt": "1. 새로운 모델을 올리고 싶나요?",
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"format_model_list": (
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"**[{title}]({model_link})** ({paper_affiliations} 에서 제공)은 {paper_authors}.{supplements}의"
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" {paper_title_link}논문과 함께 발표했습니다."
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),
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},
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"README_es.md": {
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"start_prompt": "🤗 Transformers actualmente proporciona las siguientes arquitecturas",
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"end_prompt": "1. ¿Quieres aportar un nuevo modelo?",
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"format_model_list": (
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"**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by"
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" {paper_authors}.{supplements}"
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),
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},
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"README_ja.md": {
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"start_prompt": "🤗Transformersは現在、以下のアーキテクチャを提供しています",
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"end_prompt": "1. 新しいモデルを投稿したいですか?",
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"format_model_list": (
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"**[{title}]({model_link})** ({paper_affiliations} から) {paper_authors}.{supplements} から公開された研究論文"
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" {paper_title_link}"
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),
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},
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"README_hd.md": {
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"start_prompt": "🤗 ट्रांसफॉर्मर वर्तमान में निम्नलिखित आर्किटेक्चर का समर्थन करते हैं",
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"end_prompt": "1. एक नए मॉडल में योगदान देना चाहते हैं?",
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"format_model_list": (
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"**[{title}]({model_link})** ({paper_affiliations} से) {paper_authors}.{supplements} द्वारा"
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"अनुसंधान पत्र {paper_title_link} के साथ जारी किया गया"
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),
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},
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}
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# This is to make sure the transformers module imported is the one in the repo.
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spec = importlib.util.spec_from_file_location(
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"transformers",
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os.path.join(TRANSFORMERS_PATH, "__init__.py"),
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submodule_search_locations=[TRANSFORMERS_PATH],
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)
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transformers_module = spec.loader.load_module()
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def _should_continue(line, indent):
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return line.startswith(indent) or len(line) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$", line) is not None
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def find_code_in_transformers(object_name):
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"""Find and return the code source code of `object_name`."""
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parts = object_name.split(".")
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i = 0
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# First let's find the module where our object lives.
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module = parts[i]
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while i < len(parts) and not os.path.isfile(os.path.join(TRANSFORMERS_PATH, f"{module}.py")):
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i += 1
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if i < len(parts):
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module = os.path.join(module, parts[i])
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if i >= len(parts):
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raise ValueError(
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f"`object_name` should begin with the name of a module of transformers but got {object_name}."
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)
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with open(os.path.join(TRANSFORMERS_PATH, f"{module}.py"), "r", encoding="utf-8", newline="\n") as f:
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lines = f.readlines()
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# Now let's find the class / func in the code!
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indent = ""
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line_index = 0
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for name in parts[i + 1 :]:
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while (
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line_index < len(lines) and re.search(rf"^{indent}(class|def)\s+{name}(\(|\:)", lines[line_index]) is None
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):
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line_index += 1
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indent += " "
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line_index += 1
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if line_index >= len(lines):
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raise ValueError(f" {object_name} does not match any function or class in {module}.")
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# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
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start_index = line_index
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while line_index < len(lines) and _should_continue(lines[line_index], indent):
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line_index += 1
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# Clean up empty lines at the end (if any).
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while len(lines[line_index - 1]) <= 1:
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line_index -= 1
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code_lines = lines[start_index:line_index]
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return "".join(code_lines)
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_re_copy_warning = re.compile(r"^(\s*)#\s*Copied from\s+transformers\.(\S+\.\S+)\s*($|\S.*$)")
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_re_replace_pattern = re.compile(r"^\s*(\S+)->(\S+)(\s+.*|$)")
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_re_fill_pattern = re.compile(r"<FILL\s+[^>]*>")
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def get_indent(code):
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lines = code.split("\n")
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idx = 0
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while idx < len(lines) and len(lines[idx]) == 0:
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idx += 1
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if idx < len(lines):
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return re.search(r"^(\s*)\S", lines[idx]).groups()[0]
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return ""
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def blackify(code):
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"""
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Applies the black part of our `make style` command to `code`.
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"""
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has_indent = len(get_indent(code)) > 0
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if has_indent:
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code = f"class Bla:\n{code}"
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mode = black.Mode(target_versions={black.TargetVersion.PY35}, line_length=119, preview=True)
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result = black.format_str(code, mode=mode)
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result, _ = style_docstrings_in_code(result)
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return result[len("class Bla:\n") :] if has_indent else result
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def is_copy_consistent(filename, overwrite=False):
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"""
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Check if the code commented as a copy in `filename` matches the original.
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Return the differences or overwrites the content depending on `overwrite`.
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"""
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with open(filename, "r", encoding="utf-8", newline="\n") as f:
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lines = f.readlines()
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diffs = []
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line_index = 0
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# Not a for loop cause `lines` is going to change (if `overwrite=True`).
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while line_index < len(lines):
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search = _re_copy_warning.search(lines[line_index])
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if search is None:
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line_index += 1
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continue
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# There is some copied code here, let's retrieve the original.
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indent, object_name, replace_pattern = search.groups()
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theoretical_code = find_code_in_transformers(object_name)
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theoretical_indent = get_indent(theoretical_code)
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start_index = line_index + 1 if indent == theoretical_indent else line_index + 2
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indent = theoretical_indent
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line_index = start_index
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# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
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should_continue = True
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while line_index < len(lines) and should_continue:
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line_index += 1
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if line_index >= len(lines):
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break
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line = lines[line_index]
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should_continue = _should_continue(line, indent) and re.search(f"^{indent}# End copy", line) is None
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# Clean up empty lines at the end (if any).
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while len(lines[line_index - 1]) <= 1:
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line_index -= 1
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observed_code_lines = lines[start_index:line_index]
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observed_code = "".join(observed_code_lines)
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# Before comparing, use the `replace_pattern` on the original code.
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if len(replace_pattern) > 0:
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patterns = replace_pattern.replace("with", "").split(",")
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patterns = [_re_replace_pattern.search(p) for p in patterns]
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for pattern in patterns:
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if pattern is None:
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continue
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obj1, obj2, option = pattern.groups()
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theoretical_code = re.sub(obj1, obj2, theoretical_code)
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if option.strip() == "all-casing":
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theoretical_code = re.sub(obj1.lower(), obj2.lower(), theoretical_code)
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theoretical_code = re.sub(obj1.upper(), obj2.upper(), theoretical_code)
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# Blackify after replacement. To be able to do that, we need the header (class or function definition)
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# from the previous line
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theoretical_code = blackify(lines[start_index - 1] + theoretical_code)
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theoretical_code = theoretical_code[len(lines[start_index - 1]) :]
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# Test for a diff and act accordingly.
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if observed_code != theoretical_code:
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diff_index = start_index + 1
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for observed_line, theoretical_line in zip(observed_code.split("\n"), theoretical_code.split("\n")):
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if observed_line != theoretical_line:
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break
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diff_index += 1
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diffs.append([object_name, diff_index])
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if overwrite:
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lines = lines[:start_index] + [theoretical_code] + lines[line_index:]
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line_index = start_index + 1
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if overwrite and len(diffs) > 0:
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# Warn the user a file has been modified.
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print(f"Detected changes, rewriting {filename}.")
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with open(filename, "w", encoding="utf-8", newline="\n") as f:
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f.writelines(lines)
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return diffs
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def check_copies(overwrite: bool = False):
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all_files = glob.glob(os.path.join(TRANSFORMERS_PATH, "**/*.py"), recursive=True)
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diffs = []
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for filename in all_files:
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new_diffs = is_copy_consistent(filename, overwrite)
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diffs += [f"- {filename}: copy does not match {d[0]} at line {d[1]}" for d in new_diffs]
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if not overwrite and len(diffs) > 0:
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diff = "\n".join(diffs)
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raise Exception(
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"Found the following copy inconsistencies:\n"
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+ diff
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+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."
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)
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check_model_list_copy(overwrite=overwrite)
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def check_full_copies(overwrite: bool = False):
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diffs = []
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for target, source in FULL_COPIES.items():
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with open(source, "r", encoding="utf-8") as f:
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source_code = f.read()
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with open(target, "r", encoding="utf-8") as f:
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target_code = f.read()
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if source_code != target_code:
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if overwrite:
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with open(target, "w", encoding="utf-8") as f:
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print(f"Replacing the content of {target} by the one of {source}.")
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f.write(source_code)
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else:
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diffs.append(f"- {target}: copy does not match {source}.")
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if not overwrite and len(diffs) > 0:
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diff = "\n".join(diffs)
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raise Exception(
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"Found the following copy inconsistencies:\n"
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+ diff
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+ "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."
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)
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def get_model_list(filename, start_prompt, end_prompt):
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"""Extracts the model list from the README."""
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with open(os.path.join(REPO_PATH, filename), "r", encoding="utf-8", newline="\n") as f:
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lines = f.readlines()
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# Find the start of the list.
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start_index = 0
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while not lines[start_index].startswith(start_prompt):
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start_index += 1
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start_index += 1
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result = []
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current_line = ""
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end_index = start_index
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while not lines[end_index].startswith(end_prompt):
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if lines[end_index].startswith("1."):
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if len(current_line) > 1:
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result.append(current_line)
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current_line = lines[end_index]
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elif len(lines[end_index]) > 1:
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current_line = f"{current_line[:-1]} {lines[end_index].lstrip()}"
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end_index += 1
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if len(current_line) > 1:
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result.append(current_line)
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return "".join(result)
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def convert_to_localized_md(model_list, localized_model_list, format_str):
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"""Convert `model_list` to each localized README."""
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def _rep(match):
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title, model_link, paper_affiliations, paper_title_link, paper_authors, supplements = match.groups()
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return format_str.format(
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title=title,
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model_link=model_link,
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paper_affiliations=paper_affiliations,
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paper_title_link=paper_title_link,
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paper_authors=paper_authors,
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supplements=" " + supplements.strip() if len(supplements) != 0 else "",
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)
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# This regex captures metadata from an English model description, including model title, model link,
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# affiliations of the paper, title of the paper, authors of the paper, and supplemental data (see DistilBERT for example).
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_re_capture_meta = re.compile(
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r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\* \(from ([^)]*)\)[^\[]*([^\)]*\)).*?by (.*?[A-Za-z\*]{2,}?)\. (.*)$"
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)
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# This regex is used to synchronize link.
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_re_capture_title_link = re.compile(r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\*")
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if len(localized_model_list) == 0:
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localized_model_index = {}
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else:
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try:
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localized_model_index = {
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re.search(r"\*\*\[([^\]]*)", line).groups()[0]: line
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for line in localized_model_list.strip().split("\n")
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}
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except AttributeError:
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raise AttributeError("A model name in localized READMEs cannot be recognized.")
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model_keys = [re.search(r"\*\*\[([^\]]*)", line).groups()[0] for line in model_list.strip().split("\n")]
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# We exclude keys in localized README not in the main one.
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readmes_match = not any([k not in model_keys for k in localized_model_index])
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localized_model_index = {k: v for k, v in localized_model_index.items() if k in model_keys}
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for model in model_list.strip().split("\n"):
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title, model_link = _re_capture_title_link.search(model).groups()
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if title not in localized_model_index:
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readmes_match = False
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# Add an anchor white space behind a model description string for regex.
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# If metadata cannot be captured, the English version will be directly copied.
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localized_model_index[title] = _re_capture_meta.sub(_rep, model + " ")
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elif _re_fill_pattern.search(localized_model_index[title]) is not None:
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update = _re_capture_meta.sub(_rep, model + " ")
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if update != localized_model_index[title]:
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readmes_match = False
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localized_model_index[title] = update
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else:
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# Synchronize link
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localized_model_index[title] = _re_capture_title_link.sub(
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f"**[{title}]({model_link})**", localized_model_index[title], count=1
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)
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sorted_index = sorted(localized_model_index.items(), key=lambda x: x[0].lower())
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return readmes_match, "\n".join(map(lambda x: x[1], sorted_index)) + "\n"
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def convert_readme_to_index(model_list):
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model_list = model_list.replace("https://huggingface.co/docs/transformers/main/", "")
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return model_list.replace("https://huggingface.co/docs/transformers/", "")
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def _find_text_in_file(filename, start_prompt, end_prompt):
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"""
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Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty
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lines.
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"""
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with open(filename, "r", encoding="utf-8", newline="\n") as f:
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lines = f.readlines()
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# Find the start prompt.
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start_index = 0
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while not lines[start_index].startswith(start_prompt):
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start_index += 1
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start_index += 1
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end_index = start_index
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while not lines[end_index].startswith(end_prompt):
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end_index += 1
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end_index -= 1
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while len(lines[start_index]) <= 1:
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start_index += 1
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while len(lines[end_index]) <= 1:
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end_index -= 1
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end_index += 1
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return "".join(lines[start_index:end_index]), start_index, end_index, lines
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def check_model_list_copy(overwrite=False, max_per_line=119):
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"""Check the model lists in the README and index.rst are consistent and maybe `overwrite`."""
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# Fix potential doc links in the README
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with open(os.path.join(REPO_PATH, "README.md"), "r", encoding="utf-8", newline="\n") as f:
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readme = f.read()
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new_readme = readme.replace("https://huggingface.co/transformers", "https://huggingface.co/docs/transformers")
|
|
new_readme = new_readme.replace(
|
|
"https://huggingface.co/docs/main/transformers", "https://huggingface.co/docs/transformers/main"
|
|
)
|
|
if new_readme != readme:
|
|
if overwrite:
|
|
with open(os.path.join(REPO_PATH, "README.md"), "w", encoding="utf-8", newline="\n") as f:
|
|
f.write(new_readme)
|
|
else:
|
|
raise ValueError(
|
|
"The main README contains wrong links to the documentation of Transformers. Run `make fix-copies` to "
|
|
"automatically fix them."
|
|
)
|
|
|
|
# If the introduction or the conclusion of the list change, the prompts may need to be updated.
|
|
index_list, start_index, end_index, lines = _find_text_in_file(
|
|
filename=os.path.join(PATH_TO_DOCS, "index.mdx"),
|
|
start_prompt="<!--This list is updated automatically from the README",
|
|
end_prompt="### Supported frameworks",
|
|
)
|
|
md_list = get_model_list(
|
|
filename="README.md",
|
|
start_prompt=LOCALIZED_READMES["README.md"]["start_prompt"],
|
|
end_prompt=LOCALIZED_READMES["README.md"]["end_prompt"],
|
|
)
|
|
|
|
converted_md_lists = []
|
|
for filename, value in LOCALIZED_READMES.items():
|
|
_start_prompt = value["start_prompt"]
|
|
_end_prompt = value["end_prompt"]
|
|
_format_model_list = value["format_model_list"]
|
|
|
|
localized_md_list = get_model_list(filename, _start_prompt, _end_prompt)
|
|
readmes_match, converted_md_list = convert_to_localized_md(md_list, localized_md_list, _format_model_list)
|
|
|
|
converted_md_lists.append((filename, readmes_match, converted_md_list, _start_prompt, _end_prompt))
|
|
|
|
converted_md_list = convert_readme_to_index(md_list)
|
|
if converted_md_list != index_list:
|
|
if overwrite:
|
|
with open(os.path.join(PATH_TO_DOCS, "index.mdx"), "w", encoding="utf-8", newline="\n") as f:
|
|
f.writelines(lines[:start_index] + [converted_md_list] + lines[end_index:])
|
|
else:
|
|
raise ValueError(
|
|
"The model list in the README changed and the list in `index.mdx` has not been updated. Run "
|
|
"`make fix-copies` to fix this."
|
|
)
|
|
|
|
for converted_md_list in converted_md_lists:
|
|
filename, readmes_match, converted_md, _start_prompt, _end_prompt = converted_md_list
|
|
|
|
if filename == "README.md":
|
|
continue
|
|
if overwrite:
|
|
_, start_index, end_index, lines = _find_text_in_file(
|
|
filename=os.path.join(REPO_PATH, filename), start_prompt=_start_prompt, end_prompt=_end_prompt
|
|
)
|
|
with open(os.path.join(REPO_PATH, filename), "w", encoding="utf-8", newline="\n") as f:
|
|
f.writelines(lines[:start_index] + [converted_md] + lines[end_index:])
|
|
elif not readmes_match:
|
|
raise ValueError(
|
|
f"The model list in the README changed and the list in `{filename}` has not been updated. Run "
|
|
"`make fix-copies` to fix this."
|
|
)
|
|
|
|
|
|
SPECIAL_MODEL_NAMES = {
|
|
"Bert Generation": "BERT For Sequence Generation",
|
|
"BigBird": "BigBird-RoBERTa",
|
|
"Data2VecAudio": "Data2Vec",
|
|
"Data2VecText": "Data2Vec",
|
|
"Data2VecVision": "Data2Vec",
|
|
"DonutSwin": "Swin Transformer",
|
|
"Marian": "MarianMT",
|
|
"MaskFormerSwin": "Swin Transformer",
|
|
"OpenAI GPT-2": "GPT-2",
|
|
"OpenAI GPT": "GPT",
|
|
"Perceiver": "Perceiver IO",
|
|
"ViT": "Vision Transformer (ViT)",
|
|
}
|
|
|
|
# Update this list with the models that shouldn't be in the README. This only concerns modular models or those who do
|
|
# not have an associated paper.
|
|
MODELS_NOT_IN_README = [
|
|
"BertJapanese",
|
|
"Encoder decoder",
|
|
"FairSeq Machine-Translation",
|
|
"HerBERT",
|
|
"RetriBERT",
|
|
"Speech Encoder decoder",
|
|
"Speech2Text",
|
|
"Speech2Text2",
|
|
"Vision Encoder decoder",
|
|
"VisionTextDualEncoder",
|
|
]
|
|
|
|
|
|
README_TEMPLATE = (
|
|
"1. **[{model_name}](https://huggingface.co/docs/main/transformers/model_doc/{model_type})** (from "
|
|
"<FILL INSTITUTION>) released with the paper [<FILL PAPER TITLE>](<FILL ARKIV LINK>) by <FILL AUTHORS>."
|
|
)
|
|
|
|
|
|
def check_readme(overwrite=False):
|
|
info = LOCALIZED_READMES["README.md"]
|
|
models, start_index, end_index, lines = _find_text_in_file(
|
|
os.path.join(REPO_PATH, "README.md"),
|
|
info["start_prompt"],
|
|
info["end_prompt"],
|
|
)
|
|
models_in_readme = [re.search(r"\*\*\[([^\]]*)", line).groups()[0] for line in models.strip().split("\n")]
|
|
|
|
model_names_mapping = transformers_module.models.auto.configuration_auto.MODEL_NAMES_MAPPING
|
|
absents = [
|
|
(key, name)
|
|
for key, name in model_names_mapping.items()
|
|
if SPECIAL_MODEL_NAMES.get(name, name) not in models_in_readme
|
|
]
|
|
# Remove exceptions
|
|
absents = [(key, name) for key, name in absents if name not in MODELS_NOT_IN_README]
|
|
if len(absents) > 0 and not overwrite:
|
|
print(absents)
|
|
raise ValueError(
|
|
"The main README doesn't contain all models, run `make fix-copies` to fill it with the missing model(s)"
|
|
" then complete the generated entries.\nIf the model is not supposed to be in the main README, add it to"
|
|
" the list `MODELS_NOT_IN_README` in utils/check_copies.py.\nIf it has a different name in the repo than"
|
|
" in the README, map the correspondence in `SPECIAL_MODEL_NAMES` in utils/check_copies.py."
|
|
)
|
|
|
|
new_models = [README_TEMPLATE.format(model_name=name, model_type=key) for key, name in absents]
|
|
|
|
all_models = models.strip().split("\n") + new_models
|
|
all_models = sorted(all_models, key=lambda x: re.search(r"\*\*\[([^\]]*)", x).groups()[0].lower())
|
|
all_models = "\n".join(all_models) + "\n"
|
|
|
|
if all_models != models:
|
|
if overwrite:
|
|
print("Fixing the main README.")
|
|
with open(os.path.join(REPO_PATH, "README.md"), "w", encoding="utf-8", newline="\n") as f:
|
|
f.writelines(lines[:start_index] + [all_models] + lines[end_index:])
|
|
else:
|
|
raise ValueError("The main README model list is not properly sorted. Run `make fix-copies` to fix this.")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
|
|
args = parser.parse_args()
|
|
|
|
check_readme(args.fix_and_overwrite)
|
|
check_copies(args.fix_and_overwrite)
|
|
check_full_copies(args.fix_and_overwrite)
|