Enable automated model list copying for localized READMEs (#13465)

* Complete basic mechanism

* Save

* Complete everything

* Style & Quality

* Update READMEs

* Add testing

* Fix README.md format

* Apply suggestions

* Fix format

* Update utils/check_copies.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
This commit is contained in:
Li-Huai (Allan) Lin
2021-09-08 20:03:35 +08:00
committed by GitHub
parent cd66539662
commit 18447c206d
6 changed files with 173 additions and 39 deletions

View File

@@ -31,6 +31,26 @@ REPO_PATH = "."
FULL_COPIES = {"examples/tensorflow/question-answering/utils_qa.py": "examples/pytorch/question-answering/utils_qa.py"}
LOCALIZED_READMES = {
# If the introduction or the conclusion of the list change, the prompts may need to be updated.
"README.md": {
"start_prompt": "🤗 Transformers currently provides the following architectures",
"end_prompt": "1. Want to contribute a new model?",
"format_model_list": "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by {paper_authors}.{supplements}",
},
"README_zh-hans.md": {
"start_prompt": "🤗 Transformers 目前支持如下的架构",
"end_prompt": "1. 想要贡献新的模型?",
"format_model_list": "**[{title}]({model_link})** (来自 {paper_affiliations}) 伴随论文 {paper_title_link}{paper_authors} 发布。{supplements}",
},
"README_zh-hant.md": {
"start_prompt": "🤗 Transformers 目前支援以下的架構",
"end_prompt": "1. 想要貢獻新的模型?",
"format_model_list": "**[{title}]({model_link})** (from {paper_affiliations}) released with the paper {paper_title_link} by {paper_authors}.{supplements}",
},
}
def _should_continue(line, indent):
return line.startswith(indent) or len(line) <= 1 or re.search(r"^\s*\):\s*$", line) is not None
@@ -219,16 +239,13 @@ def check_full_copies(overwrite: bool = False):
)
def get_model_list():
def get_model_list(filename, start_prompt, end_prompt):
"""Extracts the model list from the README."""
# If the introduction or the conclusion of the list change, the prompts may need to be updated.
_start_prompt = "🤗 Transformers currently provides the following architectures"
_end_prompt = "1. Want to contribute a new model?"
with open(os.path.join(REPO_PATH, "README.md"), "r", encoding="utf-8", newline="\n") as f:
with open(os.path.join(REPO_PATH, filename), "r", encoding="utf-8", newline="\n") as f:
lines = f.readlines()
# Find the start of the list.
start_index = 0
while not lines[start_index].startswith(_start_prompt):
while not lines[start_index].startswith(start_prompt):
start_index += 1
start_index += 1
@@ -236,7 +253,7 @@ def get_model_list():
current_line = ""
end_index = start_index
while not lines[end_index].startswith(_end_prompt):
while not lines[end_index].startswith(end_prompt):
if lines[end_index].startswith("1."):
if len(current_line) > 1:
result.append(current_line)
@@ -298,6 +315,52 @@ def convert_to_rst(model_list, max_per_line=None):
return "\n".join(result)
def convert_to_localized_md(model_list, localized_model_list, format_str):
"""Convert `model_list` to each localized README."""
def _rep(match):
title, model_link, paper_affiliations, paper_title_link, paper_authors, supplements = match.groups()
return format_str.format(
title=title,
model_link=model_link,
paper_affiliations=paper_affiliations,
paper_title_link=paper_title_link,
paper_authors=paper_authors,
supplements=" " + supplements.strip() if len(supplements) != 0 else "",
)
# This regex captures metadata from an English model description, including model title, model link,
# affiliations of the paper, title of the paper, authors of the paper, and supplemental data (see DistilBERT for example).
_re_capture_meta = re.compile(
r"\*\*\[([^\]]*)\]\(([^\)]*)\)\*\* \(from ([^)]*)\)[^\[]*([^\)]*\)).*?by (.*?[A-Za-z\*]{2,}?)\. (.*)$"
)
num_models_equal = True
if len(localized_model_list) == 0:
localized_model_index = {}
else:
try:
localized_model_index = {
re.search(r"\*\*\[([^\]]*)", line).groups()[0]: line
for line in localized_model_list.strip().split("\n")
}
except AttributeError:
raise AttributeError("A model name in localized READMEs cannot be recognized.")
for model in model_list.strip().split("\n"):
title = re.search(r"\*\*\[([^\]]*)", model).groups()[0]
if title not in localized_model_index:
num_models_equal = False
# Add an anchor white space behind a model description string for regex.
# If metadata cannot be captured, the English version will be directly copied.
localized_model_index[title] = re.sub(_re_capture_meta, _rep, model + " ")
sorted_index = sorted(localized_model_index.items(), key=lambda x: x[0].lower())
return num_models_equal, "\n".join(map(lambda x: x[1], sorted_index)) + "\n"
def _find_text_in_file(filename, start_prompt, end_prompt):
"""
Find the text in `filename` between a line beginning with `start_prompt` and before `end_prompt`, removing empty
@@ -326,24 +389,58 @@ def _find_text_in_file(filename, start_prompt, end_prompt):
def check_model_list_copy(overwrite=False, max_per_line=119):
"""Check the model lists in the README and index.rst are consistent and maybe `overwrite`."""
# If the introduction or the conclusion of the list change, the prompts may need to be updated.
rst_list, start_index, end_index, lines = _find_text_in_file(
filename=os.path.join(PATH_TO_DOCS, "index.rst"),
start_prompt=" This list is updated automatically from the README",
end_prompt="Supported frameworks",
)
md_list = get_model_list()
converted_list = convert_to_rst(md_list, max_per_line=max_per_line)
md_list = get_model_list(
filename="README.md",
start_prompt=LOCALIZED_READMES["README.md"]["start_prompt"],
end_prompt=LOCALIZED_READMES["README.md"]["end_prompt"],
)
if converted_list != rst_list:
converted_rst_list = convert_to_rst(md_list, max_per_line=max_per_line)
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)
num_models_equal, converted_md_list = convert_to_localized_md(md_list, localized_md_list, _format_model_list)
converted_md_lists.append((filename, num_models_equal, converted_md_list, _start_prompt, _end_prompt))
if converted_rst_list != rst_list:
if overwrite:
with open(os.path.join(PATH_TO_DOCS, "index.rst"), "w", encoding="utf-8", newline="\n") as f:
f.writelines(lines[:start_index] + [converted_list] + lines[end_index:])
f.writelines(lines[:start_index] + [converted_rst_list] + lines[end_index:])
else:
raise ValueError(
"The model list in the README changed and the list in `index.rst` has not been updated. Run "
"`make fix-copies` to fix this."
)
for converted_md_list in converted_md_lists:
filename, num_models_equal, converted_md, _start_prompt, _end_prompt = converted_md_list
if not num_models_equal:
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:])
else:
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."
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()