




from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse("3.8"):
		import importlib_metadata
else:
		import importlib.metadata as importlib_metadata


lowercase__				:     Tuple 		=						""


if version.parse(importlib_metadata.version("jiwer")) < version.parse("2.3.0"):
		class 		UpperCAmelCase   (				tr.AbstractTransform			):







								'''simple docstring'''





								def __init__(						self							:     int       ,	__lowercase							:     str = " "						):






									"""simple docstring"""






									snake_case_		    =     sentence_delimiter






								def        snake_case__						(						self							:     List[str]       ,	__lowercase							:     str						):






									"""simple docstring"""






									return list(__lowercase						)






								def        snake_case__						(						self							:     Union[str, Any]       ,	__lowercase							:     List[str]						):






									"""simple docstring"""






									snake_case_		    =     []
									for sent_idx, sentence in enumerate(__lowercase						):
										chars.extend(self.process_string(__lowercase						)						)
										if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__lowercase						) - 1:
											chars.append(self.sentence_delimiter						)
									return chars







		lowercase__				:     int 		=						tr.Compose(
		    [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
		)
else:
		lowercase__				:     int 		=						tr.Compose(
		    [
		        tr.RemoveMultipleSpaces(),
		        tr.Strip(),
		        tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
		        tr.ReduceToListOfListOfChars(),
		    ]
		)


lowercase__				:     List[str] 		=						"\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowercase__				:     Optional[int] 		=						"\\nCharacter error rate (CER) is a common metric of the performance of an automatic speech recognition system.\n\nCER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.\n\nCharacter error rate can be computed as:\n\nCER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct characters,\nN is the number of characters in the reference (N=S+D+C).\n\nCER's output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the\nperformance of the ASR system with a CER of 0 being a perfect score.\n"

lowercase__				:     List[str] 		=						"\nComputes CER score of transcribed segments against references.\nArgs:\n    references: list of references for each speech input.\n    predictions: list of transcribtions to score.\n    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.\nReturns:\n    (float): the character error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> cer = datasets.load_metric(\"cer\")\n    >>> cer_score = cer.compute(predictions=predictions, references=references)\n    >>> print(cer_score)\n    0.34146341463414637\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,   _KWARGS_DESCRIPTION			)
class 		UpperCAmelCase   (				datasets.Metric			):







						'''simple docstring'''





						def        snake_case__						(						self							:     Optional[Any]						):






							"""simple docstring"""






							return datasets.MetricInfo(
							    description=_DESCRIPTION       ,	citation=_CITATION       ,	inputs_description=_KWARGS_DESCRIPTION       ,	features=datasets.Features(
							        {
							            "predictions": datasets.Value("string"       ,	id="sequence"						),
							            "references": datasets.Value("string"       ,	id="sequence"						),
							        }						)       ,	codebase_urls=["https://github.com/jitsi/jiwer/"]       ,	reference_urls=[
							        "https://en.wikipedia.org/wiki/Word_error_rate",
							        "https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates",
							    ]       ,	)






						def        snake_case__						(						self							:     List[Any]       ,	__lowercase							:     List[str]       ,	__lowercase							:     Optional[int]       ,	__lowercase							:     Dict=False						):






							"""simple docstring"""






							if concatenate_texts:
								return jiwer.compute_measures(
								    __lowercase       ,	__lowercase       ,	truth_transform=__lowercase       ,	hypothesis_transform=__lowercase       ,	)["wer"]

							snake_case_		    =     0
							snake_case_		    =     0
							for prediction, reference in zip(__lowercase       ,	__lowercase						):
								snake_case_		    =     jiwer.compute_measures(
								    __lowercase       ,	__lowercase       ,	truth_transform=__lowercase       ,	hypothesis_transform=__lowercase       ,	)
								incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
								total += measures["substitutions"] + measures["deletions"] + measures["hits"]

							return incorrect / total






import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     None
	if token is not None:
		snake_case_		    =     {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

	snake_case_		    =     f"https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"
	snake_case_		    =     requests.get(_A     ,	headers=_A							).json()
	snake_case_		    =     {}

	try:
		job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}							)
		snake_case_		    =     math.ceil((result["total_count"] - 100) / 100							)

		for i in range(_A							):
			snake_case_		    =     requests.get(url + f"&page={i + 2}"     ,	headers=_A							).json()
			job_links.update({job["name"]: job["html_url"] for job in result["jobs"]}							)

		return job_links
	except Exception:
		print(f"Unknown error, could not fetch links:\n{traceback.format_exc()}"							)

	return {}


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     None
	if token is not None:
		snake_case_		    =     {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

	snake_case_		    =     f"https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"
	snake_case_		    =     requests.get(_A     ,	headers=_A							).json()
	snake_case_		    =     {}

	try:
		artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}							)
		snake_case_		    =     math.ceil((result["total_count"] - 100) / 100							)

		for i in range(_A							):
			snake_case_		    =     requests.get(url + f"&page={i + 2}"     ,	headers=_A							).json()
			artifacts.update({artifact["name"]: artifact["archive_download_url"] for artifact in result["artifacts"]}							)

		return artifacts
	except Exception:
		print(f"Unknown error, could not fetch links:\n{traceback.format_exc()}"							)

	return {}


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     None
	if token is not None:
		snake_case_		    =     {"Accept": "application/vnd.github+json", "Authorization": f"Bearer {token}"}

	snake_case_		    =     requests.get(_A     ,	headers=_A     ,	allow_redirects=_A							)
	snake_case_		    =     result.headers["Location"]
	snake_case_		    =     requests.get(_A     ,	allow_redirects=_A							)
	snake_case_		    =     os.path.join(_A     ,	f"{artifact_name}.zip"							)
	with open(_A     ,	"wb"							) as fp:
		fp.write(response.content							)


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     []
	snake_case_		    =     []
	snake_case_		    =     None

	with zipfile.ZipFile(_A							) as z:
		for filename in z.namelist():
			if not os.path.isdir(_A							):
				# read the file
				if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
					with z.open(_A							) as f:
						for line in f:
							snake_case_		    =     line.decode("UTF-8"							).strip()
							if filename == "failures_line.txt":
								try:
									# `error_line` is the place where `error` occurs
									snake_case_		    =     line[: line.index(": "							)]
									snake_case_		    =     line[line.index(": "							) + len(": "							) :]
									errors.append([error_line, error]							)
								except Exception:
									# skip un-related lines
									pass
							elif filename == "summary_short.txt" and line.startswith("FAILED "							):
								# `test` is the test method that failed
								snake_case_		    =     line[len("FAILED "							) :]
								failed_tests.append(_A							)
							elif filename == "job_name.txt":
								snake_case_		    =     line

	if len(_A							) != len(_A							):
		raise ValueError(
		    f"`errors` and `failed_tests` should have the same number of elements. Got {len(_A							)} for `errors` "
		    f"and {len(_A							)} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"
		    " problem."							)

	snake_case_		    =     None
	if job_name and job_links:
		snake_case_		    =     job_links.get(_A     ,	_A							)

	# A list with elements of the form (line of error, error, failed test)
	snake_case_		    =     [x + [y] + [job_link] for x, y in zip(_A     ,	_A							)]

	return result


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     []

	snake_case_		    =     [os.path.join(_A     ,	_A							) for p in os.listdir(_A							) if p.endswith(".zip"							)]
	for p in paths:
		errors.extend(get_errors_from_single_artifact(_A     ,	job_links=_A							)							)

	return errors


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     Counter()
	counter.update([x[1] for x in logs]							)
	snake_case_		    =     counter.most_common()
	snake_case_		    =     {}
	for error, count in counts:
		if error_filter is None or error not in error_filter:
			snake_case_		    =     {"count": count, "failed_tests": [(x[2], x[0]) for x in logs if x[1] == error]}

	snake_case_		    =     dict(sorted(r.items()     ,	key=lambda _A							: item[1]["count"]     ,	reverse=_A							)							)
	return r


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     test.split("::"							)[0]
	if test.startswith("tests/models/"							):
		snake_case_		    =     test.split("/"							)[2]
	else:
		snake_case_		    =     None

	return test


def 						lowerCamelCase__    (		_A     ,	_A=None							):







	'''simple docstring'''





	snake_case_		    =     [(x[0], x[1], get_model(x[2]							)) for x in logs]
	snake_case_		    =     [x for x in logs if x[2] is not None]
	snake_case_		    =     {x[2] for x in logs}

	snake_case_		    =     {}
	for test in tests:
		snake_case_		    =     Counter()
		# count by errors in `test`
		counter.update([x[1] for x in logs if x[2] == test]							)
		snake_case_		    =     counter.most_common()
		snake_case_		    =     {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
		snake_case_		    =     sum(error_counts.values()							)
		if n_errors > 0:
			snake_case_		    =     {"count": n_errors, "errors": error_counts}

	snake_case_		    =     dict(sorted(r.items()     ,	key=lambda _A							: item[1]["count"]     ,	reverse=_A							)							)
	return r


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     "| no. | error | status |"
	snake_case_		    =     "|-:|:-|:-|"
	snake_case_		    =     [header, sep]
	for error in reduced_by_error:
		snake_case_		    =     reduced_by_error[error]["count"]
		snake_case_		    =     f"| {count} | {error[:100]} |  |"
		lines.append(_A							)

	return "\n".join(_A							)


def 						lowerCamelCase__    (		_A							):







	'''simple docstring'''





	snake_case_		    =     "| model | no. of errors | major error | count |"
	snake_case_		    =     "|-:|-:|-:|-:|"
	snake_case_		    =     [header, sep]
	for model in reduced_by_model:
		snake_case_		    =     reduced_by_model[model]["count"]
		snake_case_    ,      snake_case_		    =     list(reduced_by_model[model]["errors"].items()							)[0]
		snake_case_		    =     f"| {model} | {count} | {error[:60]} | {_count} |"
		lines.append(_A							)

	return "\n".join(_A							)


if __name__ == "__main__":
		lowercase__				:     int 		=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
		parser.add_argument(
		    "--output_dir",
		    type=str,
		    required=True,
		    help="Where to store the downloaded artifacts and other result files.",
		)
		parser.add_argument("--token", default=None, type=str, help="A token that has actions:read permission.")
		lowercase__				:     int 		=						parser.parse_args()

		os.makedirs(args.output_dir, exist_ok=True)

		lowercase__				:     Optional[Any] 		=						get_job_links(args.workflow_run_id, token=args.token)
		lowercase__				:     List[Any] 		=						{}
		# To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
		# For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
		if _job_links:
				for k, v in _job_links.items():
						# This is how GitHub actions combine job names.
						if " / " in k:
								lowercase__				:     Tuple 		=						k.find(" / ")
								lowercase__				:     Union[str, Any] 		=						k[index + len(" / ") :]
						lowercase__				:     Union[str, Any] 		=						v
		with open(os.path.join(args.output_dir, "job_links.json"), "w", encoding="UTF-8") as fp:
				json.dump(job_links, fp, ensure_ascii=False, indent=4)

		lowercase__				:     Any 		=						get_artifacts_links(args.workflow_run_id, token=args.token)
		with open(os.path.join(args.output_dir, "artifacts.json"), "w", encoding="UTF-8") as fp:
				json.dump(artifacts, fp, ensure_ascii=False, indent=4)

		for idx, (name, url) in enumerate(artifacts.items()):
				download_artifact(name, url, args.output_dir, args.token)
				# Be gentle to GitHub
				time.sleep(1)

		lowercase__				:     List[Any] 		=						get_all_errors(args.output_dir, job_links=job_links)

		# `e[1]` is the error
		lowercase__				:     str 		=						Counter()
		counter.update([e[1] for e in errors])

		# print the top 30 most common test errors
		lowercase__				:     Union[str, Any] 		=						counter.most_common(30)
		for item in most_common:
				print(item)

		with open(os.path.join(args.output_dir, "errors.json"), "w", encoding="UTF-8") as fp:
				json.dump(errors, fp, ensure_ascii=False, indent=4)

		lowercase__				:     int 		=						reduce_by_error(errors)
		lowercase__				:     List[str] 		=						reduce_by_model(errors)

		lowercase__				:     Tuple 		=						make_github_table(reduced_by_error)
		lowercase__				:     Optional[int] 		=						make_github_table_per_model(reduced_by_model)

		with open(os.path.join(args.output_dir, "reduced_by_error.txt"), "w", encoding="UTF-8") as fp:
				fp.write(sa)
		with open(os.path.join(args.output_dir, "reduced_by_model.txt"), "w", encoding="UTF-8") as fp:
				fp.write(sa)
