


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

import requests







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]		,							lowercase						:						int=None				)			->		int:


    """simple docstring"""





    snake_case					:   Tuple         =					None
    if token is not None:
        snake_case					:   Optional[Any]         =					{"""Accept""": """application/vnd.github+json""", """Authorization""": F'Bearer {token}'}

    snake_case					:   Any         =					F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
    snake_case					:   int         =					requests.get(SCREAMING_SNAKE_CASE__		,							headers=SCREAMING_SNAKE_CASE__				).json()
    snake_case					:   List[str]         =					{}

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

        for i in range(SCREAMING_SNAKE_CASE__				):
            snake_case					:   Dict         =					requests.get(url + F'&page={i + 2}'		,							headers=SCREAMING_SNAKE_CASE__				).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		(		lowercase						:						List[str]		,							lowercase						:						int=None				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					None
    if token is not None:
        snake_case					:   List[str]         =					{"""Accept""": """application/vnd.github+json""", """Authorization""": F'Bearer {token}'}

    snake_case					:   int         =					F'https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100'
    snake_case					:   Optional[int]         =					requests.get(SCREAMING_SNAKE_CASE__		,							headers=SCREAMING_SNAKE_CASE__				).json()
    snake_case					:   int         =					{}

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

        for i in range(SCREAMING_SNAKE_CASE__				):
            snake_case					:   int         =					requests.get(url + F'&page={i + 2}'		,							headers=SCREAMING_SNAKE_CASE__				).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		(		lowercase						:						Optional[Any]		,							lowercase						:						Optional[Any]		,							lowercase						:						Optional[int]		,							lowercase						:						Optional[Any]				)			->		Dict:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					None
    if token is not None:
        snake_case					:   Any         =					{"""Accept""": """application/vnd.github+json""", """Authorization""": F'Bearer {token}'}

    snake_case					:   List[str]         =					requests.get(SCREAMING_SNAKE_CASE__		,							headers=SCREAMING_SNAKE_CASE__		,							allow_redirects=SCREAMING_SNAKE_CASE__				)
    snake_case					:   Optional[Any]         =					result.headers["""Location"""]
    snake_case					:   Tuple         =					requests.get(SCREAMING_SNAKE_CASE__		,							allow_redirects=SCREAMING_SNAKE_CASE__				)
    snake_case					:   Any         =					os.path.join(SCREAMING_SNAKE_CASE__		,							F'{artifact_name}.zip'				)
    with open(SCREAMING_SNAKE_CASE__		,							"wb"				) as fp:
        fp.write(response.content				)







def 						__lowerCAmelCase		(		lowercase						:						Dict		,							lowercase						:						List[Any]=None				)			->		Dict:


    """simple docstring"""





    snake_case					:   str         =					[]
    snake_case					:   Optional[int]         =					[]
    snake_case					:   Union[str, Any]         =					None

    with zipfile.ZipFile(SCREAMING_SNAKE_CASE__				) as z:
        for filename in z.namelist():
            if not os.path.isdir(SCREAMING_SNAKE_CASE__				):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(SCREAMING_SNAKE_CASE__				) as f:
                        for line in f:
                            snake_case					:   Union[str, Any]         =					line.decode("UTF-8"				).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    snake_case					:   str         =					line[: line.index(": "				)]
                                    snake_case					:   List[str]         =					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					:   int         =					line[len("FAILED "				) :]
                                failed_tests.append(SCREAMING_SNAKE_CASE__				)
                            elif filename == "job_name.txt":
                                snake_case					:   Tuple         =					line

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

    snake_case					:   Optional[int]         =					None
    if job_name and job_links:
        snake_case					:   Any         =					job_links.get(SCREAMING_SNAKE_CASE__		,							SCREAMING_SNAKE_CASE__				)

    # A list with elements of the form (line of error, error, failed test)
    snake_case					:   Optional[Any]         =					[x + [y] + [job_link] for x, y in zip(SCREAMING_SNAKE_CASE__		,							SCREAMING_SNAKE_CASE__				)]

    return result







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]		,							lowercase						:						List[Any]=None				)			->		int:


    """simple docstring"""





    snake_case					:   List[Any]         =					[]

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

    return errors







def 						__lowerCAmelCase		(		lowercase						:						List[str]		,							lowercase						:						Union[str, Any]=None				)			->		Optional[int]:


    """simple docstring"""





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

    snake_case					:   str         =					dict(sorted(r.items()		,							key=lambda lowercase				: item[1]["count"]		,							reverse=SCREAMING_SNAKE_CASE__				)				)
    return r







def 						__lowerCAmelCase		(		lowercase						:						Any				)			->		Dict:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					test.split("::"				)[0]
    if test.startswith("tests/models/"				):
        snake_case					:   Dict         =					test.split("/"				)[2]
    else:
        snake_case					:   List[Any]         =					None

    return test







def 						__lowerCAmelCase		(		lowercase						:						Any		,							lowercase						:						Dict=None				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					[(x[0], x[1], get_model(x[2]				)) for x in logs]
    snake_case					:   Any         =					[x for x in logs if x[2] is not None]
    snake_case					:   List[str]         =					{x[2] for x in logs}

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

    snake_case					:   Union[str, Any]         =					dict(sorted(r.items()		,							key=lambda lowercase				: item[1]["count"]		,							reverse=SCREAMING_SNAKE_CASE__				)				)
    return r







def 						__lowerCAmelCase		(		lowercase						:						List[str]				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					"""| no. | error | status |"""
    snake_case					:   int         =					"""|-:|:-|:-|"""
    snake_case					:   str         =					[header, sep]
    for error in reduced_by_error:
        snake_case					:   List[str]         =					reduced_by_error[error]["""count"""]
        snake_case					:   List[str]         =					F'| {count} | {error[:100]} |  |'
        lines.append(SCREAMING_SNAKE_CASE__				)

    return "\n".join(SCREAMING_SNAKE_CASE__				)







def 						__lowerCAmelCase		(		lowercase						:						List[Any]				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Dict         =					"""| model | no. of errors | major error | count |"""
    snake_case					:   List[Any]         =					"""|-:|-:|-:|-:|"""
    snake_case					:   int         =					[header, sep]
    for model in reduced_by_model:
        snake_case					:   List[str]         =					reduced_by_model[model]["""count"""]
        snake_case					:   Union[str, Any]         =					list(reduced_by_model[model]["errors"].items()				)[0]
        snake_case					:   Tuple         =					F'| {model} | {count} | {error[:60]} | {_count} |'
        lines.append(SCREAMING_SNAKE_CASE__				)

    return "\n".join(SCREAMING_SNAKE_CASE__				)


if __name__ == "__main__":
  __snake_case         =					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.""")
  __snake_case         =					parser.parse_args()

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

  __snake_case         =					get_job_links(args.workflow_run_id, token=args.token)
  __snake_case         =					{}
  # 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:
        __snake_case         =					k.find(""" / """)
        __snake_case         =					k[index + len(""" / """) :]
      __snake_case         =					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)

  __snake_case         =					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)

  __snake_case         =					get_all_errors(args.output_dir, job_links=job_links)

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

  # print the top 30 most common test errors
  __snake_case         =					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)

  __snake_case         =					reduce_by_error(errors)
  __snake_case         =					reduce_by_model(errors)

  __snake_case         =					make_github_table(reduced_by_error)
  __snake_case         =					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)









"""simple docstring"""



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


__snake_case         =					""""""


if version.parse(importlib_metadata.version("""jiwer""")) < version.parse("""2.3.0"""):







  class _lowerCAmelCase       (   tr.AbstractTransform			):







     def __init__(			self		,			UpperCamelCase__ = " "		)			->	Any:

         '''simple docstring'''




         snake_case					:   Optional[Any]         =					sentence_delimiter







     def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	Optional[Any]:

         '''simple docstring'''




         return list(UpperCamelCase__		)







     def 				lowerCamelCase	(			self		,			UpperCamelCase__		)			->	List[Any]:

         '''simple docstring'''




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



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


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

__snake_case         =					"""\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER'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
performance of the ASR system with a CER of 0 being a perfect score.
"""

__snake_case         =					"""
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]
    >>> references = [\"this is the reference\", \"there is another one\"]
    >>> cer = datasets.load_metric(\"cer\")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
"""







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,							_KWARGS_DESCRIPTION			)
class _lowerCAmelCase       (   datasets.Metric			):







   def 				lowerCamelCase	(			self		)			->	Optional[int]:

       '''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 				lowerCamelCase	(			self		,			UpperCamelCase__		,			UpperCamelCase__		,			UpperCamelCase__=False		)			->	Optional[int]:

       '''simple docstring'''




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

       snake_case					:   Optional[int]         =					0
       snake_case					:   int         =					0
       for prediction, reference in zip(UpperCamelCase__		,			UpperCamelCase__		):
           snake_case					:   Dict         =					jiwer.compute_measures(
               UpperCamelCase__		,			UpperCamelCase__		,			truth_transform=UpperCamelCase__		,			hypothesis_transform=UpperCamelCase__		,			)
           incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
           total += measures["substitutions"] + measures["deletions"] + measures["hits"]

       return incorrect / total





