

'''simple docstring'''
import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					List[str]=None    )				->      int:
     """simple docstring"""




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

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

     try:
          job_links.update({job['name']: job['html_url'] for job in result['jobs']}    )
          _SCREAMING_SNAKE_CASE							   =math.ceil((result['total_count'] - 1_00) / 1_00    )

          for i in range(_UpperCamelCase    ):
               _SCREAMING_SNAKE_CASE							   =requests.get(url + f"&page={i + 2}"							,			headers=_UpperCamelCase    ).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 (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					Dict=None    )				->      Any:
     """simple docstring"""




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

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

     try:
          artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}    )
          _SCREAMING_SNAKE_CASE							   =math.ceil((result['total_count'] - 1_00) / 1_00    )

          for i in range(_UpperCamelCase    ):
               _SCREAMING_SNAKE_CASE							   =requests.get(url + f"&page={i + 2}"							,			headers=_UpperCamelCase    ).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 (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					Optional[int]    )				->      List[str]:
     """simple docstring"""




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

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Optional[Any]=None    )				->      str:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]
     _SCREAMING_SNAKE_CASE							   =[]
     _SCREAMING_SNAKE_CASE							   =None

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

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

     _SCREAMING_SNAKE_CASE							   =None
     if job_name and job_links:
          _SCREAMING_SNAKE_CASE							   =job_links.get(_UpperCamelCase							,			_UpperCamelCase    )

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

     return result


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[str]							,			_UpperCamelCase						:					str=None    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[]

     _SCREAMING_SNAKE_CASE							   =[os.path.join(_UpperCamelCase							,			_UpperCamelCase    ) for p in os.listdir(_UpperCamelCase    ) if p.endswith('.zip'    )]
     for p in paths:
          errors.extend(get_errors_from_single_artifact(_UpperCamelCase							,			job_links=_UpperCamelCase    )    )

     return errors


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Any=None    )				->      Any:
     """simple docstring"""




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

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      Any:
     """simple docstring"""




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

     return test


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[int]							,			_UpperCamelCase						:					str=None    )				->      str:
     """simple docstring"""




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

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

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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Any    )				->      Union[str, Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   ='| no. | error | status |'
     _SCREAMING_SNAKE_CASE							   ='|-:|:-|:-|'
     _SCREAMING_SNAKE_CASE							   =[header, sep]
     for error in reduced_by_error:
          _SCREAMING_SNAKE_CASE							   =reduced_by_error[error]['count']
          _SCREAMING_SNAKE_CASE							   =f"| {count} | {error[:1_00]} |  |"
          lines.append(_UpperCamelCase    )

     return "\n".join(_UpperCamelCase    )


def 	_lowerCAmelCase (						_UpperCamelCase						:					str    )				->      Union[str, Any]:
     """simple docstring"""




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

     return "\n".join(_UpperCamelCase    )


if __name__ == "__main__":
     lowerCamelCase			:				Optional[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.")
     lowerCamelCase			:				List[str] 			=					parser.parse_args()

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

     lowerCamelCase			:				List[Any] 			=					get_job_links(args.workflow_run_id, token=args.token)
     lowerCamelCase			:				str 			=					{}
     # 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:
                    lowerCamelCase			:				int 			=					k.find(" / ")
                    lowerCamelCase			:				List[Any] 			=					k[index + len(" / ") :]
               lowerCamelCase			:				Optional[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)

     lowerCamelCase			:				Optional[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)

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

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

     # print the top 30 most common test errors
     lowerCamelCase			:				Union[str, Any] 			=					counter.most_common(3_0)
     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)

     lowerCamelCase			:				List[str] 			=					reduce_by_error(errors)
     lowerCamelCase			:				Union[str, Any] 			=					reduce_by_model(errors)

     lowerCamelCase			:				str 			=					make_github_table(reduced_by_error)
     lowerCamelCase			:				str 			=					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'''
import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin







class 							A__   (	A__  ):
  A__					=    'MCTCTFeatureExtractor'
  A__					=    'AutoTokenizer'





  def __init__(		self				:     Optional[Any]							, _a				:     Optional[int]							, _a				:     Union[str, Any]			)     ->   Union[str, Any]:





       '''simple docstring'''






       super().__init__(_a							, _a			)
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False





  def __call__(		self				:     Dict							, *_a				:     str							, **_a				:     Dict			)     ->   Dict:





       '''simple docstring'''






       if self._in_target_context_manager:
            return self.current_processor(*_a							, **_a			)

       if "raw_speech" in kwargs:
            warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'			)
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('raw_speech'			)
       else:
            _SCREAMING_SNAKE_CASE							   =kwargs.pop('audio'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('sampling_rate'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('text'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =args[1:]

       if audio is None and text is None:
            raise ValueError('You need to specify either an `audio` or `text` input to process.'			)

       if audio is not None:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor(_a							, *_a							, sampling_rate=_a							, **_a			)
       if text is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer(_a							, **_a			)

       if text is None:
            return inputs
       elif audio is None:
            return encodings
       else:
            _SCREAMING_SNAKE_CASE							   =encodings['input_ids']
            return inputs





  def 						A  (		self				:     Any							, *_a				:     List[str]							, **_a				:     Optional[int]			)     ->   Optional[int]:





       '''simple docstring'''






       return self.tokenizer.batch_decode(*_a							, **_a			)





  def 						A  (		self				:     Dict							, *_a				:     Tuple							, **_a				:     Dict			)     ->   List[str]:





       '''simple docstring'''






       if self._in_target_context_manager:
            return self.current_processor.pad(*_a							, **_a			)

       _SCREAMING_SNAKE_CASE							   =kwargs.pop('input_features'							, _a			)
       _SCREAMING_SNAKE_CASE							   =kwargs.pop('labels'							, _a			)
       if len(_a			) > 0:
            _SCREAMING_SNAKE_CASE							   =args[0]
            _SCREAMING_SNAKE_CASE							   =args[1:]

       if input_features is not None:
            _SCREAMING_SNAKE_CASE							   =self.feature_extractor.pad(_a							, *_a							, **_a			)
       if labels is not None:
            _SCREAMING_SNAKE_CASE							   =self.tokenizer.pad(_a							, **_a			)

       if labels is None:
            return input_features
       elif input_features is None:
            return labels
       else:
            _SCREAMING_SNAKE_CASE							   =labels['input_ids']
            return input_features





  def 						A  (		self				:     Tuple							, *_a				:     Dict							, **_a				:     List[Any]			)     ->   Optional[int]:





       '''simple docstring'''






       return self.tokenizer.decode(*_a							, **_a			)





  @contextmanager
  def 						A  (		self				:     Optional[Any]			)     ->   Dict:





       '''simple docstring'''






       warnings.warn(
           '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
           'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
           'your audio inputs, or in a separate call.'			)
       _SCREAMING_SNAKE_CASE							   =True
       _SCREAMING_SNAKE_CASE							   =self.tokenizer
       yield
       _SCREAMING_SNAKE_CASE							   =self.feature_extractor
       _SCREAMING_SNAKE_CASE							   =False
