




import argparse
import json
import os
import time
import zipfile

from get_ci_error_statistics import download_artifact, get_artifacts_links

from transformers import logging


__a          =			logging.get_logger(__name__)







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Union[str, Any]       =      set()
     UpperCAmelCase_      :							Union[str, Any]       =      []

     def parse_line(_lowercase				):
          for line in fp:
               if isinstance(_lowercase			,						_lowercase				):
                    UpperCAmelCase_      :							str       =      line.decode('''UTF-8'''				)
               if "warnings summary (final)" in line:
                    continue
               # This means we are outside the body of a warning
               elif not line.startswith(''' '''				):
                    # process a single warning and move it to `selected_warnings`.
                    if len(_lowercase				) > 0:
                         UpperCAmelCase_      :							Optional[int]       =      "\n".join(_lowercase				)
                         # Only keep the warnings specified in `targets`
                         if any(f''': {x}: ''' in warning for x in targets				):
                              selected_warnings.add(_lowercase				)
                         buffer.clear()
                    continue
               else:
                    UpperCAmelCase_      :							List[str]       =      line.strip()
                    buffer.append(_lowercase				)

     if from_gh:
          for filename in os.listdir(_lowercase				):
               UpperCAmelCase_      :							Dict       =      os.path.join(_lowercase			,						_lowercase				)
               if not os.path.isdir(_lowercase				):
                    # read the file
                    if filename != "warnings.txt":
                         continue
                    with open(_lowercase				) as fp:
                         parse_line(_lowercase				)
     else:
          try:
               with zipfile.ZipFile(_lowercase				) as z:
                    for filename in z.namelist():
                         if not os.path.isdir(_lowercase				):
                              # read the file
                              if filename != "warnings.txt":
                                   continue
                              with z.open(_lowercase				) as fp:
                                   parse_line(_lowercase				)
          except Exception:
               logger.warning(
                   f'''{artifact_path} is either an invalid zip file or something else wrong. This file is skipped.'''				)

     return selected_warnings







def lowerCamelCase__       (      _lowercase			,						_lowercase				):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      set()

     UpperCAmelCase_      :							Union[str, Any]       =      [os.path.join(_lowercase			,						_lowercase				) for p in os.listdir(_lowercase				) if (p.endswith('''.zip'''				) or from_gh)]
     for p in paths:
          selected_warnings.update(extract_warnings_from_single_artifact(_lowercase			,						_lowercase				)				)

     return selected_warnings


if __name__ == "__main__":







     def lowerCamelCase__       (      _lowercase				):

          '''simple docstring'''


          return values.split(''','''				)

     __a          =			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.')
     # optional parameters
     parser.add_argument(
         '--targets',
         default='DeprecationWarning,UserWarning,FutureWarning',
         type=list_str,
         help='Comma-separated list of target warning(s) which we want to extract.',
     )
     parser.add_argument(
         '--from_gh',
         action='store_true',
         help='If running from a GitHub action workflow and collecting warnings from its artifacts.',
     )

     __a          =			parser.parse_args()

     __a          =			args.from_gh
     if from_gh:
          # The artifacts have to be downloaded using `actions/download-artifact@v3`
          pass
     else:
          os.makedirs(args.output_dir, exist_ok=True)

          # get download links
          __a          =			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)

          # download artifacts
          for idx, (name, url) in enumerate(artifacts.items()):
               print(name)
               print(url)
               print('=' * 80)
               download_artifact(name, url, args.output_dir, args.token)
               # Be gentle to GitHub
               time.sleep(1)

    # extract warnings from artifacts
     __a          =			extract_warnings(args.output_dir, args.targets)
     __a          =			sorted(selected_warnings)
     with open(os.path.join(args.output_dir, 'selected_warnings.json'), 'w', encoding='UTF-8') as fp:
          json.dump(selected_warnings, fp, ensure_ascii=False, indent=4)





from ...processing_utils import ProcessorMixin

class   __a(					_a       ):






    """simple docstring"""





    lowerCAmelCase        =				'''SpeechT5FeatureExtractor'''
    lowerCAmelCase        =				'''SpeechT5Tokenizer'''





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		int:
         super().__init__(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    def __call__(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		int:
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''audio'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      kwargs.pop('''text'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''text_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							int       =      kwargs.pop('''audio_target'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      kwargs.pop('''sampling_rate'''       ,_SCREAMING_SNAKE_CASE  )

         if audio is not None and text is not None:
              raise ValueError(
                  '''Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?'''  )
         if audio_target is not None and text_target is not None:
              raise ValueError(
                  '''Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?'''  )
         if audio is None and audio_target is None and text is None and text_target is None:
              raise ValueError(
                  '''You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.'''  )

         if audio is not None:
              UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif text is not None:
              UpperCAmelCase_      :							Dict       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if audio_target is not None:
              UpperCAmelCase_      :							List[Any]       =      self.feature_extractor(audio_target=_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,sampling_rate=_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      targets['''input_values''']
         elif text_target is not None:
              UpperCAmelCase_      :							Optional[int]       =      self.tokenizer(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							Optional[Any]       =      targets['''input_ids''']
         else:
              UpperCAmelCase_      :							Tuple       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Dict       =      labels

              UpperCAmelCase_      :							Optional[int]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Dict       =      kwargs.pop('''input_values'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Union[str, Any]       =      kwargs.pop('''input_ids'''       ,_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      kwargs.pop('''labels'''       ,_SCREAMING_SNAKE_CASE  )

         if input_values is not None and input_ids is not None:
              raise ValueError('''Cannot process both `input_values` and `input_ids` inputs.'''  )
         if input_values is None and input_ids is None and labels is None:
              raise ValueError(
                  '''You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.'''  )

         if input_values is not None:
              UpperCAmelCase_      :							Tuple       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         elif input_ids is not None:
              UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
         else:
              UpperCAmelCase_      :							List[str]       =      None

         if labels is not None:
              if "input_ids" in labels or (isinstance(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ) and "input_ids" in labels[0]):
                   UpperCAmelCase_      :							Tuple       =      self.tokenizer.pad(_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							Any       =      targets['''input_ids''']
              else:
                   UpperCAmelCase_      :							int       =      self.feature_extractor.feature_size
                   UpperCAmelCase_      :							List[str]       =      self.feature_extractor.num_mel_bins
                   UpperCAmelCase_      :							Optional[Any]       =      self.feature_extractor.pad(_SCREAMING_SNAKE_CASE       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							List[Any]       =      feature_size_hack
                   UpperCAmelCase_      :							List[Any]       =      targets['''input_values''']
         else:
              UpperCAmelCase_      :							Optional[int]       =      None

         if inputs is None:
              return targets

         if targets is not None:
              UpperCAmelCase_      :							Optional[int]       =      labels

              UpperCAmelCase_      :							Union[str, Any]       =      targets.get('''attention_mask'''  )
              if decoder_attention_mask is not None:
                   UpperCAmelCase_      :							List[Any]       =      decoder_attention_mask

         return inputs





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self       ,*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE       ,**_SCREAMING_SNAKE_CASE  )