


'''simple docstring'''






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class        a   (   __UpperCAmelCase				):
 _lowerCAmelCase           =		'''ClapFeatureExtractor'''
 _lowerCAmelCase           =		('''RobertaTokenizer''', '''RobertaTokenizerFast''')
 def __init__(       self					, __magic_name__					, __magic_name__  )    ->							List[Any]:
      super().__init__(__magic_name__					, __magic_name__  )
 def __call__(       self					, __magic_name__=None					, __magic_name__=None					, __magic_name__=None					, **__magic_name__  )    ->							List[Any]:
      _a						=							kwargs.pop('sampling_rate'					, __magic_name__  )

      if text is None and audios is None:
           raise ValueError('You have to specify either text or audios. Both cannot be none.'  )

      if text is not None:
           _a						=							self.tokenizer(__magic_name__					, return_tensors=__magic_name__					, **__magic_name__  )

      if audios is not None:
           _a						=							self.feature_extractor(
               __magic_name__					, sampling_rate=__magic_name__					, return_tensors=__magic_name__					, **__magic_name__  )

      if text is not None and audios is not None:
           _a						=							audio_features.input_features
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__magic_name__  )					, tensor_type=__magic_name__  )
 def 							__UpperCAmelCase						(       self					, *__magic_name__					, **__magic_name__  )    ->							List[str]:
      return self.tokenizer.batch_decode(*__magic_name__					, **__magic_name__  )
 def 							__UpperCAmelCase						(       self					, *__magic_name__					, **__magic_name__  )    ->							List[str]:
      return self.tokenizer.decode(*__magic_name__					, **__magic_name__  )




 @property
 def 							__UpperCAmelCase						(       self  )    ->							List[str]:
      _a						=							self.tokenizer.model_input_names
      _a						=							self.feature_extractor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names  )  )








import argparse
from collections import defaultdict

import yaml


lowercase      :       int									=  """docs/source/en/_toctree.yml"""



def A_	(		A__				)  ->		Tuple:
       a__						:	int =				defaultdict(A__				)
       a__						:	List[str] =				[]
       a__						:	Any =				[]
       for doc in doc_list:
              if "local" in doc:
                     counts[doc["local"]] += 1

              if doc["title"].lower() == "overview":
                     overview_doc.append({'local': doc['local'], 'title': doc['title']}				)
              else:
                     new_doc_list.append(A__				)

       a__						:	int =				new_doc_list
       a__						:	List[str] =				[key for key, value in counts.items() if value > 1]

       a__						:	Tuple =				[]
       for duplicate_key in duplicates:
              a__						:	int =				list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}				)
              if len(A__				) > 1:
                     raise ValueError(
                         F'{duplicate_key} is present several times in the documentation table of content at '
                         '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                         'others.'				)
              # Only add this once
              new_doc.append({'local': duplicate_key, 'title': titles[0]}				)

       # Add none duplicate-keys
       new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]				)
       a__						:	Any =				sorted(A__		,      key=lambda A__				: s["title"].lower()				)

       # "overview" gets special treatment and is always first
       if len(A__				) > 1:
              raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'				)

       overview_doc.extend(A__				)

       # Sort
       return overview_doc



def A_	(		A__=False				)  ->		str:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	int =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Tuple =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	Any =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[str] =				0
       while api_doc[scheduler_idx]["title"] != "Schedulers":
              scheduler_idx += 1

       a__						:	Tuple =				api_doc[scheduler_idx]['sections']
       a__						:	Optional[int] =				clean_doc_toc(A__				)

       a__						:	int =				False
       if new_scheduler_doc != scheduler_doc:
              a__						:	int =				True
              if overwrite:
                     a__						:	int =				new_scheduler_doc

       if diff:
              if overwrite:
                     a__						:	Optional[Any] =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)



def A_	(		A__=False				)  ->		Tuple:
       with open(A__		,      encoding='utf-8'				) as f:
              a__						:	Optional[Any] =				yaml.safe_load(f.read()				)

       # Get to the API doc
       a__						:	Optional[int] =				0
       while content[api_idx]["title"] != "API":
              api_idx += 1
       a__						:	int =				content[api_idx]['sections']

       # Then to the model doc
       a__						:	List[Any] =				0
       while api_doc[pipeline_idx]["title"] != "Pipelines":
              pipeline_idx += 1

       a__						:	Optional[int] =				False
       a__						:	Union[str, Any] =				api_doc[pipeline_idx]['sections']
       a__						:	List[str] =				[]

       # sort sub pipeline docs
       for pipeline_doc in pipeline_docs:
              if "section" in pipeline_doc:
                     a__						:	Any =				pipeline_doc['section']
                     a__						:	Dict =				clean_doc_toc(A__				)
                     if overwrite:
                            a__						:	Optional[Any] =				new_sub_pipeline_doc
              new_pipeline_docs.append(A__				)

       # sort overall pipeline doc
       a__						:	Optional[Any] =				clean_doc_toc(A__				)

       if new_pipeline_docs != pipeline_docs:
              a__						:	List[Any] =				True
              if overwrite:
                     a__						:	Union[str, Any] =				new_pipeline_docs

       if diff:
              if overwrite:
                     a__						:	Dict =				api_doc
                     with open(A__		,      'w'		,      encoding='utf-8'				) as f:
                            f.write(yaml.dump(A__		,      allow_unicode=A__				)				)
              else:
                     raise ValueError(
                         'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'				)


if __name__ == "__main__":
    lowercase      :       List[Any]									=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    lowercase      :       str									=  parser.parse_args()

    check_scheduler_doc(args.fix_and_overwrite)
    check_pipeline_doc(args.fix_and_overwrite)


