
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer







class        lowercase	(      SCREAMING_SNAKE_CASE__	):
							lowercase_ : List[str]       ='''Wav2Vec2FeatureExtractor'''
							lowercase_ : Optional[int]       ='''AutoTokenizer'''




							def __init__(       self     ,A__     ,A__):
												super().__init__(A__     ,A__)
												lowercase					=		self.feature_extractor
												lowercase					=		False




							@classmethod
							def 				A__   (       cls     ,A__     ,**A__):
												try:
																	return super().from_pretrained(A__     ,**A__)
												except OSError:
																	warnings.warn(
																	    f'Loading a tokenizer inside {cls.__name__} from a config that does not'
																	    ''' include a `tokenizer_class` attribute is deprecated and will be '''
																	    '''removed in v5. Please add `\'tokenizer_class\': \'Wav2Vec2CTCTokenizer\'`'''
																	    ''' attribute to either your `config.json` or `tokenizer_config.json` '''
																	    '''file to suppress this warning: '''     ,A__     ,)

																	lowercase					=		WavaVecaFeatureExtractor.from_pretrained(A__     ,**A__)
																	lowercase					=		WavaVecaCTCTokenizer.from_pretrained(A__     ,**A__)

																	return cls(feature_extractor=A__     ,tokenizer=A__)




							def __call__(       self     ,*A__     ,**A__):
												# For backward compatibility
												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.''')
																	lowercase					=		kwargs.pop('''raw_speech''')
												else:
																	lowercase					=		kwargs.pop('''audio'''     ,A__)
												lowercase					=		kwargs.pop('''sampling_rate'''     ,A__)
												lowercase					=		kwargs.pop('''text'''     ,A__)
												if len(A__) > 0:
																	lowercase					=		args[0]
																	lowercase					=		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:
																	lowercase					=		self.feature_extractor(A__     ,*A__     ,sampling_rate=A__     ,**A__)
												if text is not None:
																	lowercase					=		self.tokenizer(A__     ,**A__)

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




							def 				A__   (       self     ,*A__     ,**A__):
												# For backward compatibility
												if self._in_target_context_manager:
																	return self.current_processor.pad(*A__     ,**A__)

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

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

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




							def 				A__   (       self     ,*A__     ,**A__):
												return self.tokenizer.batch_decode(*A__     ,**A__)




							def 				A__   (       self     ,*A__     ,**A__):
												return self.tokenizer.decode(*A__     ,**A__)





							@contextmanager
							def 				A__   (       self):
												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.''')
												lowercase					=		True
												lowercase					=		self.tokenizer
												yield
												lowercase					=		self.feature_extractor
												lowercase					=		False






import argparse
from collections import defaultdict

import yaml


lowercase__	:Optional[int]										=					"docs/source/en/_toctree.yml"



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		defaultdict(lowerCAmelCase__	)
					for doc in model_doc:
										counts[doc["local"]] += 1
					lowercase					=		[key for key, value in counts.items() if value > 1]

					lowercase					=		[]
					for duplicate_key in duplicates:
										lowercase					=		list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}	)
										if len(lowerCAmelCase__	) > 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 model_doc if counts[doc['''local''']] == 1]	)

					# Sort
					return sorted(lowerCAmelCase__				,    key=lambda lowerCAmelCase__	: s["title"].lower()	)



def  UpperCamelCase					(							lowerCAmelCase__=False	):
					'''simple docstring'''
					with open(lowerCAmelCase__				,    encoding='''utf-8'''	) as f:
										lowercase					=		yaml.safe_load(f.read()	)

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

					# Then to the model doc
					lowercase					=		0
					while api_doc[model_idx]["title"] != "Models":
										model_idx += 1

					lowercase					=		api_doc[model_idx]['''sections''']

					lowercase					=		[(idx, section) for idx, section in enumerate(lowerCAmelCase__	) if '''sections''' in section]
					lowercase					=		False
					for idx, modality_doc in modalities_docs:
										lowercase					=		modality_doc['''sections''']
										lowercase					=		clean_model_doc_toc(lowerCAmelCase__	)

										if old_modality_doc != new_modality_doc:
															lowercase					=		True
															if overwrite:
																				lowercase					=		new_modality_doc

					if diff:
										if overwrite:
															lowercase					=		model_doc
															lowercase					=		api_doc
															with open(lowerCAmelCase__				,    '''w'''				,    encoding='''utf-8'''	) as f:
																				f.write(yaml.dump(lowerCAmelCase__				,    allow_unicode=lowerCAmelCase__	)	)
										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__	:Any										=					argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							lowercase__	:int										=					parser.parse_args()

							check_model_doc(args.fix_and_overwrite)




