



import warnings
from contextlib import contextmanager

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



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Optional[int]								=     'Wav2Vec2FeatureExtractor'
				A_   :     Optional[Any]								=     'AutoTokenizer'


				def __init__(							self      :  int						, __UpperCamelCase      :  Optional[Any]						, __UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											super().__init__(__UpperCamelCase						, __UpperCamelCase       )
											A      						=    self.feature_extractor
											A      						=    False


				@classmethod
				def __UpperCamelCase						(							cls      :  List[Any]						, __UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  str       )		->     Union[str, Any]:
											try:
																		return super().from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
											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: '						, __UpperCamelCase						, )

																		A      						=    WavaVecaFeatureExtractor.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )
																		A      						=    WavaVecaCTCTokenizer.from_pretrained(__UpperCamelCase						, **__UpperCamelCase       )

																		return cls(feature_extractor=__UpperCamelCase						, tokenizer=__UpperCamelCase       )


				def __call__(							self      :  Optional[int]						, *__UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  Dict       )		->     Optional[Any]:
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor(*__UpperCamelCase						, **__UpperCamelCase       )

											if "raw_speech" in kwargs:
																		warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'       )
																		A      						=    kwargs.pop('raw_speech'       )
											else:
																		A      						=    kwargs.pop('audio'						, __UpperCamelCase       )
											A      						=    kwargs.pop('sampling_rate'						, __UpperCamelCase       )
											A      						=    kwargs.pop('text'						, __UpperCamelCase       )
											if len(__UpperCamelCase       ) > 0:
																		A      						=    args[0]
																		A      						=    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:
																		A      						=    self.feature_extractor(__UpperCamelCase						, *__UpperCamelCase						, sampling_rate=__UpperCamelCase						, **__UpperCamelCase       )
											if text is not None:
																		A      						=    self.tokenizer(__UpperCamelCase						, **__UpperCamelCase       )

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


				def __UpperCamelCase						(							self      :  List[str]						, *__UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											# For backward compatibility
											if self._in_target_context_manager:
																		return self.current_processor.pad(*__UpperCamelCase						, **__UpperCamelCase       )

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

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

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


				def __UpperCamelCase						(							self      :  List[str]						, *__UpperCamelCase      :  Union[str, Any]						, **__UpperCamelCase      :  Union[str, Any]       )		->     Union[str, Any]:
											return self.tokenizer.batch_decode(*__UpperCamelCase						, **__UpperCamelCase       )


				def __UpperCamelCase						(							self      :  Tuple						, *__UpperCamelCase      :  List[str]						, **__UpperCamelCase      :  str       )		->     Dict:
											return self.tokenizer.decode(*__UpperCamelCase						, **__UpperCamelCase       )


				@contextmanager
				def __UpperCamelCase						(							self      :  Optional[int]       )		->     Optional[Any]:
											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.'       )
											A      						=    True
											A      						=    self.tokenizer
											yield
											A      						=    self.feature_extractor
											A      						=    False




from typing import List

import numpy as np






def  lowerCamelCase_	(      lowerCAmelCase__	:	dict				)       ->							int:

							'''simple docstring'''






							A      						=    {key: len(lowerCAmelCase__				) for key, value in gen_kwargs.items() if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				)}
							if len(set(lists_lengths.values()				)				) > 1:
														raise RuntimeError(
														    (
														        'Sharding is ambiguous for this dataset: '
														        + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
														        + '\n'.join(F'''\t- key {key} has length {length}''' for key, length in lists_lengths.items()				)
														        + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
														        + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
														    )				)
							A      						=    max(lists_lengths.values()			,	default=0				)
							return max(1			,	lowerCAmelCase__				)






def  lowerCamelCase_	(      lowerCAmelCase__	:	int			,	lowerCAmelCase__	:	int				)       ->							List[range]:

							'''simple docstring'''






							A      						=    []
							for group_idx in range(lowerCAmelCase__				):
														A      						=    num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
														if num_shards_to_add == 0:
																					break
														A      						=    shards_indices_per_group[-1].stop if shards_indices_per_group else 0
														A      						=    range(lowerCAmelCase__			,	start + num_shards_to_add				)
														shards_indices_per_group.append(lowerCAmelCase__				)
							return shards_indices_per_group






def  lowerCamelCase_	(      lowerCAmelCase__	:	dict			,	lowerCAmelCase__	:	int				)       ->							List[dict]:

							'''simple docstring'''






							A      						=    _number_of_shards_in_gen_kwargs(lowerCAmelCase__				)
							if num_shards == 1:
														return [dict(lowerCAmelCase__				)]
							else:
														A      						=    _distribute_shards(num_shards=lowerCAmelCase__			,	max_num_jobs=lowerCAmelCase__				)
														return [
														    {
														        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
														        if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				)
														        else value
														        for key, value in gen_kwargs.items()
														    }
														    for group_idx in range(len(lowerCAmelCase__				)				)
														]






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[dict]				)       ->							dict:

							'''simple docstring'''






							return {
							    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
							    if isinstance(gen_kwargs_list[0][key]			,	lowerCAmelCase__				)
							    else gen_kwargs_list[0][key]
							    for key in gen_kwargs_list[0]
							}






def  lowerCamelCase_	(      lowerCAmelCase__	:	np.random.Generator			,	lowerCAmelCase__	:	dict				)       ->							dict:

							'''simple docstring'''






							A      						=    {len(lowerCAmelCase__				) for value in gen_kwargs.values() if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				)}
							A      						=    {}
							for size in list_sizes:
														A      						=    list(range(lowerCAmelCase__				)				)
														rng.shuffle(indices_per_size[size]				)
							# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
							A      						=    dict(lowerCAmelCase__				)
							for key, value in shuffled_kwargs.items():
														if isinstance(lowerCAmelCase__			,	lowerCAmelCase__				):
																					A      						=    [value[i] for i in indices_per_size[len(lowerCAmelCase__				)]]
							return shuffled_kwargs