from typing import List

import numpy as np
def 							lowerCAmelCase(     __lowerCamelCase						):
					__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				,    __lowerCamelCase						):
					__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				,    __lowerCamelCase						):
					__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						):
					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				,    __lowerCamelCase						):
					__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





import warnings
from contextlib import contextmanager

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


class 						a__							(       __snake_case		):
			A__    :       Any        =      'Wav2Vec2FeatureExtractor'
			A__    :       str        =      'AutoTokenizer'



			def __init__(						self				,						UpperCAmelCase				,						UpperCAmelCase   )			->   Union[str, Any]:
								super().__init__(UpperCAmelCase				,						UpperCAmelCase   )
								__a		 =       self.feature_extractor
								__a		 =       False



			@classmethod
			def __SCREAMING_SNAKE_CASE							(						cls				,						UpperCAmelCase				,						**UpperCAmelCase   )			->   Dict:
								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				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[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 __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   Tuple:
								# 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 __SCREAMING_SNAKE_CASE							(						self				,						*UpperCAmelCase				,						**UpperCAmelCase   )			->   List[str]:
								return self.tokenizer.batch_decode(*UpperCAmelCase				,						**UpperCAmelCase   )



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







			@contextmanager
			def __SCREAMING_SNAKE_CASE							(						self   )			->   List[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




