



"""simple docstring"""

from ...processing_utils import ProcessorMixin
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[str]  						= '''SpeechT5FeatureExtractor'''
	_SCREAMING_SNAKE_CASE	:		int  						= '''SpeechT5Tokenizer'''





	def __init__(      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       str     ,       _SCREAMING_SNAKE_CASE				:       Tuple    )						->	Optional[Any]:





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE    )





	def __call__(      self				:       Any     ,       *_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       **_SCREAMING_SNAKE_CASE				:       int    )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Any							=     kwargs.pop('audio'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Any							=     kwargs.pop('text'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     kwargs.pop('text_target'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							str							=     kwargs.pop('audio_target'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     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:
									SCREAMING_SNAKE_CASE						:							Any							=     self.feature_extractor(_SCREAMING_SNAKE_CASE     ,       *_SCREAMING_SNAKE_CASE     ,       sampling_rate=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					elif text is not None:
									SCREAMING_SNAKE_CASE						:							str							=     self.tokenizer(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					else:
									SCREAMING_SNAKE_CASE						:							Tuple							=     None

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

					if inputs is None:
									return targets

					if targets is not None:
									SCREAMING_SNAKE_CASE						:							Tuple							=     labels

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

					return inputs





	def 			_lowerCAmelCase     (      self				:       Optional[int]     ,       *_SCREAMING_SNAKE_CASE				:       List[Any]     ,       **_SCREAMING_SNAKE_CASE				:       Tuple    )						->	Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     kwargs.pop('input_values'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Dict							=     kwargs.pop('input_ids'     ,       _SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							int							=     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:
									SCREAMING_SNAKE_CASE						:							Dict							=     self.feature_extractor.pad(_SCREAMING_SNAKE_CASE     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					elif input_ids is not None:
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.tokenizer.pad(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					else:
									SCREAMING_SNAKE_CASE						:							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]):
													SCREAMING_SNAKE_CASE						:							str							=     self.tokenizer.pad(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							int							=     targets['input_ids']
									else:
													SCREAMING_SNAKE_CASE						:							Any							=     self.feature_extractor.feature_size
													SCREAMING_SNAKE_CASE						:							int							=     self.feature_extractor.num_mel_bins
													SCREAMING_SNAKE_CASE						:							Any							=     self.feature_extractor.pad(_SCREAMING_SNAKE_CASE     ,       *_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
													SCREAMING_SNAKE_CASE						:							Optional[Any]							=     feature_size_hack
													SCREAMING_SNAKE_CASE						:							Dict							=     targets['input_values']
					else:
									SCREAMING_SNAKE_CASE						:							List[str]							=     None

					if inputs is None:
									return targets

					if targets is not None:
									SCREAMING_SNAKE_CASE						:							List[Any]							=     labels

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

					return inputs





	def 			_lowerCAmelCase     (      self				:       Dict     ,       *_SCREAMING_SNAKE_CASE				:       Union[str, Any]     ,       **_SCREAMING_SNAKE_CASE				:       Optional[int]    )						->	List[Any]:





					"""simple docstring"""


					return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )







	def 			_lowerCAmelCase     (      self				:       Union[str, Any]     ,       *_SCREAMING_SNAKE_CASE				:       Dict     ,       **_SCREAMING_SNAKE_CASE				:       List[str]    )						->	Optional[int]:





					"""simple docstring"""


					return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )










"""simple docstring"""

from __future__ import annotations
def  __snake_case							(	__A      :						list[int]     )     ->       list[int]:






				'''simple docstring'''
				if len(__A     ) == 0:
								return array

				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							Optional[int]							=     min(__A     ), max(__A     )

				# Compute the variables
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     _max - _min + 1
				SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						:							List[str]							=     [0] * holes_range, [0] * holes_range

				# Make the sorting.
				for i in array:
								SCREAMING_SNAKE_CASE						:							List[Any]							=     i - _min
								SCREAMING_SNAKE_CASE						:							Dict							=     i
								holes_repeat[index] += 1

				# Makes the array back by replacing the numbers.
				SCREAMING_SNAKE_CASE						:							int							=     0
				for i in range(__A     ):
								while holes_repeat[i] > 0:
												SCREAMING_SNAKE_CASE						:							int							=     holes[i]
												index += 1
												holes_repeat[i] -= 1

    # Returns the sorted array.
				return array


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							A_						:    Optional[Any]				=      input('Enter numbers separated by comma:\n')
							A_						:    Union[str, Any]				=      [int(x) for x in user_input.split(',')]
							print(pigeon_sort(unsorted))





