



from ...processing_utils import ProcessorMixin







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''SpeechT5FeatureExtractor'''
			UpperCamelCase        =					'''SpeechT5Tokenizer'''



			def __init__(						self       :  Optional[int]			,		A       :  Any			,		A       :  List[Any])       ->   str:



						"""simple docstring"""






						super().__init__(A			,		A)



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



						"""simple docstring"""






						_UpperCAmelCase			=       kwargs.pop('audio'			,		A)
						_UpperCAmelCase			=       kwargs.pop('text'			,		A)
						_UpperCAmelCase			=       kwargs.pop('text_target'			,		A)
						_UpperCAmelCase			=       kwargs.pop('audio_target'			,		A)
						_UpperCAmelCase			=       kwargs.pop('sampling_rate'			,		A)

						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:
									_UpperCAmelCase			=       self.feature_extractor(A			,		*A			,		sampling_rate=A			,		**A)
						elif text is not None:
									_UpperCAmelCase			=       self.tokenizer(A			,		**A)
						else:
									_UpperCAmelCase			=       None

						if audio_target is not None:
									_UpperCAmelCase			=       self.feature_extractor(audio_target=A			,		*A			,		sampling_rate=A			,		**A)
									_UpperCAmelCase			=       targets['input_values']
						elif text_target is not None:
									_UpperCAmelCase			=       self.tokenizer(A			,		**A)
									_UpperCAmelCase			=       targets['input_ids']
						else:
									_UpperCAmelCase			=       None

						if inputs is None:
									return targets

						if targets is not None:
									_UpperCAmelCase			=       labels

									_UpperCAmelCase			=       targets.get('attention_mask')
									if decoder_attention_mask is not None:
												_UpperCAmelCase			=       decoder_attention_mask

						return inputs



			def    _lowerCamelCase	(						self       :  int			,		*A       :  Optional[Any]			,		**A       :  Dict)       ->   Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       kwargs.pop('input_values'			,		A)
						_UpperCAmelCase			=       kwargs.pop('input_ids'			,		A)
						_UpperCAmelCase			=       kwargs.pop('labels'			,		A)

						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:
									_UpperCAmelCase			=       self.feature_extractor.pad(A			,		*A			,		**A)
						elif input_ids is not None:
									_UpperCAmelCase			=       self.tokenizer.pad(A			,		**A)
						else:
									_UpperCAmelCase			=       None

						if labels is not None:
									if "input_ids" in labels or (isinstance(A			,		A) and "input_ids" in labels[0]):
												_UpperCAmelCase			=       self.tokenizer.pad(A			,		**A)
												_UpperCAmelCase			=       targets['input_ids']
									else:
												_UpperCAmelCase			=       self.feature_extractor.feature_size
												_UpperCAmelCase			=       self.feature_extractor.num_mel_bins
												_UpperCAmelCase			=       self.feature_extractor.pad(A			,		*A			,		**A)
												_UpperCAmelCase			=       feature_size_hack
												_UpperCAmelCase			=       targets['input_values']
						else:
									_UpperCAmelCase			=       None

						if inputs is None:
									return targets

						if targets is not None:
									_UpperCAmelCase			=       labels

									_UpperCAmelCase			=       targets.get('attention_mask')
									if decoder_attention_mask is not None:
												_UpperCAmelCase			=       decoder_attention_mask

						return inputs



			def    _lowerCamelCase	(						self       :  str			,		*A       :  Optional[Any]			,		**A       :  Optional[Any])       ->   List[str]:



						"""simple docstring"""






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



			def    _lowerCamelCase	(						self       :  List[str]			,		*A       :  List[Any]			,		**A       :  Dict)       ->   Optional[int]:



						"""simple docstring"""






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





from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat





def 							A     (					)    ->			tuple[list[int], int]:







			'''simple docstring'''
			_UpperCAmelCase			=       [randint(-1_000							,       1_000   ) for i in range(10   )]
			_UpperCAmelCase			=       randint(-5_000							,       5_000   )
			return (arr, r)


UpperCAmelCase__       =	make_dataset()





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, ...]:







			'''simple docstring'''
			for triplet in permutations(_UpperCAmelCase							,       3   ):
						if sum(_UpperCAmelCase   ) == target:
									return tuple(sorted(_UpperCAmelCase   )   )
			return (0, 0, 0)





def 							A     (					_UpperCAmelCase    :       list[int]							,       _UpperCAmelCase    :       int   )    ->			tuple[int, int, int]:







			'''simple docstring'''
			arr.sort()
			_UpperCAmelCase			=       len(_UpperCAmelCase   )
			for i in range(n - 1   ):
						_UpperCAmelCase	,					_UpperCAmelCase			=       i + 1, n - 1
						while left < right:
									if arr[i] + arr[left] + arr[right] == target:
												return (arr[i], arr[left], arr[right])
									elif arr[i] + arr[left] + arr[right] < target:
												left += 1
									elif arr[i] + arr[left] + arr[right] > target:
												right -= 1
			return (0, 0, 0)





def 							A     (					)    ->			tuple[float, float]:







			'''simple docstring'''
			_UpperCAmelCase			=       '\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
			_UpperCAmelCase			=       '\ntriplet_sum1(*dataset)\n'
			_UpperCAmelCase			=       '\ntriplet_sum2(*dataset)\n'
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			_UpperCAmelCase			=       repeat(setup=_UpperCAmelCase							,       stmt=_UpperCAmelCase							,       repeat=5							,       number=10_000   )
			return (min(_UpperCAmelCase   ), min(_UpperCAmelCase   ))


if __name__ == "__main__":
							from doctest import testmod

							testmod()
							UpperCAmelCase__       =	solution_times()
							print(f"""The time for naive implementation is {times[0]}.""")
							print(f"""The time for optimized implementation is {times[1]}.""")
