





from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 			UpperCamelCase_		(  _lowerCamelCase					):
					lowerCAmelCase_							=	'''EncodecFeatureExtractor'''
					lowerCAmelCase_							=	('''T5Tokenizer''', '''T5TokenizerFast''')




					def __init__(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[Any]:
										super().__init__(lowerCAmelCase_				,						lowerCAmelCase_  )
										_snake_case									=				self.feature_extractor
										_snake_case									=				False




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_=None				,						lowerCAmelCase_=None				,						lowerCAmelCase_=True  )		->							str:
										return self.tokenizer.get_decoder_prompt_ids(task=lowerCAmelCase_				,						language=lowerCAmelCase_				,						no_timestamps=lowerCAmelCase_  )




					def __call__(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Tuple:
										# For backward compatibility
										if self._in_target_context_manager:
															return self.current_processor(*lowerCAmelCase_				,						**lowerCAmelCase_  )

										_snake_case									=				kwargs.pop('audio'				,						lowerCAmelCase_  )
										_snake_case									=				kwargs.pop('sampling_rate'				,						lowerCAmelCase_  )
										_snake_case									=				kwargs.pop('text'				,						lowerCAmelCase_  )
										if len(lowerCAmelCase_  ) > 0:
															_snake_case									=				args[0]
															_snake_case									=				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 text is not None:
															_snake_case									=				self.tokenizer(lowerCAmelCase_				,						**lowerCAmelCase_  )

										if audio is not None:
															_snake_case									=				self.feature_extractor(lowerCAmelCase_				,						*lowerCAmelCase_				,						sampling_rate=lowerCAmelCase_				,						**lowerCAmelCase_  )

										if audio is None:
															return inputs

										elif text is None:
															return audio_inputs

										else:
															_snake_case									=				audio_inputs['input_values']
															if "padding_mask" in audio_inputs:
																				_snake_case									=				audio_inputs['padding_mask']
															return inputs




					def 	lowerCAmelCase			(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Union[str, Any]:
										_snake_case									=				kwargs.pop('audio'				,						lowerCAmelCase_  )
										_snake_case									=				kwargs.pop('padding_mask'				,						lowerCAmelCase_  )

										if len(lowerCAmelCase_  ) > 0:
															_snake_case									=				args[0]
															_snake_case									=				args[1:]

										if audio_values is not None:
															return self._decode_audio(lowerCAmelCase_				,						padding_mask=lowerCAmelCase_  )
										else:
															return self.tokenizer.batch_decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							List[Any]:
										return self.tokenizer.decode(*lowerCAmelCase_				,						**lowerCAmelCase_  )






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = None  )		->							List[np.ndarray]:
										_snake_case									=				to_numpy(lowerCAmelCase_  )
										_snake_case							,     _snake_case							,     _snake_case									=				audio_values.shape

										if padding_mask is None:
															return list(lowerCAmelCase_  )

										_snake_case									=				to_numpy(lowerCAmelCase_  )

										# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
										# token (so that the generated audio values are **not** treated as padded tokens)
										_snake_case									=				seq_len - padding_mask.shape[-1]
										_snake_case									=				1 - self.feature_extractor.padding_value
										_snake_case									=				np.pad(lowerCAmelCase_				,						((0, 0), (0, difference))				,						'constant'				,						constant_values=lowerCAmelCase_  )

										_snake_case									=				audio_values.tolist()
										for i in range(lowerCAmelCase_  ):
															_snake_case									=				np.asarray(audio_values[i]  )[
															    padding_mask[i][None, :] != self.feature_extractor.padding_value
															]
															_snake_case									=				sliced_audio.reshape(lowerCAmelCase_				,						-1  )

										return audio_values










def 						lowerCamelCase__     (     UpperCamelCase__			:       list						)			-> list:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					for i in range(1       ,    UpperCamelCase__						):
										_snake_case									=				collection[i]
										_snake_case									=				0
										_snake_case									=				i - 1

										while low <= high:
															_snake_case									=				(low + high) // 2
															if val < collection[mid]:
																				_snake_case									=				mid - 1
															else:
																				_snake_case									=				mid + 1
										for j in range(UpperCamelCase__       ,    UpperCamelCase__       ,    -1						):
															_snake_case									=				collection[j - 1]
										_snake_case									=				val
					return collection


if __name__ == "__main__":
					UpperCAmelCase_      							=     input("""Enter numbers separated by a comma:\n""").strip()
					UpperCAmelCase_      							=     [int(item) for item in user_input.split(""",""")]
					print(binary_insertion_sort(unsorted))



