

from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy






class 	A_		(	SCREAMING_SNAKE_CASE    ):
							_UpperCAmelCase   :  Union[str, Any]										=				'''EncodecFeatureExtractor'''
							_UpperCAmelCase   :  Dict										=				('''T5Tokenizer''', '''T5TokenizerFast''')
							def __init__(		self		:      int       ,SCREAMING_SNAKE_CASE__		:      str       ,SCREAMING_SNAKE_CASE__		:      Optional[Any]):
								super().__init__(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			self.feature_extractor
								__lowerCamelCase    :    List[str] 	=			False
							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      str=None       ,SCREAMING_SNAKE_CASE__		:      Any=None       ,SCREAMING_SNAKE_CASE__		:      Union[str, Any]=True):
								return self.tokenizer.get_decoder_prompt_ids(task=SCREAMING_SNAKE_CASE__       ,language=SCREAMING_SNAKE_CASE__       ,no_timestamps=SCREAMING_SNAKE_CASE__)
							def __call__(		self		:      str       ,*SCREAMING_SNAKE_CASE__		:      Dict       ,**SCREAMING_SNAKE_CASE__		:      Optional[int]):
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor(*SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			kwargs.pop('audio'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    str 	=			kwargs.pop('sampling_rate'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[int] 	=			kwargs.pop('text'       ,SCREAMING_SNAKE_CASE__)
								if len(SCREAMING_SNAKE_CASE__) > 0:
									__lowerCamelCase    :    Dict 	=			args[0]
									__lowerCamelCase    :    Union[str, Any] 	=			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:
									__lowerCamelCase    :    List[Any] 	=			self.tokenizer(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								if audio is not None:
									__lowerCamelCase    :    int 	=			self.feature_extractor(SCREAMING_SNAKE_CASE__       ,*SCREAMING_SNAKE_CASE__       ,sampling_rate=SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)

								if audio is None:
									return inputs

								elif text is None:
									return audio_inputs

								else:
									__lowerCamelCase    :    Optional[Any] 	=			audio_inputs['input_values']
									if "padding_mask" in audio_inputs:
										__lowerCamelCase    :    Union[str, Any] 	=			audio_inputs['padding_mask']
									return inputs
							def    lowerCAmelCase					(		self		:      Tuple       ,*SCREAMING_SNAKE_CASE__		:      List[Any]       ,**SCREAMING_SNAKE_CASE__		:      List[Any]):
								__lowerCamelCase    :    Union[str, Any] 	=			kwargs.pop('audio'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Union[str, Any] 	=			kwargs.pop('padding_mask'       ,SCREAMING_SNAKE_CASE__)

								if len(SCREAMING_SNAKE_CASE__) > 0:
									__lowerCamelCase    :    Optional[Any] 	=			args[0]
									__lowerCamelCase    :    Optional[int] 	=			args[1:]

								if audio_values is not None:
									return self._decode_audio(SCREAMING_SNAKE_CASE__       ,padding_mask=SCREAMING_SNAKE_CASE__)
								else:
									return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      int       ,*SCREAMING_SNAKE_CASE__		:      List[str]       ,**SCREAMING_SNAKE_CASE__		:      str):
								return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)


							def    lowerCAmelCase					(		self		:      Union[str, Any]       ,SCREAMING_SNAKE_CASE__		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Optional = None):
								__lowerCamelCase    :    List[str] 	=			to_numpy(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase	,			__lowerCamelCase	,			__lowerCamelCase    :    List[str] 	=			audio_values.shape

								if padding_mask is None:
									return list(SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Any 	=			to_numpy(SCREAMING_SNAKE_CASE__)

								# 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)
								__lowerCamelCase    :    Optional[Any] 	=			seq_len - padding_mask.shape[-1]
								__lowerCamelCase    :    Optional[int] 	=			1 - self.feature_extractor.padding_value
								__lowerCamelCase    :    Dict 	=			np.pad(SCREAMING_SNAKE_CASE__       ,((0, 0), (0, difference))       ,'constant'       ,constant_values=SCREAMING_SNAKE_CASE__)

								__lowerCamelCase    :    Optional[int] 	=			audio_values.tolist()
								for i in range(SCREAMING_SNAKE_CASE__):
									__lowerCamelCase    :    Optional[int] 	=			np.asarray(audio_values[i])[
									    padding_mask[i][None, :] != self.feature_extractor.padding_value
									]
									__lowerCamelCase    :    Any 	=			sliced_audio.reshape(SCREAMING_SNAKE_CASE__       ,-1)

								return audio_values







from __future__ import annotations






def        SCREAMING_SNAKE_CASE__	(  lowerCamelCase__     ,			lowerCamelCase__      )  ->					bool:
	if len(lowerCamelCase__      ) == 0:
		return False
	__lowerCamelCase    :    List[Any] 	=			len(lowerCamelCase__      ) // 2
	if a_list[midpoint] == item:
		return True
	if item < a_list[midpoint]:
		return binary_search(a_list[:midpoint]     ,			lowerCamelCase__      )
	else:
		return binary_search(a_list[midpoint + 1 :]     ,			lowerCamelCase__      )


if __name__ == "__main__":
					a		=input("""Enter numbers separated by comma:\n""").strip()
					a		=[int(item.strip()) for item in user_input.split(""",""")]
					a		=int(input("""Enter the number to be found in the list:\n""").strip())
					a		="""""" if binary_search(sequence, target) else """not """
					print(F"""{target} was {not_str}found in {sequence}""")




