





from typing import List, Optional

import numpy as np

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

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					A__				 =    "EncodecFeatureExtractor"
					A__				 =    ("T5Tokenizer", "T5TokenizerFast")





					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										super().__init__(_lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       self.feature_extractor
										lowerCamelCase__       =       False





					def 					__magic_name__ ( self	,  _lowerCAmelCase=None	,  _lowerCAmelCase=None	,  _lowerCAmelCase=True	):
										return self.tokenizer.get_decoder_prompt_ids(task=_lowerCAmelCase	,  language=_lowerCAmelCase	,  no_timestamps=_lowerCAmelCase	)





					def __call__( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										# For backward compatibility
										if self._in_target_context_manager:
															return self.current_processor(*_lowerCAmelCase	,  **_lowerCAmelCase	)

										lowerCamelCase__       =       kwargs.pop("audio"	,  _lowerCAmelCase	)
										lowerCamelCase__       =       kwargs.pop("sampling_rate"	,  _lowerCAmelCase	)
										lowerCamelCase__       =       kwargs.pop("text"	,  _lowerCAmelCase	)
										if len(_lowerCAmelCase	) > 0:
															lowerCamelCase__       =       args[0]
															lowerCamelCase__       =       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__       =       self.tokenizer(_lowerCAmelCase	,  **_lowerCAmelCase	)

										if audio is not None:
															lowerCamelCase__       =       self.feature_extractor(_lowerCAmelCase	,  *_lowerCAmelCase	,  sampling_rate=_lowerCAmelCase	,  **_lowerCAmelCase	)

										if audio is None:
															return inputs

										elif text is None:
															return audio_inputs

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





					def 					__magic_name__ ( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										lowerCamelCase__       =       kwargs.pop("audio"	,  _lowerCAmelCase	)
										lowerCamelCase__       =       kwargs.pop("padding_mask"	,  _lowerCAmelCase	)

										if len(_lowerCAmelCase	) > 0:
															lowerCamelCase__       =       args[0]
															lowerCamelCase__       =       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 					__magic_name__ ( self	,  *_lowerCAmelCase	,  **_lowerCAmelCase	):
										return self.tokenizer.decode(*_lowerCAmelCase	,  **_lowerCAmelCase	)






					def 					__magic_name__ ( self	,  _lowerCAmelCase	,  _lowerCAmelCase = None	):
										lowerCamelCase__       =       to_numpy(_lowerCAmelCase	)
										lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       audio_values.shape

										if padding_mask is None:
															return list(_lowerCAmelCase	)

										lowerCamelCase__       =       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)
										lowerCamelCase__       =       seq_len - padding_mask.shape[-1]
										lowerCamelCase__       =       1 - self.feature_extractor.padding_value
										lowerCamelCase__       =       np.pad(_lowerCAmelCase	,  ((0, 0), (0, difference))	,  "constant"	,  constant_values=_lowerCAmelCase	)

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

										return audio_values








def    __UpperCamelCase      (    a,     a) ->Optional[int]:
					lowerCamelCase__       =       [1]
					for i in range(2,     a):
										factorials.append(factorials[-1] * i)
					assert 0 <= k < factorials[-1] * n, "k out of bounds"

					lowerCamelCase__       =       []
					lowerCamelCase__       =       list(range(a))

					# Find permutation
					while factorials:
										lowerCamelCase__       =       factorials.pop()
										lowerCamelCase__    ,		lowerCamelCase__       =       divmod(a,     a)
										permutation.append(elements[number])
										elements.remove(elements[number])
					permutation.append(elements[0])

					return permutation


if __name__ == "__main__":
						import doctest

						doctest.testmod()

