


from ...processing_utils import ProcessorMixin



class 			lowerCamelCase__						(   UpperCAmelCase_			):
						lowerCAmelCase       =     """SpeechT5FeatureExtractor"""
						lowerCAmelCase       =     """SpeechT5Tokenizer"""







						def __init__(							self   :    Optional[int]			,			_lowercase   :    Any			,			_lowercase   :    List[str]		):
								super().__init__(UpperCamelCase_			,			UpperCamelCase_		)







						def __call__(							self   :    Union[str, Any]			,			*_lowercase   :    Tuple			,			**_lowercase   :    Any		):
								A					       = kwargs.pop('audio'			,			UpperCamelCase_		)
								A					       = kwargs.pop('text'			,			UpperCamelCase_		)
								A					       = kwargs.pop('text_target'			,			UpperCamelCase_		)
								A					       = kwargs.pop('audio_target'			,			UpperCamelCase_		)
								A					       = kwargs.pop('sampling_rate'			,			UpperCamelCase_		)

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

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

								if inputs is None:
										return targets

								if targets is not None:
										A					       = labels

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

								return inputs







						def __a						(							self   :    List[str]			,			*_lowercase   :    str			,			**_lowercase   :    Any		):
								A					       = kwargs.pop('input_values'			,			UpperCamelCase_		)
								A					       = kwargs.pop('input_ids'			,			UpperCamelCase_		)
								A					       = kwargs.pop('labels'			,			UpperCamelCase_		)

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

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

								if inputs is None:
										return targets

								if targets is not None:
										A					       = labels

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

								return inputs







						def __a						(							self   :    List[str]			,			*_lowercase   :    List[str]			,			**_lowercase   :    Union[str, Any]		):
								return self.tokenizer.batch_decode(*UpperCamelCase_			,			**UpperCamelCase_		)







						def __a						(							self   :    int			,			*_lowercase   :    Tuple			,			**_lowercase   :    int		):
								return self.tokenizer.decode(*UpperCamelCase_			,			**UpperCamelCase_		)









"""simple docstring"""


from math import factorial







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__ ,			UpperCamelCase__	)      ->		float:







		"""simple docstring"""





		if successes > trials:
				raise ValueError('successes must be lower or equal to trials'	)
		if trials < 0 or successes < 0:
				raise ValueError('the function is defined for non-negative integers'	)
		if not isinstance(UpperCamelCase__ ,			UpperCamelCase__	) or not isinstance(UpperCamelCase__ ,			UpperCamelCase__	):
				raise ValueError('the function is defined for non-negative integers'	)
		if not 0 < prob < 1:
				raise ValueError('prob has to be in range of 1 - 0'	)
		A					       = (prob**successes) * ((1 - prob) ** (trials - successes))
		# Calculate the binomial coefficient: n! / k!(n-k)!
		A					       = float(factorial(UpperCamelCase__	)	)
		coefficient /= factorial(UpperCamelCase__	) * factorial(trials - successes	)
		return probability * coefficient


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

	testmod()
	print("Probability of 2 successes out of 4 trails")
	print("with probability of 0.75 is:", end=" ")
	print(binomial_distribution(2, 4, 0.75))





