



from ...processing_utils import ProcessorMixin


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Tuple			       =    "SpeechT5FeatureExtractor"
					A__				:		int			       =    "SpeechT5Tokenizer"



					def __init__(     self					:       Optional[int]				,					_snake_case					:       int				,					_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								super().__init__(_snake_case				,					_snake_case					)



					def __call__(     self					:       Tuple				,					*_snake_case					:       int				,					**_snake_case					:       int					):






								"""simple docstring"""


								A__       =    kwargs.pop('audio'				,					_snake_case					)
								A__       =    kwargs.pop('text'				,					_snake_case					)
								A__       =    kwargs.pop('text_target'				,					_snake_case					)
								A__       =    kwargs.pop('audio_target'				,					_snake_case					)
								A__       =    kwargs.pop('sampling_rate'				,					_snake_case					)

								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(_snake_case				,					*_snake_case				,					sampling_rate=_snake_case				,					**_snake_case					)
								elif text is not None:
											A__       =    self.tokenizer(_snake_case				,					**_snake_case					)
								else:
											A__       =    None

								if audio_target is not None:
											A__       =    self.feature_extractor(audio_target=_snake_case				,					*_snake_case				,					sampling_rate=_snake_case				,					**_snake_case					)
											A__       =    targets['input_values']
								elif text_target is not None:
											A__       =    self.tokenizer(_snake_case				,					**_snake_case					)
											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					:       str				,					*_snake_case					:       Union[str, Any]				,					**_snake_case					:       int					):






								"""simple docstring"""


								A__       =    kwargs.pop('input_values'				,					_snake_case					)
								A__       =    kwargs.pop('input_ids'				,					_snake_case					)
								A__       =    kwargs.pop('labels'				,					_snake_case					)

								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(_snake_case				,					*_snake_case				,					**_snake_case					)
								elif input_ids is not None:
											A__       =    self.tokenizer.pad(_snake_case				,					**_snake_case					)
								else:
											A__       =    None

								if labels is not None:
											if "input_ids" in labels or (isinstance(_snake_case				,					_snake_case					) and "input_ids" in labels[0]):
														A__       =    self.tokenizer.pad(_snake_case				,					**_snake_case					)
														A__       =    targets['input_ids']
											else:
														A__       =    self.feature_extractor.feature_size
														A__       =    self.feature_extractor.num_mel_bins
														A__       =    self.feature_extractor.pad(_snake_case				,					*_snake_case				,					**_snake_case					)
														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					:       Optional[Any]				,					*_snake_case					:       Dict				,					**_snake_case					:       str					):






								"""simple docstring"""


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



					def     _a							(     self					:       int				,					*_snake_case					:       Any				,					**_snake_case					:       str					):






								"""simple docstring"""


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









import math
import random


def A						(       __UpperCamelCase ,	__UpperCamelCase = False					)						->      float:
			if deriv:
						return value * (1 - value)
			return 1 / (1 + math.exp(-value					))


# Initial Value
SCREAMING_SNAKE_CASE__       =      0.02


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      float:
			A__       =    float(2 * (random.randint(1 ,	100					)) - 1					)

			for _ in range(__UpperCamelCase					):
						# Forward propagation
						A__       =    sigmoid_function(INITIAL_VALUE * weight					)
						# How much did we miss?
						A__       =    (expected / 100) - layer_a
						# Error delta
						A__       =    layer_1_error * sigmoid_function(__UpperCamelCase ,	__UpperCamelCase					)
						# Update weight
						weight += INITIAL_VALUE * layer_1_delta

			return layer_a * 100


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							SCREAMING_SNAKE_CASE__       =      int(input('''Expected value: '''))
							SCREAMING_SNAKE_CASE__       =      int(input('''Number of propagations: '''))
							print(forward_propagation(expected, number_propagations))




