



from ...processing_utils import ProcessorMixin



class 			lowerCamelCase__     (				__lowerCamelCase):




		'''simple docstring'''



		snake_case_      ='SpeechT5FeatureExtractor'
		snake_case_      ='SpeechT5Tokenizer'



		def __init__(self						,__lowerCamelCase						,__lowerCamelCase					)	->     Optional[Any]:

						"""simple docstring"""






						super().__init__(lowerCamelCase__						,lowerCamelCase__					)



		def __call__(self						,*__lowerCamelCase						,**__lowerCamelCase					)	->     Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__	:							Union[str, Any]				=	kwargs.pop('''audio'''						,lowerCamelCase__					)
						lowerCAmelCase__	:							List[str]				=	kwargs.pop('''text'''						,lowerCamelCase__					)
						lowerCAmelCase__	:							Optional[Any]				=	kwargs.pop('''text_target'''						,lowerCamelCase__					)
						lowerCAmelCase__	:							Any				=	kwargs.pop('''audio_target'''						,lowerCamelCase__					)
						lowerCAmelCase__	:							str				=	kwargs.pop('''sampling_rate'''						,lowerCamelCase__					)

						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:
										lowerCAmelCase__	:							Dict				=	self.feature_extractor(lowerCamelCase__						,*lowerCamelCase__						,sampling_rate=lowerCamelCase__						,**lowerCamelCase__					)
						elif text is not None:
										lowerCAmelCase__	:							int				=	self.tokenizer(lowerCamelCase__						,**lowerCamelCase__					)
						else:
										lowerCAmelCase__	:							List[str]				=	None

						if audio_target is not None:
										lowerCAmelCase__	:							Union[str, Any]				=	self.feature_extractor(audio_target=lowerCamelCase__						,*lowerCamelCase__						,sampling_rate=lowerCamelCase__						,**lowerCamelCase__					)
										lowerCAmelCase__	:							int				=	targets['''input_values''']
						elif text_target is not None:
										lowerCAmelCase__	:							Tuple				=	self.tokenizer(lowerCamelCase__						,**lowerCamelCase__					)
										lowerCAmelCase__	:							Dict				=	targets['''input_ids''']
						else:
										lowerCAmelCase__	:							Tuple				=	None

						if inputs is None:
										return targets

						if targets is not None:
										lowerCAmelCase__	:							str				=	labels

										lowerCAmelCase__	:							str				=	targets.get('''attention_mask'''					)
										if decoder_attention_mask is not None:
														lowerCAmelCase__	:							List[str]				=	decoder_attention_mask

						return inputs



		def 						lowerCAmelCase__ (self						,*__lowerCamelCase						,**__lowerCamelCase					)	->     Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__	:							Optional[Any]				=	kwargs.pop('''input_values'''						,lowerCamelCase__					)
						lowerCAmelCase__	:							int				=	kwargs.pop('''input_ids'''						,lowerCamelCase__					)
						lowerCAmelCase__	:							Dict				=	kwargs.pop('''labels'''						,lowerCamelCase__					)

						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:
										lowerCAmelCase__	:							Union[str, Any]				=	self.feature_extractor.pad(lowerCamelCase__						,*lowerCamelCase__						,**lowerCamelCase__					)
						elif input_ids is not None:
										lowerCAmelCase__	:							Dict				=	self.tokenizer.pad(lowerCamelCase__						,**lowerCamelCase__					)
						else:
										lowerCAmelCase__	:							Dict				=	None

						if labels is not None:
										if "input_ids" in labels or (isinstance(lowerCamelCase__						,lowerCamelCase__					) and "input_ids" in labels[0]):
														lowerCAmelCase__	:							Any				=	self.tokenizer.pad(lowerCamelCase__						,**lowerCamelCase__					)
														lowerCAmelCase__	:							str				=	targets['''input_ids''']
										else:
														lowerCAmelCase__	:							Optional[int]				=	self.feature_extractor.feature_size
														lowerCAmelCase__	:							Optional[Any]				=	self.feature_extractor.num_mel_bins
														lowerCAmelCase__	:							Dict				=	self.feature_extractor.pad(lowerCamelCase__						,*lowerCamelCase__						,**lowerCamelCase__					)
														lowerCAmelCase__	:							Union[str, Any]				=	feature_size_hack
														lowerCAmelCase__	:							Optional[int]				=	targets['''input_values''']
						else:
										lowerCAmelCase__	:							Tuple				=	None

						if inputs is None:
										return targets

						if targets is not None:
										lowerCAmelCase__	:							Tuple				=	labels

										lowerCAmelCase__	:							int				=	targets.get('''attention_mask'''					)
										if decoder_attention_mask is not None:
														lowerCAmelCase__	:							Tuple				=	decoder_attention_mask

						return inputs



		def 						lowerCAmelCase__ (self						,*__lowerCamelCase						,**__lowerCamelCase					)	->     List[Any]:

						"""simple docstring"""






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



		def 						lowerCAmelCase__ (self						,*__lowerCamelCase						,**__lowerCamelCase					)	->     Optional[int]:

						"""simple docstring"""






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




from itertools import product







def 							_lowerCamelCase   (			SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE   ):
      '''simple docstring'''







      A_							       =		sides_number
      A_							       =		max_face_number * dice_number
      A_							       =		[0] * (max_total + 1)

      A_							       =		1
      A_							       =		range(SCREAMING_SNAKE_CASE		,				max_face_number + 1   )
      for dice_numbers in product(SCREAMING_SNAKE_CASE		,				repeat=SCREAMING_SNAKE_CASE   ):
            A_							       =		sum(SCREAMING_SNAKE_CASE   )
            totals_frequencies[total] += 1

      return totals_frequencies







def 							_lowerCamelCase   (			):
      '''simple docstring'''







      A_							       =		total_frequency_distribution(
          sides_number=4		,				dice_number=9   )
      A_							       =		total_frequency_distribution(
          sides_number=6		,				dice_number=6   )

      A_							       =		0
      A_							       =		9
      A_							       =		4 * 9
      A_							       =		6
      for peter_total in range(SCREAMING_SNAKE_CASE		,				max_peter_total + 1   ):
            peter_wins_count += peter_totals_frequencies[peter_total] * sum(
                colin_totals_frequencies[min_colin_total:peter_total]   )

      A_							       =		(4**9) * (6**6)
      A_							       =		peter_wins_count / total_games_number

      A_							       =		round(SCREAMING_SNAKE_CASE		,				ndigits=7   )

      return rounded_peter_win_probability


if __name__ == "__main__":
      print(f'{solution() = }')





