



from ...processing_utils import ProcessorMixin



class     UpperCAmelCase_       (			__a):
							lowerCamelCase__       =						'SpeechT5FeatureExtractor'
							lowerCamelCase__       =						'SpeechT5Tokenizer'







							def __init__(  self,     __a,     __a):

														'''simple docstring'''

														super().__init__(lowercase_,     lowercase_)







							def __call__(  self,     *__a,     **__a):

														'''simple docstring'''

														_lowerCAmelCase	:				Union[str, Any]					  =       kwargs.pop("audio",     lowercase_)
														_lowerCAmelCase	:				Any					  =       kwargs.pop("text",     lowercase_)
														_lowerCAmelCase	:				Dict					  =       kwargs.pop("text_target",     lowercase_)
														_lowerCAmelCase	:				Any					  =       kwargs.pop("audio_target",     lowercase_)
														_lowerCAmelCase	:				Tuple					  =       kwargs.pop("sampling_rate",     lowercase_)

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

														if audio_target is not None:
																					_lowerCAmelCase	:				Tuple					  =       self.feature_extractor(audio_target=lowercase_,     *lowercase_,     sampling_rate=lowercase_,     **lowercase_)
																					_lowerCAmelCase	:				Any					  =       targets["input_values"]
														elif text_target is not None:
																					_lowerCAmelCase	:				Optional[Any]					  =       self.tokenizer(lowercase_,     **lowercase_)
																					_lowerCAmelCase	:				Any					  =       targets["input_ids"]
														else:
																					_lowerCAmelCase	:				Optional[Any]					  =       None

														if inputs is None:
																					return targets

														if targets is not None:
																					_lowerCAmelCase	:				int					  =       labels

																					_lowerCAmelCase	:				Dict					  =       targets.get("attention_mask")
																					if decoder_attention_mask is not None:
																												_lowerCAmelCase	:				Optional[int]					  =       decoder_attention_mask

														return inputs







							def   snake_case__ (  self,     *__a,     **__a):

														'''simple docstring'''

														_lowerCAmelCase	:				Union[str, Any]					  =       kwargs.pop("input_values",     lowercase_)
														_lowerCAmelCase	:				List[Any]					  =       kwargs.pop("input_ids",     lowercase_)
														_lowerCAmelCase	:				Dict					  =       kwargs.pop("labels",     lowercase_)

														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(lowercase_,     *lowercase_,     **lowercase_)
														elif input_ids is not None:
																					_lowerCAmelCase	:				List[str]					  =       self.tokenizer.pad(lowercase_,     **lowercase_)
														else:
																					_lowerCAmelCase	:				Tuple					  =       None

														if labels is not None:
																					if "input_ids" in labels or (isinstance(lowercase_,     lowercase_) and "input_ids" in labels[0]):
																												_lowerCAmelCase	:				List[Any]					  =       self.tokenizer.pad(lowercase_,     **lowercase_)
																												_lowerCAmelCase	:				str					  =       targets["input_ids"]
																					else:
																												_lowerCAmelCase	:				Optional[Any]					  =       self.feature_extractor.feature_size
																												_lowerCAmelCase	:				Optional[Any]					  =       self.feature_extractor.num_mel_bins
																												_lowerCAmelCase	:				List[str]					  =       self.feature_extractor.pad(lowercase_,     *lowercase_,     **lowercase_)
																												_lowerCAmelCase	:				str					  =       feature_size_hack
																												_lowerCAmelCase	:				Any					  =       targets["input_values"]
														else:
																					_lowerCAmelCase	:				int					  =       None

														if inputs is None:
																					return targets

														if targets is not None:
																					_lowerCAmelCase	:				List[Any]					  =       labels

																					_lowerCAmelCase	:				List[str]					  =       targets.get("attention_mask")
																					if decoder_attention_mask is not None:
																												_lowerCAmelCase	:				List[Any]					  =       decoder_attention_mask

														return inputs







							def   snake_case__ (  self,     *__a,     **__a):

														'''simple docstring'''

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







							def   snake_case__ (  self,     *__a,     **__a):

														'''simple docstring'''

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

'''simple docstring'''


import math

def   A_      (       SCREAMING_SNAKE_CASE_						)			->int:

  if not isinstance(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						):
    lowercase_     =       f"""Input value of [number={number}] must be an integer"""
    raise TypeError(SCREAMING_SNAKE_CASE_						)

  if number < 1:
    lowercase_     =       f"""Input value of [number={number}] must be > 0"""
    raise ValueError(SCREAMING_SNAKE_CASE_						)
  elif number == 1:
    return 3
  elif number == 2:
    return 5
  else:
    lowercase_     =       int(math.log(number // 3 ,	2						)						) + 2

    lowercase_     =       [3, 5]
    lowercase_     =       2
    lowercase_     =       3
    for block in range(1 ,	SCREAMING_SNAKE_CASE_						):
      for _ in range(SCREAMING_SNAKE_CASE_						):
        proth_list.append(2 ** (block + 1) + proth_list[proth_index - 1]						)
        proth_index += 1
      increment *= 2

  return proth_list[number - 1]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       for number in range(11):
              __snake_case						  =		0
              try:
                     __snake_case						  =		proth(number)
              except ValueError:
                     print(f'''ValueError: there is no {number}th Proth number''')
                     continue

              print(f'''The {number}th Proth number: {value}''')

