'''simple docstring'''




from ...processing_utils import ProcessorMixin


class       UpperCAmelCase__  (       _snake_case			):






   """simple docstring"""
   A							:       str     =    '''SpeechT5FeatureExtractor'''
   A							:       Optional[Any]     =    '''SpeechT5Tokenizer'''
   def __init__(self       ,				_a       ,				_a				)    ->     Dict:
        super().__init__(_UpperCAmelCase       ,				_UpperCAmelCase				)
   def __call__(self       ,				*_a       ,				**_a				)    ->     Optional[int]:
        lowercase_					:       Union[str, Any]     				=					kwargs.pop('audio'       ,				_UpperCAmelCase				)
        lowercase_					:       Dict     				=					kwargs.pop('text'       ,				_UpperCAmelCase				)
        lowercase_					:       int     				=					kwargs.pop('text_target'       ,				_UpperCAmelCase				)
        lowercase_					:       List[str]     				=					kwargs.pop('audio_target'       ,				_UpperCAmelCase				)
        lowercase_					:       List[Any]     				=					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:
             lowercase_					:       Tuple     				=					self.feature_extractor(_UpperCAmelCase       ,				*_UpperCAmelCase       ,				sampling_rate=_UpperCAmelCase       ,				**_UpperCAmelCase				)
        elif text is not None:
             lowercase_					:       List[Any]     				=					self.tokenizer(_UpperCAmelCase       ,				**_UpperCAmelCase				)
        else:
             lowercase_					:       Any     				=					None

        if audio_target is not None:
             lowercase_					:       Union[str, Any]     				=					self.feature_extractor(audio_target=_UpperCAmelCase       ,				*_UpperCAmelCase       ,				sampling_rate=_UpperCAmelCase       ,				**_UpperCAmelCase				)
             lowercase_					:       Optional[Any]     				=					targets['input_values']
        elif text_target is not None:
             lowercase_					:       Dict     				=					self.tokenizer(_UpperCAmelCase       ,				**_UpperCAmelCase				)
             lowercase_					:       List[Any]     				=					targets['input_ids']
        else:
             lowercase_					:       Dict     				=					None

        if inputs is None:
             return targets

        if targets is not None:
             lowercase_					:       str     				=					labels

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

        return inputs
   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     List[str]:
        lowercase_					:       str     				=					kwargs.pop('input_values'       ,				_UpperCAmelCase				)
        lowercase_					:       Tuple     				=					kwargs.pop('input_ids'       ,				_UpperCAmelCase				)
        lowercase_					:       Optional[Any]     				=					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:
             lowercase_					:       str     				=					self.feature_extractor.pad(_UpperCAmelCase       ,				*_UpperCAmelCase       ,				**_UpperCAmelCase				)
        elif input_ids is not None:
             lowercase_					:       int     				=					self.tokenizer.pad(_UpperCAmelCase       ,				**_UpperCAmelCase				)
        else:
             lowercase_					:       int     				=					None

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

        if inputs is None:
             return targets

        if targets is not None:
             lowercase_					:       List[str]     				=					labels

             lowercase_					:       Tuple     				=					targets.get('attention_mask'				)
             if decoder_attention_mask is not None:
                  lowercase_					:       Dict     				=					decoder_attention_mask

        return inputs
   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     str:
        return self.tokenizer.batch_decode(*_UpperCAmelCase       ,				**_UpperCAmelCase				)





   def 		_lowerCamelCase (self       ,				*_a       ,				**_a				)    ->     List[str]:
        return self.tokenizer.decode(*_UpperCAmelCase       ,				**_UpperCAmelCase				)

'''simple docstring'''




import math

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_							):

     if 1 < number < 4:
          # 2 and 3 are primes
          return True
     elif number < 2 or number % 2 == 0 or number % 3 == 0:
          # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
          return False

     # All primes number are in format of 6k +/- 1
     for i in range(5  ,       int(math.sqrt(SCREAMING_SNAKE_CASE_							) + 1							)  ,       6							):
          if number % i == 0 or number % (i + 2) == 0:
               return False
     return True

def _UpperCamelCase   (       SCREAMING_SNAKE_CASE_ = 10_001							):

     try:
          lowercase_					:       List[Any]     				=					int(SCREAMING_SNAKE_CASE_							)
     except (TypeError, ValueError):
          raise TypeError('Parameter nth must be int or castable to int.'							) from None
     if nth <= 0:
          raise ValueError('Parameter nth must be greater than or equal to one.'							)
     lowercase_					:       list[int]     				=					[]
     lowercase_					:       Optional[int]     				=					2
     while len(SCREAMING_SNAKE_CASE_							) < nth:
          if is_prime(SCREAMING_SNAKE_CASE_							):
               primes.append(SCREAMING_SNAKE_CASE_							)
               num += 1
          else:
               num += 1
     return primes[len(SCREAMING_SNAKE_CASE_							) - 1]


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




