
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer

class    _a				(_A):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  'Wav2Vec2FeatureExtractor'
       SCREAMING_SNAKE_CASE      =  'AutoTokenizer'






       def __init__(							self       ,							A__       ,							A__      )    ->			Optional[int]:
          super().__init__(__lowerCamelCase       ,							__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				self.feature_extractor
          _SCREAMING_SNAKE_CASE      =				False






       @classmethod
       def        UpperCamelCase				(							cls       ,							A__       ,							**A__      )    ->			str:
          try:
             return super().from_pretrained(__lowerCamelCase       ,							**__lowerCamelCase      )
          except OSError:
             warnings.warn(
                 F"Loading a tokenizer inside {cls.__name__} from a config that does not"
                 """ include a `tokenizer_class` attribute is deprecated and will be """
                 """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
                 """ attribute to either your `config.json` or `tokenizer_config.json` """
                 """file to suppress this warning: """       ,							__lowerCamelCase       ,							)

             _SCREAMING_SNAKE_CASE      =				WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase       ,							**__lowerCamelCase      )
             _SCREAMING_SNAKE_CASE      =				WavaVecaCTCTokenizer.from_pretrained(__lowerCamelCase       ,							**__lowerCamelCase      )

             return cls(feature_extractor=__lowerCamelCase       ,							tokenizer=__lowerCamelCase      )






       def __call__(							self       ,							*A__       ,							**A__      )    ->			List[Any]:
          # For backward compatibility
          if self._in_target_context_manager:
             return self.current_processor(*__lowerCamelCase       ,							**__lowerCamelCase      )

          if "raw_speech" in kwargs:
             warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""      )
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""raw_speech"""      )
          else:
             _SCREAMING_SNAKE_CASE      =				kwargs.pop("""audio"""       ,							__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				kwargs.pop("""sampling_rate"""       ,							__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				kwargs.pop("""text"""       ,							__lowerCamelCase      )
          if len(__lowerCamelCase      ) > 0:
             _SCREAMING_SNAKE_CASE      =				args[0]
             _SCREAMING_SNAKE_CASE      =				args[1:]

          if audio is None and text is None:
             raise ValueError("""You need to specify either an `audio` or `text` input to process."""      )

          if audio is not None:
             _SCREAMING_SNAKE_CASE      =				self.feature_extractor(__lowerCamelCase       ,							*__lowerCamelCase       ,							sampling_rate=__lowerCamelCase       ,							**__lowerCamelCase      )
          if text is not None:
             _SCREAMING_SNAKE_CASE      =				self.tokenizer(__lowerCamelCase       ,							**__lowerCamelCase      )

          if text is None:
             return inputs
          elif audio is None:
             return encodings
          else:
             _SCREAMING_SNAKE_CASE      =				encodings["input_ids"]
             return inputs






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			str:
          # For backward compatibility
          if self._in_target_context_manager:
             return self.current_processor.pad(*__lowerCamelCase       ,							**__lowerCamelCase      )

          _SCREAMING_SNAKE_CASE      =				kwargs.pop("""input_features"""       ,							__lowerCamelCase      )
          _SCREAMING_SNAKE_CASE      =				kwargs.pop("""labels"""       ,							__lowerCamelCase      )
          if len(__lowerCamelCase      ) > 0:
             _SCREAMING_SNAKE_CASE      =				args[0]
             _SCREAMING_SNAKE_CASE      =				args[1:]

          if input_features is not None:
             _SCREAMING_SNAKE_CASE      =				self.feature_extractor.pad(__lowerCamelCase       ,							*__lowerCamelCase       ,							**__lowerCamelCase      )
          if labels is not None:
             _SCREAMING_SNAKE_CASE      =				self.tokenizer.pad(__lowerCamelCase       ,							**__lowerCamelCase      )

          if labels is None:
             return input_features
          elif input_features is None:
             return labels
          else:
             _SCREAMING_SNAKE_CASE      =				labels["input_ids"]
             return input_features






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			List[Any]:
          return self.tokenizer.batch_decode(*__lowerCamelCase       ,							**__lowerCamelCase      )






       def        UpperCamelCase				(							self       ,							*A__       ,							**A__      )    ->			List[Any]:
          return self.tokenizer.decode(*__lowerCamelCase       ,							**__lowerCamelCase      )






       @contextmanager
       def        UpperCamelCase				(							self      )    ->			Union[str, Any]:
          warnings.warn(
              """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
              """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
              """your audio inputs, or in a separate call."""      )
          _SCREAMING_SNAKE_CASE      =				True
          _SCREAMING_SNAKE_CASE      =				self.tokenizer
          yield
          _SCREAMING_SNAKE_CASE      =				self.feature_extractor
          _SCREAMING_SNAKE_CASE      =				False








'''simple docstring'''



import math
from collections.abc import Iterator
from itertools import takewhile
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_						)     ->     bool:




   """simple docstring"""

   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    lowerCAmelCase_	(				)     ->     Iterator[int]:




   """simple docstring"""
   _SCREAMING_SNAKE_CASE      =				2
   while True:
      if is_prime(SCREAMING_SNAKE_CASE_						):
         yield num
      num += 1
def    lowerCAmelCase_	(				SCREAMING_SNAKE_CASE_ = 2_00_00_00						)     ->     int:




   """simple docstring"""
   return sum(takewhile(lambda SCREAMING_SNAKE_CASE_						: x < n    ,      prime_generator()						)						)


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






