




import warnings
from contextlib import contextmanager

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







class 		lowercase							(     UpperCamelCase__	):
     _a											=		"Wav2Vec2FeatureExtractor"
     _a											=		"AutoTokenizer"

     def __init__(						self	,  _a	,  _a       )		->							List[str]:
           super().__init__(_a	,  _a       )
           _A							:  int    =		self.feature_extractor
           _A							:  Optional[int]    =		False

     @classmethod
     def       a__			(						cls	,  _a	,  **_a       )		->							str:
           try:
                 return super().from_pretrained(_a	,  **_a       )
           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: """	,  _a	,  )

                 _A							:  Optional[Any]    =		WavaVecaFeatureExtractor.from_pretrained(_a	,  **_a       )
                 _A							:  str    =		WavaVecaCTCTokenizer.from_pretrained(_a	,  **_a       )

                 return cls(feature_extractor=_a	,  tokenizer=_a       )

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

           if "raw_speech" in kwargs:
                 warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""       )
                 _A							:  Dict    =		kwargs.pop("""raw_speech"""       )
           else:
                 _A							:  int    =		kwargs.pop("""audio"""	,  _a       )
           _A							:  str    =		kwargs.pop("""sampling_rate"""	,  _a       )
           _A							:  Tuple    =		kwargs.pop("""text"""	,  _a       )
           if len(_a       ) > 0:
                 _A							:  Dict    =		args[0]
                 _A							:  Optional[Any]    =		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:
                 _A							:  Optional[Any]    =		self.feature_extractor(_a	,  *_a	,  sampling_rate=_a	,  **_a       )
           if text is not None:
                 _A							:  str    =		self.tokenizer(_a	,  **_a       )

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

     def       a__			(						self	,  *_a	,  **_a       )		->							Dict:
           # For backward compatibility
           if self._in_target_context_manager:
                 return self.current_processor.pad(*_a	,  **_a       )

           _A							:  Optional[Any]    =		kwargs.pop("""input_features"""	,  _a       )
           _A							:  Dict    =		kwargs.pop("""labels"""	,  _a       )
           if len(_a       ) > 0:
                 _A							:  Union[str, Any]    =		args[0]
                 _A							:  List[Any]    =		args[1:]

           if input_features is not None:
                 _A							:  Dict    =		self.feature_extractor.pad(_a	,  *_a	,  **_a       )
           if labels is not None:
                 _A							:  List[Any]    =		self.tokenizer.pad(_a	,  **_a       )

           if labels is None:
                 return input_features
           elif input_features is None:
                 return labels
           else:
                 _A							:  List[str]    =		labels["""input_ids"""]
                 return input_features

     def       a__			(						self	,  *_a	,  **_a       )		->							Optional[Any]:
           return self.tokenizer.batch_decode(*_a	,  **_a       )

     def       a__			(						self	,  *_a	,  **_a       )		->							Any:
           return self.tokenizer.decode(*_a	,  **_a       )



     @contextmanager
     def       a__			(						self       )		->							Optional[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."""       )
           _A							:  List[str]    =		True
           _A							:  Optional[int]    =		self.tokenizer
           yield
           _A							:  List[str]    =		self.feature_extractor
           _A							:  str    =		False







from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean







def        lowerCAmelCase_			(  snake_case_  ):

      # A local function to see if a dot lands in the circle.
      def is_in_circle(snake_case_,snake_case_  ) -> bool:
            _A							:  List[str]    =		sqrt((x**2) + (y**2)  )
            # Our circle has a radius of 1, so a distance
            # greater than 1 would land outside the circle.
            return distance_from_centre <= 1

      # The proportion of guesses that landed in the circle
      _A							:  Optional[int]    =		mean(
          int(is_in_circle(uniform(-1.0,1.0  ),uniform(-1.0,1.0  )  )  )
          for _ in range(snake_case_  )  )
      # The ratio of the area for circle to square is pi/4.
      _A							:  List[str]    =		proportion * 4
      print(f'''The estimated value of pi is {pi_estimate}'''  )
      print(f'''The numpy value of pi is {pi}'''  )
      print(f'''The total error is {abs(pi - pi_estimate  )}'''  )







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_ = 0.0,snake_case_ = 1.0,):
      return mean(
          function_to_integrate(uniform(snake_case_,snake_case_  )  ) for _ in range(snake_case_  )  ) * (max_value - min_value)







def        lowerCAmelCase_			(  snake_case_,snake_case_ = 0.0,snake_case_ = 1.0  ):

      def identity_function(snake_case_  ) -> float:
            return x

      _A							:  Any    =		area_under_curve_estimator(
          snake_case_,snake_case_,snake_case_,snake_case_  )
      _A							:  Tuple    =		(max_value * max_value - min_value * min_value) / 2

      print("""******************"""  )
      print(f'''Estimating area under y=x where x varies from {min_value} to {max_value}'''  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {expected_value}'''  )
      print(f'''Total error is {abs(estimated_value - expected_value  )}'''  )
      print("""******************"""  )







def        lowerCAmelCase_			(  snake_case_  ):

      def function_to_integrate(snake_case_  ) -> float:
            return sqrt(4.0 - x * x  )

      _A							:  Optional[int]    =		area_under_curve_estimator(
          snake_case_,snake_case_,0.0,2.0  )

      print("""******************"""  )
      print("""Estimating pi using area_under_curve_estimator"""  )
      print(f'''Estimated value is {estimated_value}'''  )
      print(f'''Expected value is {pi}'''  )
      print(f'''Total error is {abs(estimated_value - pi  )}'''  )
      print("""******************"""  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

