





import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Dict				      = 'Wav2Vec2FeatureExtractor'
   _UpperCamelCase					:	Any				      = 'AutoTokenizer'


   def __init__(					self    :       str    ,  a    :       Dict    ,  a    :       List[str] )->     Tuple:





     """simple docstring"""







     super().__init__(a    ,  a )
     lowercase__			    =						self.feature_extractor
     lowercase__			    =						False


   @classmethod
   def  SCREAMING_SNAKE_CASE_							(					cls    :       int    ,  a    :       List[Any]    ,  **a    :       int )->     Dict:





     """simple docstring"""







     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    ,  )

       lowercase__			    =						WavaVecaFeatureExtractor.from_pretrained(a    ,  **a )
       lowercase__			    =						WavaVecaCTCTokenizer.from_pretrained(a    ,  **a )

       return cls(feature_extractor=a    ,  tokenizer=a )


   def __call__(					self    :       Dict    ,  *a    :       Optional[Any]    ,  **a    :       Optional[Any] )->     List[Any]:





     """simple docstring"""







     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.' )
       lowercase__			    =						kwargs.pop('raw_speech' )
     else:
       lowercase__			    =						kwargs.pop('audio'    ,  a )
     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )
     lowercase__			    =						kwargs.pop('text'    ,  a )
     if len(a ) > 0:
       lowercase__			    =						args[0]
       lowercase__			    =						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:
       lowercase__			    =						self.feature_extractor(a    ,  *a    ,  sampling_rate=a    ,  **a )
     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  **a )

     if text is None:
       return inputs
     elif audio is None:
       return encodings
     else:
       lowercase__			    =						encodings['input_ids']
       return inputs


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  *a    :       Tuple    ,  **a    :       Any )->     Optional[int]:





     """simple docstring"""







     if self._in_target_context_manager:
       return self.current_processor.pad(*a    ,  **a )

     lowercase__			    =						kwargs.pop('input_features'    ,  a )
     lowercase__			    =						kwargs.pop('labels'    ,  a )
     if len(a ) > 0:
       lowercase__			    =						args[0]
       lowercase__			    =						args[1:]

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

     if labels is None:
       return input_features
     elif input_features is None:
       return labels
     else:
       lowercase__			    =						labels['input_ids']
       return input_features


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any    ,  *a    :       Optional[Any]    ,  **a    :       Optional[Any] )->     Any:





     """simple docstring"""







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


   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any]    ,  *a    :       str    ,  **a    :       str )->     int:





     """simple docstring"""







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


   @contextmanager
   def  SCREAMING_SNAKE_CASE_							(					self    :       Dict )->     Any:





     """simple docstring"""







     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.' )
     lowercase__			    =						True
     lowercase__			    =						self.tokenizer
     yield
     lowercase__			    =						self.feature_extractor
     lowercase__			    =						False









from __future__ import annotations

import math
from collections.abc import Callable




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = 100	,		)       ->     float:
  lowercase__			    =						x_start
  lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
  lowercase__			    =						0.0

  for _ in range(_SCREAMING_SNAKE_CASE	):
    # Approximates curve as a sequence of linear lines and sums their length
    lowercase__			    =						(x_end - x_start) / steps + xa
    lowercase__			    =						fnc(_SCREAMING_SNAKE_CASE	)
    length += math.hypot(xa - xa	,		fxa - fxa	)

    # Increment step
    lowercase__			    =						xa
    lowercase__			    =						fxa

  return length


if __name__ == "__main__":




      def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     str:
        return math.sin(10 * x	)

      print("""f(x) = sin(10 * x)""")
      print("""The length of the curve from x = -10 to x = 10 is:""")
      lowercase_					     =	10
      while i <= 100_000:
            print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
            i *= 10


