



import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin



class 			__lowerCAmelCase  (						lowerCAmelCase__ ):
 lowerCAmelCase__							       =	"""MCTCTFeatureExtractor"""
 lowerCAmelCase__							       =	"""AutoTokenizer"""




 def __init__(    self						,					__UpperCAmelCase						,					__UpperCAmelCase					):



    '''simple docstring'''
    super().__init__(__UpperCAmelCase						,					__UpperCAmelCase					)
    __lowerCamelCase    				=     self.feature_extractor
    __lowerCamelCase    				=     False




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



    '''simple docstring'''
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor(*__UpperCAmelCase						,					**__UpperCAmelCase					)

    if "raw_speech" in kwargs:
       warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''					)
       __lowerCamelCase    				=     kwargs.pop('''raw_speech'''					)
    else:
       __lowerCamelCase    				=     kwargs.pop('''audio'''						,					__UpperCAmelCase					)
    __lowerCamelCase    				=     kwargs.pop('''sampling_rate'''						,					__UpperCAmelCase					)
    __lowerCamelCase    				=     kwargs.pop('''text'''						,					__UpperCAmelCase					)
    if len(__UpperCAmelCase					) > 0:
       __lowerCamelCase    				=     args[0]
       __lowerCamelCase    				=     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:
       __lowerCamelCase    				=     self.feature_extractor(__UpperCAmelCase						,					*__UpperCAmelCase						,					sampling_rate=__UpperCAmelCase						,					**__UpperCAmelCase					)
    if text is not None:
       __lowerCamelCase    				=     self.tokenizer(__UpperCAmelCase						,					**__UpperCAmelCase					)

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




 def 							lowerCamelCase   (    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    return self.tokenizer.batch_decode(*__UpperCAmelCase						,					**__UpperCAmelCase					)




 def 							lowerCamelCase   (    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    # For backward compatibility
    if self._in_target_context_manager:
       return self.current_processor.pad(*__UpperCAmelCase						,					**__UpperCAmelCase					)

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

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

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




 def 							lowerCamelCase   (    self						,					*__UpperCAmelCase						,					**__UpperCAmelCase					):



    '''simple docstring'''
    return self.tokenizer.decode(*__UpperCAmelCase						,					**__UpperCAmelCase					)




 @contextmanager
 def 							lowerCamelCase   (    self					):



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











from __future__ import annotations

from math import ceil, floor, sqrt





def 		a__      (		_UpperCamelCase      :			int = 2_00_00_00				):
   __lowerCamelCase    				=     [0]
   __lowerCamelCase    				=     42

   for idx in range(1			,ceil(sqrt(target * 2				) * 1.1				)				):
      triangle_numbers.append(triangle_numbers[-1] + idx				)

   # we want this to be as close as possible to target
   __lowerCamelCase    				=     0
   # the area corresponding to the grid that gives the product closest to target
   __lowerCamelCase    				=     0
   # an estimate of b, using the quadratic formula
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the largest integer less than b_estimate
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_floor
   __lowerCamelCase    				=     42
   # the triangle number corresponding to b_ceil
   __lowerCamelCase    				=     42

   for idx_a, triangle_a in enumerate(triangle_numbers[1:]			,1				):
      __lowerCamelCase    				=     (-1 + sqrt(1 + 8 * target / triangle_a				)) / 2
      __lowerCamelCase    				=     floor(_UpperCamelCase				)
      __lowerCamelCase    				=     ceil(_UpperCamelCase				)
      __lowerCamelCase    				=     triangle_numbers[b_floor]
      __lowerCamelCase    				=     triangle_numbers[b_ceil]

      if abs(target - triangle_b_first_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_first_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_floor

      if abs(target - triangle_b_second_guess * triangle_a				) < abs(
          target - best_product				):
         __lowerCamelCase    				=     triangle_b_second_guess * triangle_a
         __lowerCamelCase    				=     idx_a * b_ceil

   return area


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






