




import math

class      UpperCAmelCase						:






   def __init__(	self   :       Union[str, Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       List[str]     )->			Any:
     _lowercase: Optional[int]						     =    x
     _lowercase: Tuple						     =    y
     _lowercase: Union[str, Any]						     =    step_size
     _lowercase: Optional[Any]						     =    function_to_optimize






   def  _a						(	self   :       List[Any]     )->			int:
     return self.function(self.x						,					self.y     )






   def  _a						(	self   :       Any     )->			int:
     _lowercase: str						     =    self.step_size
     return [
         SearchProblem(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					self.function     )
         for x, y in (
             (self.x - step_size, self.y - step_size),
             (self.x - step_size, self.y),
             (self.x - step_size, self.y + step_size),
             (self.x, self.y - step_size),
             (self.x, self.y + step_size),
             (self.x + step_size, self.y - step_size),
             (self.x + step_size, self.y),
             (self.x + step_size, self.y + step_size),
         )
     ]






   def __hash__(	self   :       Optional[int]     )->			Tuple:
     return hash(str(self     )     )






   def __eq__(	self   :       int						,					UpperCamelCase_   :       Union[str, Any]     )->			Optional[Any]:
     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       return hash(str(self     )     ) == hash(str(UpperCamelCase_     )     )
     return False






   def __str__(	self   :       Optional[Any]     )->			str:
     return f'x: {self.x} y: {self.y}'


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase = True					,     UpperCamelCase = math.inf					,     UpperCamelCase = -math.inf					,     UpperCamelCase = math.inf					,     UpperCamelCase = -math.inf					,     UpperCamelCase = False					,     UpperCamelCase = 10_000					,     )			->		SearchProblem:





  """simple docstring"""





  _lowercase: int						     =    search_prob
  _lowercase: Any						     =    []  # list to store the current score at each iteration
  _lowercase: Any						     =    0
  _lowercase: Optional[int]						     =    False
  _lowercase: Tuple						     =    set()
  while not solution_found and iterations < max_iter:
    visited.add(UpperCamelCase							)
    iterations += 1
    _lowercase: Any						     =    current_state.score()
    scores.append(UpperCamelCase							)
    _lowercase: str						     =    current_state.get_neighbors()
    _lowercase: Dict						     =    -math.inf
    _lowercase: Tuple						     =    math.inf
    _lowercase: Any						     =    None  # to hold the next best neighbor
    for neighbor in neighbors:
      if neighbor in visited:
        continue  # do not want to visit the same state again
      if (
          neighbor.x > max_x
          or neighbor.x < min_x
          or neighbor.y > max_y
          or neighbor.y < min_y
      ):
        continue  # neighbor outside our bounds
      _lowercase: str						     =    neighbor.score() - current_score
      if find_max:  # finding max
        # going to direction with greatest ascent
        if change > max_change and change > 0:
          _lowercase: Any						     =    change
          _lowercase: Tuple						     =    neighbor
      else:  # finding min
        # to direction with greatest descent
        if change < min_change and change < 0:
          _lowercase: Optional[int]						     =    change
          _lowercase: List[str]						     =    neighbor
    if next_state is not None:
      # we found at least one neighbor which improved the current state
      _lowercase: str						     =    next_state
    else:
      # since we have no neighbor that improves the solution we stop the search
      _lowercase: List[Any]						     =    True

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(UpperCamelCase							)					,     UpperCamelCase							)
    plt.xlabel('Iterations'							)
    plt.ylabel('Function values'							)
    plt.show()

  return current_state


if __name__ == "__main__":
   import doctest

   doctest.testmod()


   def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Any:





     """simple docstring"""





     return (x**2) + (y**2)

   # starting the problem with initial coordinates (3, 4)
   UpperCAmelCase__						:       List[str]							=    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   UpperCAmelCase__						:       Any							=    hill_climbing(prob, find_max=False)
   print(
       'The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '
       f'''{local_min.score()}'''
   )

   # starting the problem with initial coordinates (12, 47)
   UpperCAmelCase__						:       str							=    SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   UpperCAmelCase__						:       Any							=    hill_climbing(
       prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
   )
   print(
       'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
       f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
   )


   def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		str:





     """simple docstring"""





     return (3 * x**2) - (6 * y)

   UpperCAmelCase__						:       List[Any]							=    SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   UpperCAmelCase__						:       Optional[int]							=    hill_climbing(prob, find_max=True)





   print(
       'The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '
       f'''{local_min.score()}'''
   )






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"AutoFeatureExtractor"
   __SCREAMING_SNAKE_CASE		:							Optional[Any]							=					"Speech2Text2Tokenizer"






   def __init__(	self   :       Optional[Any]						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       List[Any]     )->			Dict:
     super().__init__(UpperCamelCase_						,					UpperCamelCase_     )
     _lowercase: Optional[int]						     =    self.feature_extractor
     _lowercase: int						     =    False






   def __call__(	self   :       List[Any]						,					*UpperCamelCase_   :       Dict						,					**UpperCamelCase_   :       Optional[int]     )->			str:
     # 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.'     )
       _lowercase: Tuple						     =    kwargs.pop('raw_speech'     )
     else:
       _lowercase: Optional[Any]						     =    kwargs.pop('audio'						,					UpperCamelCase_     )
     _lowercase: int						     =    kwargs.pop('sampling_rate'						,					UpperCamelCase_     )
     _lowercase: Dict						     =    kwargs.pop('text'						,					UpperCamelCase_     )
     if len(UpperCamelCase_     ) > 0:
       _lowercase: List[Any]						     =    args[0]
       _lowercase: List[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:
       _lowercase: Optional[int]						     =    self.feature_extractor(UpperCamelCase_						,					*UpperCamelCase_						,					sampling_rate=UpperCamelCase_						,					**UpperCamelCase_     )
     if text is not None:
       _lowercase: str						     =    self.tokenizer(UpperCamelCase_						,					**UpperCamelCase_     )

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






   def  _a						(	self   :       Optional[int]						,					*UpperCamelCase_   :       Tuple						,					**UpperCamelCase_   :       Dict     )->			List[str]:
     return self.tokenizer.batch_decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   def  _a						(	self   :       Tuple						,					*UpperCamelCase_   :       List[Any]						,					**UpperCamelCase_   :       Union[str, Any]     )->			Optional[int]:
     return self.tokenizer.decode(*UpperCamelCase_						,					**UpperCamelCase_     )






   @contextmanager
   def  _a						(	self   :       Tuple     )->			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.'     )
     _lowercase: Any						     =    True
     _lowercase: Optional[Any]						     =    self.tokenizer
     yield
     _lowercase: Union[str, Any]						     =    self.feature_extractor
     _lowercase: str						     =    False
