






import math


class 			__SCREAMING_SNAKE_CASE    :






						def __init__(		self   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase   ,  _UpperCamelCase      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  x
												lowerCAmelCase__			  =  y
												lowerCAmelCase__			  =  step_size
												lowerCAmelCase__			  =  function_to_optimize






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												return self.function(self.x   ,  self.y      )






						def 					UpperCamelCase__					(		self      ):






												"""simple docstring"""
												lowerCAmelCase__			  =  self.step_size
												return [
												    SearchProblem(a__   ,  a__   ,  a__   ,  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      ):






												"""simple docstring"""
												return hash(str(self      )      )






						def __eq__(		self   ,  _UpperCamelCase      ):






												"""simple docstring"""







												if isinstance(a__   ,  a__      ):
																		return hash(str(self      )      ) == hash(str(a__      )      )
												return False







						def __str__(		self      ):






												"""simple docstring"""
												return F"x: {self.x} y: {self.y}"







def 		_UpperCamelCase      (			UpperCamelCase_				:    List[Any]			,				UpperCamelCase_				:    Optional[Any] = True			,				UpperCamelCase_				:    Optional[int] = math.inf			,				UpperCamelCase_				:    Union[str, Any] = -math.inf			,				UpperCamelCase_				:    Any = math.inf			,				UpperCamelCase_				:    Dict = -math.inf			,				UpperCamelCase_				:    List[Any] = False			,				UpperCamelCase_				:    List[str] = 1_0000			,				)     ->      SearchProblem:

						"""simple docstring"""



						lowerCAmelCase__			  =  search_prob
						lowerCAmelCase__			  =  []  # list to store the current score at each iteration
						lowerCAmelCase__			  =  0
						lowerCAmelCase__			  =  False
						lowerCAmelCase__			  =  set()
						while not solution_found and iterations < max_iter:
												visited.add(__lowercase						)
												iterations += 1
												lowerCAmelCase__			  =  current_state.score()
												scores.append(__lowercase						)
												lowerCAmelCase__			  =  current_state.get_neighbors()
												lowerCAmelCase__			  =  -math.inf
												lowerCAmelCase__			  =  math.inf
												lowerCAmelCase__			  =  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
																		lowerCAmelCase__			  =  neighbor.score() - current_score
																		if find_max:  # finding max
																								# going to direction with greatest ascent
																								if change > max_change and change > 0:
																														lowerCAmelCase__			  =  change
																														lowerCAmelCase__			  =  neighbor
																		else:  # finding min
																								# to direction with greatest descent
																								if change < min_change and change < 0:
																														lowerCAmelCase__			  =  change
																														lowerCAmelCase__			  =  neighbor
												if next_state is not None:
																		# we found at least one neighbor which improved the current state
																		lowerCAmelCase__			  =  next_state
												else:
																		# since we have no neighbor that improves the solution we stop the search
																		lowerCAmelCase__			  =  True

						if visualization:
												from matplotlib import pyplot as plt

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

						return current_state


if __name__ == "__main__":
					import doctest

					doctest.testmod()







					def 		_UpperCamelCase      (			UpperCamelCase_				:    Union[str, Any]			,				UpperCamelCase_				:    Any						)     ->      List[Any]:

											"""simple docstring"""



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

					# starting the problem with initial coordinates (3, 4)
					__snake_case			:			Optional[int]			   =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					__snake_case			:			Tuple			   =   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)
					__snake_case			:			List[Any]			   =   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					__snake_case			:			str			   =   hill_climbing(
					    prob, find_max=False, max_x=1_00, 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 		_UpperCamelCase      (			UpperCamelCase_				:    Optional[Any]			,				UpperCamelCase_				:    Tuple						)     ->      str:

											"""simple docstring"""



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

					__snake_case			:			int			   =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					__snake_case			:			Dict			   =   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()}'
					)









"""simple docstring"""


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	'AutoFeatureExtractor'
		__UpperCamelCase							=	'Speech2Text2Tokenizer'







		def __init__(    self					:				int			,      a__					:				Tuple			,      a__					:				Optional[Any]						)				->  str:





						'''simple docstring'''







						super().__init__(a__			,      a__						)
						_A         =	self.feature_extractor
						_A         =	False







		def __call__(    self					:				Optional[int]			,      *a__					:				List[str]			,      **a__					:				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."						)
										_A         =	kwargs.pop("raw_speech"						)
						else:
										_A         =	kwargs.pop("audio"			,      a__						)
						_A         =	kwargs.pop("sampling_rate"			,      a__						)
						_A         =	kwargs.pop("text"			,      a__						)
						if len(a__						) > 0:
										_A         =	args[0]
										_A         =	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         =	self.feature_extractor(a__			,      *a__			,      sampling_rate=a__			,      **a__						)
						if text is not None:
										_A         =	self.tokenizer(a__			,      **a__						)

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







		def        a_       (    self					:				List[Any]			,      *a__					:				Tuple			,      **a__					:				List[Any]						)				->  List[Any]:





						'''simple docstring'''







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







		def        a_       (    self					:				Any			,      *a__					:				Dict			,      **a__					:				Tuple						)				->  Union[str, Any]:





						'''simple docstring'''







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







		@contextmanager
		def        a_       (    self					:				Optional[int]						)				->  Optional[int]:





						'''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."						)
						_A         =	True
						_A         =	self.tokenizer
						yield
						_A         =	self.feature_extractor
						_A         =	False