

import math





class      snake_case_       :







			'''simple docstring'''

			def __init__(					self			:    str							,       _UpperCamelCase			:    int							,       _UpperCamelCase			:    Union[str, Any]							,       _UpperCamelCase			:    Dict							,       _UpperCamelCase			:    Any							)				->Union[str, Any]:
									snake_case_       =     x
									snake_case_       =     y
									snake_case_       =     step_size
									snake_case_       =     function_to_optimize







			def     snake_case__(					self			:    Any							)				->int:
									return self.function(self.x							,       self.y							)







			def     snake_case__(					self			:    Optional[Any]							)				->Dict:
									snake_case_       =     self.step_size
									return [
									    SearchProblem(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       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			:    List[Any]							)				->List[Any]:
									return hash(str(self							)							)







			def __eq__(					self			:    Tuple							,       _UpperCamelCase			:    int							)				->Dict:






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







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






def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__ = True  ,		SCREAMING_SNAKE_CASE__ = math.inf  ,		SCREAMING_SNAKE_CASE__ = -math.inf  ,		SCREAMING_SNAKE_CASE__ = math.inf  ,		SCREAMING_SNAKE_CASE__ = -math.inf  ,		SCREAMING_SNAKE_CASE__ = False  ,		SCREAMING_SNAKE_CASE__ = 10000  ,		):
						snake_case_       =     search_prob
						snake_case_       =     []  # list to store the current score at each iteration
						snake_case_       =     0
						snake_case_       =     False
						snake_case_       =     set()
						while not solution_found and iterations < max_iter:
												visited.add(SCREAMING_SNAKE_CASE__	)
												iterations += 1
												snake_case_       =     current_state.score()
												scores.append(SCREAMING_SNAKE_CASE__	)
												snake_case_       =     current_state.get_neighbors()
												snake_case_       =     -math.inf
												snake_case_       =     math.inf
												snake_case_       =     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
																		snake_case_       =     neighbor.score() - current_score
																		if find_max:  # finding max
																								# going to direction with greatest ascent
																								if change > max_change and change > 0:
																														snake_case_       =     change
																														snake_case_       =     neighbor
																		else:  # finding min
																								# to direction with greatest descent
																								if change < min_change and change < 0:
																														snake_case_       =     change
																														snake_case_       =     neighbor
												if next_state is not None:
																		# we found at least one neighbor which improved the current state
																		snake_case_       =     next_state
												else:
																		# since we have no neighbor that improves the solution we stop the search
																		snake_case_       =     True

						if visualization:
												from matplotlib import pyplot as plt

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

						return current_state


if __name__ == "__main__":
				import doctest

				doctest.testmod()


				def   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
										return (x**2) + (y**2)

				# starting the problem with initial coordinates (3, 4)
				lowerCAmelCase_	     =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				lowerCAmelCase_	     =     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)
				lowerCAmelCase_	     =     SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
				lowerCAmelCase_	     =     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   __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__	):
										return (3 * x**2) - (6 * y)

				lowerCAmelCase_	     =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				lowerCAmelCase_	     =     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      snake_case_       (		__A ):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE					:       List[str]						      =    ["image_processor", "tokenizer"]
			SCREAMING_SNAKE_CASE					:       List[Any]						      =    "AutoImageProcessor"
			SCREAMING_SNAKE_CASE					:       Optional[int]						      =    "AutoTokenizer"







			def __init__(					self			:    List[str]							,       _UpperCamelCase			:    List[str]=None							,       _UpperCamelCase			:    Any=None							,       **_UpperCamelCase			:    Tuple							)				->str:
									snake_case_       =     None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
															    ''' instead.'''							,       _UpperCamelCase							,       )
															snake_case_       =     kwargs.pop('''feature_extractor'''							)

									snake_case_       =     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
															raise ValueError('''You need to specify an `image_processor`.'''							)
									if tokenizer is None:
															raise ValueError('''You need to specify a `tokenizer`.'''							)

									super().__init__(_UpperCamelCase							,       _UpperCamelCase							)
									snake_case_       =     self.image_processor
									snake_case_       =     False







			def __call__(					self			:    int							,       *_UpperCamelCase			:    Tuple							,       **_UpperCamelCase			:    Any							)				->Tuple:
									# For backward compatibility
									if self._in_target_context_manager:
															return self.current_processor(*_UpperCamelCase							,       **_UpperCamelCase							)

									snake_case_       =     kwargs.pop('''images'''							,       _UpperCamelCase							)
									snake_case_       =     kwargs.pop('''text'''							,       _UpperCamelCase							)
									if len(_UpperCamelCase							) > 0:
															snake_case_       =     args[0]
															snake_case_       =     args[1:]

									if images is None and text is None:
															raise ValueError('''You need to specify either an `images` or `text` input to process.'''							)

									if images is not None:
															snake_case_       =     self.image_processor(_UpperCamelCase							,       *_UpperCamelCase							,       **_UpperCamelCase							)
									if text is not None:
															snake_case_       =     self.tokenizer(_UpperCamelCase							,       **_UpperCamelCase							)

									if text is None:
															return inputs
									elif images is None:
															return encodings
									else:
															snake_case_       =     encodings['''input_ids''']
															return inputs







			def     snake_case__(					self			:    List[Any]							,       *_UpperCamelCase			:    Union[str, Any]							,       **_UpperCamelCase			:    Tuple							)				->Any:
									return self.tokenizer.batch_decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			def     snake_case__(					self			:    Optional[Any]							,       *_UpperCamelCase			:    List[Any]							,       **_UpperCamelCase			:    Union[str, Any]							)				->List[str]:
									return self.tokenizer.decode(*_UpperCamelCase							,       **_UpperCamelCase							)







			@contextmanager
			def     snake_case__(					self			:    Any							)				->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 images inputs, or in a separate call.'''							)
									snake_case_       =     True
									snake_case_       =     self.tokenizer
									yield
									snake_case_       =     self.image_processor
									snake_case_       =     False







			@property
			def     snake_case__(					self			:    Optional[int]							)				->int:
									warnings.warn(
									    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor_class







			@property
			def     snake_case__(					self			:    Union[str, Any]							)				->List[Any]:
									warnings.warn(
									    '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''							,       _UpperCamelCase							,       )
									return self.image_processor