


import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def      _a					( 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 = 1_00		,							SCREAMING_SNAKE_CASE = 0.01		,							SCREAMING_SNAKE_CASE = 1		,							):


		"""simple docstring"""




		lowercase__				= False
		lowercase__				= search_prob
		lowercase__				= start_temperate
		lowercase__				= []
		lowercase__				= 0
		lowercase__				= None

		while not search_end:
				lowercase__				= current_state.score()
				if best_state is None or current_score > best_state.score():
						lowercase__				= current_state
				scores.append(SCREAMING_SNAKE_CASE	)
				iterations += 1
				lowercase__				= None
				lowercase__				= current_state.get_neighbors()
				while (
				    next_state is None and neighbors
				):  # till we do not find a neighbor that we can move to
						lowercase__				= random.randint(0		,							len(SCREAMING_SNAKE_CASE	) - 1	)  # picking a random neighbor
						lowercase__				= neighbors.pop(SCREAMING_SNAKE_CASE	)
						lowercase__				= picked_neighbor.score() - current_score

						if (
						    picked_neighbor.x > max_x
						    or picked_neighbor.x < min_x
						    or picked_neighbor.y > max_y
						    or picked_neighbor.y < min_y
						):
								continue  # neighbor outside our bounds

						if not find_max:
								lowercase__				= change * -1  # in case we are finding minimum
						if change > 0:  # improves the solution
								lowercase__				= picked_neighbor
						else:
								lowercase__				= (math.e) ** (
								    change / current_temp
								)  # probability generation function
								if random.random() < probability:  # random number within probability
										lowercase__				= picked_neighbor
				lowercase__				= current_temp - (current_temp * rate_of_decrease)

				if current_temp < threshold_temp or next_state is None:
						# temperature below threshold, or could not find a suitable neighbor
						lowercase__				= True
				else:
						lowercase__				= next_state

		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 best_state


if __name__ == "__main__":
			def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


					"""simple docstring"""




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

			# starting the problem with initial coordinates (12, 47)
			lowerCAmelCase			=				SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(
			    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()}"""
			)

			# starting the problem with initial coordinates (12, 47)
			lowerCAmelCase			=				SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(
			    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
			)
			print(
			    'The maximum 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      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


					"""simple docstring"""




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

			lowerCAmelCase			=				SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(prob, find_max=False, visualization=True)
			print(
			    'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
			    f"""{local_min.score()}"""
			)

			lowerCAmelCase			=				SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
			lowerCAmelCase			=				simulated_annealing(prob, find_max=True, visualization=True)
			print(
			    'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
			    f"""{local_min.score()}"""
			)






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							['''image_processor''', '''tokenizer''']
      UpperCamelCase										=							'''BridgeTowerImageProcessor'''
      UpperCamelCase										=							('''RobertaTokenizer''', '''RobertaTokenizerFast''')





      def __init__(		self    :       int       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       List[Any]							)     -> Union[str, Any]:






         """simple docstring"""







         super().__init__(__lowerCamelCase       ,							__lowerCamelCase							)





      def __call__(		self    :       Any       ,							__lowerCamelCase    :       Optional[int]       ,							__lowerCamelCase    :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Union[bool, str, PaddingStrategy] = False       ,							__lowerCamelCase    :       Union[bool, str, TruncationStrategy] = None       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       int = 0       ,							__lowerCamelCase    :       Optional[int] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       Optional[bool] = None       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = False       ,							__lowerCamelCase    :       bool = True       ,							__lowerCamelCase    :       Optional[Union[str, TensorType]] = None       ,							**__lowerCamelCase    :       Dict       ,							)     -> BatchEncoding:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer(
             text=__lowerCamelCase       ,							add_special_tokens=__lowerCamelCase       ,							padding=__lowerCamelCase       ,							truncation=__lowerCamelCase       ,							max_length=__lowerCamelCase       ,							stride=__lowerCamelCase       ,							pad_to_multiple_of=__lowerCamelCase       ,							return_token_type_ids=__lowerCamelCase       ,							return_attention_mask=__lowerCamelCase       ,							return_overflowing_tokens=__lowerCamelCase       ,							return_special_tokens_mask=__lowerCamelCase       ,							return_offsets_mapping=__lowerCamelCase       ,							return_length=__lowerCamelCase       ,							verbose=__lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							**__lowerCamelCase       ,							)
         # add pixel_values + pixel_mask
         UpperCAmelCase      					=  self.image_processor(
             __lowerCamelCase       ,							return_tensors=__lowerCamelCase       ,							do_normalize=__lowerCamelCase       ,							do_center_crop=__lowerCamelCase       ,							**__lowerCamelCase							)
         encoding.update(__lowerCamelCase							)

         return encoding





      def 							_lowercase					(		self    :       Optional[Any]       ,							*__lowerCamelCase    :       Optional[int]       ,							**__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







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





      def 							_lowercase					(		self    :       Dict       ,							*__lowerCamelCase    :       Any       ,							**__lowerCamelCase    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







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





      @property
      def 							_lowercase					(		self    :       List[Any]							)     -> Optional[int]:






         """simple docstring"""







         UpperCAmelCase      					=  self.tokenizer.model_input_names
         UpperCAmelCase      					=  self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names							)							)



