



'''simple docstring'''

import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def 						__UpperCamelCase				(							_lowercase,				_lowercase = True,				_lowercase = math.inf,				_lowercase = -math.inf,				_lowercase = math.inf,				_lowercase = -math.inf,				_lowercase = False,				_lowercase = 100,				_lowercase = 0.0_1,				_lowercase = 1,				) ->			Any:
	_lowercase					:       Dict       =		False
	_lowercase					:       Optional[Any]       =		search_prob
	_lowercase					:       Any       =		start_temperate
	_lowercase					:       int       =		[]
	_lowercase					:       List[Any]       =		0
	_lowercase					:       List[Any]       =		None

	while not search_end:
		_lowercase					:       int       =		current_state.score()
		if best_state is None or current_score > best_state.score():
			_lowercase					:       int       =		current_state
		scores.append(_lowercase					)
		iterations += 1
		_lowercase					:       Tuple       =		None
		_lowercase					:       List[str]       =		current_state.get_neighbors()
		while (
		    next_state is None and neighbors
		):  # till we do not find a neighbor that we can move to
			_lowercase					:       Optional[Any]       =		random.randint(0,				len(_lowercase					) - 1					)  # picking a random neighbor
			_lowercase					:       List[Any]       =		neighbors.pop(_lowercase					)
			_lowercase					:       List[str]       =		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					:       Union[str, Any]       =		change * -1  # in case we are finding minimum
			if change > 0:  # improves the solution
				_lowercase					:       str       =		picked_neighbor
			else:
				_lowercase					:       str       =		(math.e) ** (
				    change / current_temp
				)  # probability generation function
				if random.random() < probability:  # random number within probability
					_lowercase					:       List[Any]       =		picked_neighbor
		_lowercase					:       Any       =		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					:       Dict       =		True
		else:
			_lowercase					:       int       =		next_state

	if visualization:
		from matplotlib import pyplot as plt

		plt.plot(range(_lowercase					),				_lowercase					)
		plt.xlabel('Iterations'					)
		plt.ylabel('Function values'					)
		plt.show()
	return best_state


if __name__ == "__main__":







		def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Dict:
			return (x**2) + (y**2)

		# starting the problem with initial coordinates (12, 47)
		_A				:    Dict     						=SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
		_A				:    Optional[Any]     						=simulated_annealing(
		    prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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)
		_A				:    Union[str, Any]     						=SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
		_A				:    Optional[Any]     						=simulated_annealing(
		    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			List[str]:
			return (3 * x**2) - (6 * y)

		_A				:    Any     						=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		_A				:    Union[str, Any]     						=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()}'''
		)

		_A				:    List[str]     						=SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		_A				:    Any     						=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()}'''
		)





'''simple docstring'''

from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_A				:    Optional[int]     						=logging.get_logger(__name__)



@add_end_docstrings(A     )
class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	Tuple     ,  **UpperCamelCase_				:	List[str]					)  ->       int:



						'''simple docstring'''





						super().__init__(**UpperCamelCase_					)

						requires_backends(self     ,  'vision'					)
						self.check_model_type(
						    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
						    if self.framework == 'tf'
						    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING					)




					def __call__(  self				:	int     ,  UpperCamelCase_				:	Union[str, List[str], "Image", List["Image"]]     ,  **UpperCamelCase_				:	Tuple					)  ->       List[Any]:



						'''simple docstring'''





						return super().__call__(UpperCamelCase_     ,  **UpperCamelCase_					)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  **UpperCamelCase_				:	str					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		{}
						if "candidate_labels" in kwargs:
							_lowercase					:       Union[str, Any]       =		kwargs['candidate_labels']
						if "hypothesis_template" in kwargs:
							_lowercase					:       int       =		kwargs['hypothesis_template']

						return preprocess_params, {}, {}




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Dict     ,  UpperCamelCase_				:	Optional[Any]=None     ,  UpperCamelCase_				:	str="This is a photo of {}."					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		load_image(UpperCamelCase_					)
						_lowercase					:       List[str]       =		self.image_processor(images=[image]     ,  return_tensors=self.framework					)
						_lowercase					:       Optional[Any]       =		candidate_labels
						_lowercase					:       List[Any]       =		[hypothesis_template.format(UpperCamelCase_					) for x in candidate_labels]
						_lowercase					:       Union[str, Any]       =		self.tokenizer(UpperCamelCase_     ,  return_tensors=self.framework     ,  padding=UpperCamelCase_					)
						_lowercase					:       Any       =		[text_inputs]
						return inputs




					def 						__UpperCAmelCase     (  self				:	str     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		model_inputs.pop('candidate_labels'					)
						_lowercase					:       List[str]       =		model_inputs.pop('text_inputs'					)
						if isinstance(text_inputs[0]     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		text_inputs[0]
						else:
							# Batching case.
							_lowercase					:       List[str]       =		text_inputs[0][0]

						_lowercase					:       Optional[Any]       =		self.model(**UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		{
						    'candidate_labels': candidate_labels,
						    'logits': outputs.logits_per_image,
						}
						return model_outputs




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	int					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[int]       =		model_outputs.pop('candidate_labels'					)
						_lowercase					:       Optional[int]       =		model_outputs['logits'][0]
						if self.framework == "pt":
							_lowercase					:       List[Any]       =		logits.softmax(dim=-1					).squeeze(-1					)
							_lowercase					:       Tuple       =		probs.tolist()
							if not isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
								_lowercase					:       List[Any]       =		[scores]
						elif self.framework == "tf":
							_lowercase					:       Optional[int]       =		stable_softmax(UpperCamelCase_     ,  axis=-1					)
							_lowercase					:       List[Any]       =		probs.numpy().tolist()
						else:
							raise ValueError(F'''Unsupported framework: {self.framework}'''					)

						_lowercase					:       List[Any]       =		[
						    {'score': score, 'label': candidate_label}
						    for score, candidate_label in sorted(zip(UpperCamelCase_     ,  UpperCamelCase_					)     ,  key=lambda UpperCamelCase_					: -x[0]					)
						]
						return result
