





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

if is_torch_available():
				import torch

				from transformers.modeling_outputs import BaseModelOutput

				from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

snake_case_							=			logging.get_logger(__name__)
@add_end_docstrings(lowercase_)
class        snake_case_		(			lowercase_):







		def __init__(		self			,     **__lowercase   )				->   Tuple:
				super().__init__(**UpperCamelCase__   )

				if self.framework == "tf":
						raise ValueError(F"The {self.__class__} is only available in PyTorch."   )

				requires_backends(self			,     '''vision'''   )
				self.check_model_type(UpperCamelCase__   )







		def __call__(		self			,     __lowercase			,     __lowercase = None			,     **__lowercase			,     )				->   Optional[int]:







				if "text_queries" in kwargs:
						lowerCamelCase   : List[str]								=kwargs.pop('''text_queries'''   )

				if isinstance(UpperCamelCase__			,     (str, Image.Image)   ):
						lowerCamelCase   : str								={'''image''': image, '''candidate_labels''': candidate_labels}
				else:
						lowerCamelCase   : int								=image
				lowerCamelCase   : Optional[int]								=super().__call__(UpperCamelCase__			,     **UpperCamelCase__   )
				return results







		def 			__lowercase   (		self			,     **__lowercase   )				->   int:
				lowerCamelCase   : Optional[Any]								={}
				if "threshold" in kwargs:
						lowerCamelCase   : Tuple								=kwargs['''threshold''']
				if "top_k" in kwargs:
						lowerCamelCase   : List[str]								=kwargs['''top_k''']
				return {}, {}, postprocess_params







		def 			__lowercase   (		self			,     __lowercase   )				->   int:
				lowerCamelCase   : Tuple								=load_image(inputs['''image''']   )
				lowerCamelCase   : Any								=inputs['''candidate_labels''']
				if isinstance(UpperCamelCase__			,     UpperCamelCase__   ):
						lowerCamelCase   : Tuple								=candidate_labels.split(''','''   )

				lowerCamelCase   : int								=torch.tensor([[image.height, image.width]]			,     dtype=torch.intaa   )
				for i, candidate_label in enumerate(UpperCamelCase__   ):
						lowerCamelCase   : Union[str, Any]								=self.tokenizer(UpperCamelCase__			,     return_tensors=self.framework   )
						lowerCamelCase   : Union[str, Any]								=self.image_processor(UpperCamelCase__			,     return_tensors=self.framework   )
						yield {
						    "is_last": i == len(UpperCamelCase__   ) - 1,
						    "target_size": target_size,
						    "candidate_label": candidate_label,
						    **text_inputs,
						    **image_features,
						}







		def 			__lowercase   (		self			,     __lowercase   )				->   Union[str, Any]:
				lowerCamelCase   : int								=model_inputs.pop('''target_size'''   )
				lowerCamelCase   : Tuple								=model_inputs.pop('''candidate_label'''   )
				lowerCamelCase   : Optional[Any]								=model_inputs.pop('''is_last'''   )

				lowerCamelCase   : str								=self.model(**UpperCamelCase__   )

				lowerCamelCase   : str								={'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
				return model_outputs







		def 			__lowercase   (		self			,     __lowercase			,     __lowercase=0.1			,     __lowercase=None   )				->   Optional[int]:
				lowerCamelCase   : Optional[Any]								=[]
				for model_output in model_outputs:
						lowerCamelCase   : Optional[Any]								=model_output['''candidate_label''']
						lowerCamelCase   : str								=BaseModelOutput(UpperCamelCase__   )
						lowerCamelCase   : Union[str, Any]								=self.image_processor.post_process_object_detection(
						    outputs=UpperCamelCase__			,     threshold=UpperCamelCase__			,     target_sizes=model_output['''target_size''']   )[0]

						for index in outputs["scores"].nonzero():
								lowerCamelCase   : List[Any]								=outputs['''scores'''][index].item()
								lowerCamelCase   : List[Any]								=self._get_bounding_box(outputs['''boxes'''][index][0]   )

								lowerCamelCase   : Optional[Any]								={'''score''': score, '''label''': label, '''box''': box}
								results.append(UpperCamelCase__   )

				lowerCamelCase   : Dict								=sorted(UpperCamelCase__			,     key=lambda __lowercase   : x["score"]			,     reverse=UpperCamelCase__   )
				if top_k:
						lowerCamelCase   : Optional[int]								=results[:top_k]

				return results







		def 			__lowercase   (		self			,     __lowercase   )				->   List[str]:







				if self.framework != "pt":
						raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''   )
				lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : List[Any]								=box.int().tolist()
				lowerCamelCase   : Any								={
				    '''xmin''': xmin,
				    '''ymin''': ymin,
				    '''xmax''': xmax,
				    '''ymax''': ymax,
				}
				return bbox








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_0_0							,       SCREAMING_SNAKE_CASE_ = 0.0_1							,       SCREAMING_SNAKE_CASE_ = 1							,       )   ->     Any:
		lowerCamelCase   : Union[str, Any]								=False
		lowerCamelCase   : int								=search_prob
		lowerCamelCase   : int								=start_temperate
		lowerCamelCase   : Any								=[]
		lowerCamelCase   : Any								=0
		lowerCamelCase   : Union[str, Any]								=None

		while not search_end:
				lowerCamelCase   : Dict								=current_state.score()
				if best_state is None or current_score > best_state.score():
						lowerCamelCase   : Tuple								=current_state
				scores.append(SCREAMING_SNAKE_CASE_		)
				iterations += 1
				lowerCamelCase   : Union[str, Any]								=None
				lowerCamelCase   : int								=current_state.get_neighbors()
				while (
				    next_state is None and neighbors
				):  # till we do not find a neighbor that we can move to
						lowerCamelCase   : Union[str, Any]								=random.randint(0							,       len(SCREAMING_SNAKE_CASE_		) - 1		)  # picking a random neighbor
						lowerCamelCase   : List[str]								=neighbors.pop(SCREAMING_SNAKE_CASE_		)
						lowerCamelCase   : Any								=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:
								lowerCamelCase   : Dict								=change * -1  # in case we are finding minimum
						if change > 0:  # improves the solution
								lowerCamelCase   : Optional[int]								=picked_neighbor
						else:
								lowerCamelCase   : Any								=(math.e) ** (
								    change / current_temp
								)  # probability generation function
								if random.random() < probability:  # random number within probability
										lowerCamelCase   : Union[str, Any]								=picked_neighbor
				lowerCamelCase   : Union[str, 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
						lowerCamelCase   : Optional[int]								=True
				else:
						lowerCamelCase   : List[str]								=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_		)   ->     List[str]:
						return (x**2) + (y**2)

				# starting the problem with initial coordinates (12, 47)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			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)
				snake_case_							=			SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Tuple:
						return (3 * x**2) - (6 * y)

				snake_case_							=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			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()}"""
				)

				snake_case_							=			SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				snake_case_							=			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()}"""
				)

