


import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = True       ,			_UpperCamelCase = math.inf       ,			_UpperCamelCase = -math.inf       ,			_UpperCamelCase = math.inf       ,			_UpperCamelCase = -math.inf       ,			_UpperCamelCase = False       ,			_UpperCamelCase = 1_00       ,			_UpperCamelCase = 0.01       ,			_UpperCamelCase = 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(_UpperCamelCase  )
						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(_UpperCamelCase  ) - 1  )  # picking a random neighbor
									__lowercase 					=   neighbors.pop(_UpperCamelCase  )
									__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(_UpperCamelCase  )       ,			_UpperCamelCase  )
						plt.xlabel('''Iterations'''  )
						plt.ylabel('''Function values'''  )
						plt.show()
			return best_state


if __name__ == "__main__":


	def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


				'''simple docstring'''







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

	# starting the problem with initial coordinates (12, 47)
	a : Dict		=				SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	a : Union[str, Any]		=				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)
	a : Any		=				SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	a : int		=				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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


				'''simple docstring'''







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

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



import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union


if TYPE_CHECKING:
	from ...processing_utils import ProcessorMixin
	from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a : Union[str, Any]		=				logging.get_logger(__name__)

a : Any		=				{
    '''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
    '''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
    '''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit_text_model"






					def __init__(				self ,       snake_case_=4_9_4_0_8 ,       snake_case_=5_1_2 ,       snake_case_=2_0_4_8 ,       snake_case_=1_2 ,       snake_case_=8 ,       snake_case_=1_6 ,       snake_case_="quick_gelu" ,       snake_case_=1e-5 ,       snake_case_=0.0 ,       snake_case_=0.0_2 ,       snake_case_=1.0 ,       snake_case_=0 ,       snake_case_=4_9_4_0_6 ,       snake_case_=4_9_4_0_7 ,       **snake_case_ ,       )   ->			int:







								'''simple docstring'''

								super().__init__(pad_token_id=snake_case_ ,       bos_token_id=snake_case_ ,       eos_token_id=snake_case_ ,       **snake_case_						)

								__lowercase 					=   vocab_size
								__lowercase 					=   hidden_size
								__lowercase 					=   intermediate_size
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   max_position_embeddings
								__lowercase 					=   hidden_act
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   attention_dropout
								__lowercase 					=   initializer_range
								__lowercase 					=   initializer_factor






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

								# get the text config dict if we are loading from OwlViTConfig
								if config_dict.get('''model_type'''						) == "owlvit":
											__lowercase 					=   config_dict['''text_config''']

								if "model_type" in config_dict and hasattr(cls ,       '''model_type'''						) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

								return cls.from_dict(snake_case_ ,       **snake_case_						)




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit_vision_model"






					def __init__(				self ,       snake_case_=7_6_8 ,       snake_case_=3_0_7_2 ,       snake_case_=1_2 ,       snake_case_=1_2 ,       snake_case_=3 ,       snake_case_=7_6_8 ,       snake_case_=3_2 ,       snake_case_="quick_gelu" ,       snake_case_=1e-5 ,       snake_case_=0.0 ,       snake_case_=0.0_2 ,       snake_case_=1.0 ,       **snake_case_ ,       )   ->			Any:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								__lowercase 					=   hidden_size
								__lowercase 					=   intermediate_size
								__lowercase 					=   num_hidden_layers
								__lowercase 					=   num_attention_heads
								__lowercase 					=   num_channels
								__lowercase 					=   image_size
								__lowercase 					=   patch_size
								__lowercase 					=   hidden_act
								__lowercase 					=   layer_norm_eps
								__lowercase 					=   attention_dropout
								__lowercase 					=   initializer_range
								__lowercase 					=   initializer_factor






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

								# get the vision config dict if we are loading from OwlViTConfig
								if config_dict.get('''model_type'''						) == "owlvit":
											__lowercase 					=   config_dict['''vision_config''']

								if "model_type" in config_dict and hasattr(cls ,       '''model_type'''						) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

								return cls.from_dict(snake_case_ ,       **snake_case_						)




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					__UpperCAmelCase            =				"owlvit"
					__UpperCAmelCase            =				True






					def __init__(				self ,       snake_case_=None ,       snake_case_=None ,       snake_case_=5_1_2 ,       snake_case_=2.6_5_9_2 ,       snake_case_=True ,       **snake_case_ ,       )   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__(**snake_case_						)

								if text_config is None:
											__lowercase 					=   {}
											logger.info('''text_config is None. Initializing the OwlViTTextConfig with default values.'''						)

								if vision_config is None:
											__lowercase 					=   {}
											logger.info('''vision_config is None. initializing the OwlViTVisionConfig with default values.'''						)

								__lowercase 					=   OwlViTTextConfig(**snake_case_						)
								__lowercase 					=   OwlViTVisionConfig(**snake_case_						)

								__lowercase 					=   projection_dim
								__lowercase 					=   logit_scale_init_value
								__lowercase 					=   return_dict
								__lowercase 					=   1.0






					@classmethod
					def A      (				cls ,       snake_case_ ,       **snake_case_						)   ->			"PretrainedConfig":







								'''simple docstring'''

								cls._set_token_in_kwargs(snake_case_						)

								__lowercase					, __lowercase 					=   cls.get_config_dict(snake_case_ ,       **snake_case_						)

								if "model_type" in config_dict and hasattr(cls ,       '''model_type'''						) and config_dict["model_type"] != cls.model_type:
											logger.warning(
											    F'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
											    F'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

								return cls.from_dict(snake_case_ ,       **snake_case_						)






					@classmethod
					def A      (				cls ,       snake_case_ ,       snake_case_ ,       **snake_case_						)   ->			int:







								'''simple docstring'''

								__lowercase 					=   {}
								__lowercase 					=   text_config
								__lowercase 					=   vision_config

								return cls.from_dict(snake_case_ ,       **snake_case_						)






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   copy.deepcopy(self.__dict__						)
								__lowercase 					=   self.text_config.to_dict()
								__lowercase 					=   self.vision_config.to_dict()
								__lowercase 					=   self.__class__.model_type
								return output




class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					@property
					def A      (				self						)   ->			Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								return OrderedDict(
								    [
								        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
								        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
								        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
								    ]						)






					@property
					def A      (				self						)   ->			Mapping[str, Mapping[int, str]]:







								'''simple docstring'''

								return OrderedDict(
								    [
								        ('''logits_per_image''', {0: '''batch'''}),
								        ('''logits_per_text''', {0: '''batch'''}),
								        ('''text_embeds''', {0: '''batch'''}),
								        ('''image_embeds''', {0: '''batch'''}),
								    ]						)






					@property
					def A      (				self						)   ->			float:







								'''simple docstring'''

								return 1e-4






					def A      (				self ,       snake_case_ ,       snake_case_ = -1 ,       snake_case_ = -1 ,       snake_case_ = None ,       )   ->			Mapping[str, Any]:







								'''simple docstring'''

								__lowercase 					=   super().generate_dummy_inputs(
								    processor.tokenizer ,       batch_size=snake_case_ ,       seq_length=snake_case_ ,       framework=snake_case_						)
								__lowercase 					=   super().generate_dummy_inputs(
								    processor.image_processor ,       batch_size=snake_case_ ,       framework=snake_case_						)
								return {**text_input_dict, **image_input_dict}






					@property
					def A      (				self						)   ->			int:







								'''simple docstring'''

								return 1_4


