
'''simple docstring'''







import math






class 				lowerCAmelCase__     :






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  Any									= x
											lowercase_			:  List[Any]									= y
											lowercase_			:  str									= step_size
											lowercase_			:  str									= function_to_optimize






					def 			_snake_case					(     self	):


											"""simple docstring"""





											return self.function(self.x       ,							self.y	)






					def 			_snake_case					(     self	):


											"""simple docstring"""





											lowercase_			:  str									= 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	):


											"""simple docstring"""





											return hash(str(self	)	)






					def __eq__(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





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






					def __str__(     self	):


											"""simple docstring"""





											return F'''x: {self.x} y: {self.y}'''







def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				bool = True   ,   __SCREAMING_SNAKE_CASE      :				float = math.inf   ,   __SCREAMING_SNAKE_CASE      :				float = -math.inf   ,   __SCREAMING_SNAKE_CASE      :				float = math.inf   ,   __SCREAMING_SNAKE_CASE      :				float = -math.inf   ,   __SCREAMING_SNAKE_CASE      :				bool = False   ,   __SCREAMING_SNAKE_CASE      :				int = 10000   ,   ):



						"""simple docstring"""





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



													"""simple docstring"""





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

							# starting the problem with initial coordinates (3, 4)
							_lowercase				:						Tuple												= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							_lowercase				:						int												= 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)
							_lowercase				:						List[str]												= SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							_lowercase				:						Optional[Any]												= hill_climbing(
							    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()}"""
							)
							def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				Any   ,   __SCREAMING_SNAKE_CASE      :				Tuple							):



													"""simple docstring"""





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

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





'''simple docstring'''







from typing import List

from ...processing_utils import ProcessorMixin
from ...utils import is_torch_available


if is_torch_available():
							import torch






class 				lowerCAmelCase__     (						lowerCamelCase_  ):
					lowerCAmelCase_       =  ['''image_processor''', '''tokenizer''']
					lowerCAmelCase_       =  '''OneFormerImageProcessor'''
					lowerCAmelCase_       =  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')






					def __init__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE = 77       ,							__SCREAMING_SNAKE_CASE = 77       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											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`.'''	)

											lowercase_			:  Dict									= max_seq_length
											lowercase_			:  Dict									= task_seq_length

											super().__init__(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=77	):


											"""simple docstring"""





											if text_list is None:
																	raise ValueError('''tokens cannot be None.'''	)

											lowercase_			:  str									= self.tokenizer(__SCREAMING_SNAKE_CASE       ,							padding='''max_length'''       ,							max_length=__SCREAMING_SNAKE_CASE       ,							truncation=__SCREAMING_SNAKE_CASE	)

											lowercase_		,				lowercase_			:  List[str]									= tokens['''attention_mask'''], tokens['''input_ids''']

											lowercase_			:  List[str]									= []
											for attn_mask, input_id in zip(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  Optional[Any]									= torch.tensor(__SCREAMING_SNAKE_CASE	) * torch.tensor(__SCREAMING_SNAKE_CASE	)
																	token_inputs.append(token.unsqueeze(0	)	)

											lowercase_			:  str									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)
											return token_inputs






					def __call__(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Optional[int]									= self.image_processor(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[str]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  List[str]									= []
																	for task in task_inputs:
																							lowercase_			:  Tuple									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  Tuple									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Any									= encoded_inputs.text_inputs

																	lowercase_			:  int									= []
																	for texts in texts_list:
																							lowercase_			:  Any									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Tuple									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							__SCREAMING_SNAKE_CASE=None       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if task_inputs is None:
																	raise ValueError('''You have to specify the task_input. Found None.'''	)
											elif images is None:
																	raise ValueError('''You have to specify the image. Found None.'''	)

											if not all(task in ['''semantic''', '''instance''', '''panoptic'''] for task in task_inputs	):
																	raise ValueError('''task_inputs must be semantic, instance, or panoptic.'''	)

											lowercase_			:  Dict									= self.image_processor.encode_inputs(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):
																	lowercase_			:  List[Any]									= [task_inputs]

											if isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) and all(isinstance(__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	) for task_input in task_inputs	):
																	lowercase_			:  int									= []
																	for task in task_inputs:
																							lowercase_			:  int									= F'''the task is {task}'''
																							task_token_inputs.append(__SCREAMING_SNAKE_CASE	)
																	lowercase_			:  str									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.task_seq_length	)
											else:
																	raise TypeError('''Task Inputs should be a string or a list of strings.'''	)

											if hasattr(__SCREAMING_SNAKE_CASE       ,							'''text_inputs'''	):
																	lowercase_			:  Dict									= encoded_inputs.text_inputs

																	lowercase_			:  str									= []
																	for texts in texts_list:
																							lowercase_			:  List[str]									= self._preprocess_text(__SCREAMING_SNAKE_CASE       ,							max_length=self.max_seq_length	)
																							text_inputs.append(text_input_list.unsqueeze(0	)	)

																	lowercase_			:  Union[str, Any]									= torch.cat(__SCREAMING_SNAKE_CASE       ,							dim=0	)

											return encoded_inputs






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_semantic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_instance_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)






					def 			_snake_case					(     self       ,							*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											return self.image_processor.post_process_panoptic_segmentation(*__SCREAMING_SNAKE_CASE       ,							**__SCREAMING_SNAKE_CASE	)



