



import math







class 							a_  :

						def __init__(    self	:Optional[int]   ,						_lowercase	:int   ,						_lowercase	:int   ,						_lowercase	:int   ,						_lowercase	:Dict)		->					Tuple:
												UpperCAmelCase_     							=     x
												UpperCAmelCase_     							=     y
												UpperCAmelCase_     							=     step_size
												UpperCAmelCase_     							=     function_to_optimize

						def 	__a       (    self	:Optional[int])		->					int:
												return self.function(self.x   ,						self.y)

						def 	__a       (    self	:int)		->					Union[str, Any]:
												UpperCAmelCase_     							=     self.step_size
												return [
												    SearchProblem(_lowercase   ,						_lowercase   ,						_lowercase   ,						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	:Union[str, Any])		->					Tuple:
												return hash(str(self))

						def __eq__(    self	:Tuple   ,						_lowercase	:List[Any])		->					List[Any]:
												if isinstance(_lowercase   ,						_lowercase):
																		return hash(str(self)) == hash(str(_lowercase))
												return False




						def __str__(    self	:List[str])		->					Tuple:
												return f"x: {self.x} y: {self.y}"







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase = True							,					__UpperCAmelCase = math.inf							,					__UpperCAmelCase = -math.inf							,					__UpperCAmelCase = math.inf							,					__UpperCAmelCase = -math.inf							,					__UpperCAmelCase = False							,					__UpperCAmelCase = 1_0000							,					)       ->       SearchProblem:



						'''simple docstring'''






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

						if visualization:
												from matplotlib import pyplot as plt

												plt.plot(range(__UpperCAmelCase	)							,					__UpperCAmelCase	)
												plt.xlabel('''Iterations'''	)
												plt.ylabel('''Function values'''	)
												plt.show()

						return current_state


if __name__ == "__main__":
							import doctest

							doctest.testmod()







							def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       int:



													'''simple docstring'''






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

							# starting the problem with initial coordinates (3, 4)
							UpperCamelCase_	       =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase_	       =   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)
							UpperCamelCase_	       =   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
							UpperCamelCase_	       =   hill_climbing(
							    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()}"
							)







							def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase	)       ->       List[Any]:



													'''simple docstring'''






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

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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EsmForMaskedLM",
							    "EsmForSequenceClassification",
							    "EsmForTokenClassification",
							    "EsmModel",
							    "EsmPreTrainedModel",
							]
							UpperCamelCase_	       =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEsmForMaskedLM",
							    "TFEsmForSequenceClassification",
							    "TFEsmForTokenClassification",
							    "TFEsmModel",
							    "TFEsmPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
							from .tokenization_esm import EsmTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_esm import (
														    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EsmForMaskedLM,
														    EsmForSequenceClassification,
														    EsmForTokenClassification,
														    EsmModel,
														    EsmPreTrainedModel,
														)
														from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_esm import (
														    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEsmForMaskedLM,
														    TFEsmForSequenceClassification,
														    TFEsmForTokenClassification,
														    TFEsmModel,
														    TFEsmPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)





