


'''simple docstring'''


import math





class 					lowercase__						:





							'''simple docstring'''


							def __init__(					self					,      __snake_case					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: str  	=   x
											_SCREAMING_SNAKE_CASE				: str  	=   y
											_SCREAMING_SNAKE_CASE				: List[str]  	=   step_size
											_SCREAMING_SNAKE_CASE				: int  	=   function_to_optimize






							def UpperCAmelCase_			(					self						):
											return self.function(self.x					,      self.y						)






							def UpperCAmelCase_			(					self						):
											_SCREAMING_SNAKE_CASE				: List[str]  	=   self.step_size
											return [
											    SearchProblem(__snake_case					,      __snake_case					,      __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						):
											return hash(str(self						)						)






							def __eq__(					self					,      __snake_case						):
											if isinstance(__snake_case					,      __snake_case						):
															return hash(str(self						)						) == hash(str(__snake_case						)						)
											return False






							def __str__(					self						):
											return f"""x: {self.x} y: {self.y}"""







def     snake_case_     (						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_0000							,   ):







				"""simple docstring"""





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

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(lowerCAmelCase__  )							,   lowerCAmelCase__  )
								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__							,   SCREAMING_SNAKE_CASE__  ):







										"""simple docstring"""





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

						# starting the problem with initial coordinates (3, 4)
						UpperCAmelCase_	:     List[Any]							     =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						UpperCAmelCase_	:     Dict							     =	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_	:     Union[str, Any]							     =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
						UpperCAmelCase_	:     List[Any]							     =	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     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







										"""simple docstring"""





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

						UpperCAmelCase_	:     Optional[Any]							     =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
						UpperCAmelCase_	:     int							     =	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 TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_	:     Union[str, Any]							     =	{'configuration_pegasus': ['PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PegasusConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Dict							     =	['PegasusTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	['PegasusTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[int]							     =	[
				    'PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'PegasusForCausalLM',
				    'PegasusForConditionalGeneration',
				    'PegasusModel',
				    'PegasusPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Optional[Any]							     =	[
				    'TFPegasusForConditionalGeneration',
				    'TFPegasusModel',
				    'TFPegasusPreTrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCAmelCase_	:     Any							     =	[
				    'FlaxPegasusForConditionalGeneration',
				    'FlaxPegasusModel',
				    'FlaxPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_pegasus import PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus import PegasusTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_pegasus_fast import PegasusTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_pegasus import (
								    PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    PegasusForCausalLM,
								    PegasusForConditionalGeneration,
								    PegasusModel,
								    PegasusPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_pegasus import TFPegasusForConditionalGeneration, TFPegasusModel, TFPegasusPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_pegasus import (
								    FlaxPegasusForConditionalGeneration,
								    FlaxPegasusModel,
								    FlaxPegasusPreTrainedModel,
								)

else:
				import sys

				UpperCAmelCase_	:     List[str]							     =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




