

"""simple docstring"""





import math

class     __a   :

			"""simple docstring"""

			def __init__(							self     :					List[Any]						,  lowercase_     :					int						,  lowercase_     :					int						,  lowercase_     :					int						,  lowercase_     :					Any						):
								UpperCamelCase__		:    List[Any]         =x
								UpperCamelCase__		:    List[Any]         =y
								UpperCamelCase__		:    Any         =step_size
								UpperCamelCase__		:    Optional[int]         =function_to_optimize






			def    _lowerCAmelCase			(							self     :					int						):
								return self.function(self.x						,  self.y						)






			def    _lowerCAmelCase			(							self     :					Dict						):
								UpperCamelCase__		:    Tuple         =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     :					List[Any]						):
								return hash(str(self						)						)






			def __eq__(							self     :					Union[str, Any]						,  lowercase_     :					Optional[Any]						):
								if isinstance(lowercase_						,  lowercase_						):
													return hash(str(self						)						) == hash(str(lowercase_						)						)
								return False






			def __str__(							self     :					Optional[int]						):
								return f'''x: {self.x} y: {self.y}'''



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[str]      ,							UpperCAmelCase     :							bool = True      ,							UpperCAmelCase     :							float = math.inf      ,							UpperCAmelCase     :							float = -math.inf      ,							UpperCAmelCase     :							float = math.inf      ,							UpperCAmelCase     :							float = -math.inf      ,							UpperCAmelCase     :							bool = False      ,							UpperCAmelCase     :							int = 10_000      ,							):


					'''simple docstring'''






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


									'''simple docstring'''






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

				# starting the problem with initial coordinates (3, 4)
				_SCREAMING_SNAKE_CASE       :  Any        = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
				_SCREAMING_SNAKE_CASE       :  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)
				_SCREAMING_SNAKE_CASE       :  Union[str, Any]        = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
				_SCREAMING_SNAKE_CASE       :  List[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        _lowerCAmelCase      (						UpperCAmelCase     :							str      ,							UpperCAmelCase     :							Tuple				):


									'''simple docstring'''






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

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


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


