
import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def    a      ( a		,						a = True		,						a = math.inf		,						a = -math.inf		,						a = math.inf		,						a = -math.inf		,						a = False		,						a = 100		,						a = 0.01		,						a = 1		,						)     ->Any:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE     						= False
			SCREAMING_SNAKE_CASE     						= search_prob
			SCREAMING_SNAKE_CASE     						= start_temperate
			SCREAMING_SNAKE_CASE     						= []
			SCREAMING_SNAKE_CASE     						= 0
			SCREAMING_SNAKE_CASE     						= None

			while not search_end:
						SCREAMING_SNAKE_CASE     						= current_state.score()
						if best_state is None or current_score > best_state.score():
									SCREAMING_SNAKE_CASE     						= current_state
						scores.append(a	)
						iterations += 1
						SCREAMING_SNAKE_CASE     						= None
						SCREAMING_SNAKE_CASE     						= current_state.get_neighbors()
						while (
						    next_state is None and neighbors
						):  # till we do not find a neighbor that we can move to
									SCREAMING_SNAKE_CASE     						= random.randint(0		,						len(a	) - 1	)  # picking a random neighbor
									SCREAMING_SNAKE_CASE     						= neighbors.pop(a	)
									SCREAMING_SNAKE_CASE     						= 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:
												SCREAMING_SNAKE_CASE     						= change * -1  # in case we are finding minimum
									if change > 0:  # improves the solution
												SCREAMING_SNAKE_CASE     						= picked_neighbor
									else:
												SCREAMING_SNAKE_CASE     						= (math.e) ** (
												    change / current_temp
												)  # probability generation function
												if random.random() < probability:  # random number within probability
															SCREAMING_SNAKE_CASE     						= picked_neighbor
						SCREAMING_SNAKE_CASE     						= 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
									SCREAMING_SNAKE_CASE     						= True
						else:
									SCREAMING_SNAKE_CASE     						= next_state

			if visualization:
						from matplotlib import pyplot as plt

						plt.plot(range(a	)		,						a	)
						plt.xlabel('''Iterations'''	)
						plt.ylabel('''Function values'''	)
						plt.show()
			return best_state


if __name__ == "__main__":


							def    a      ( a		,						a	)     ->Tuple:




										'''simple docstring'''


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

							# starting the problem with initial coordinates (12, 47)
							__lowerCAmelCase					   =       SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							__lowerCAmelCase					   =       simulated_annealing(
							    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()}'''
							)

							# starting the problem with initial coordinates (12, 47)
							__lowerCAmelCase					   =       SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
							__lowerCAmelCase					   =       simulated_annealing(
							    prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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    a      ( a		,						a	)     ->Dict:




										'''simple docstring'''


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

							__lowerCAmelCase					   =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							__lowerCAmelCase					   =       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()}'''
							)

							__lowerCAmelCase					   =       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
							__lowerCAmelCase					   =       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()}'''
							)

from typing import TYPE_CHECKING

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


__lowerCAmelCase					   =       {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['DebertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'DebertaForMaskedLM',
							    'DebertaForQuestionAnswering',
							    'DebertaForSequenceClassification',
							    'DebertaForTokenClassification',
							    'DebertaModel',
							    'DebertaPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFDebertaForMaskedLM',
							    'TFDebertaForQuestionAnswering',
							    'TFDebertaForSequenceClassification',
							    'TFDebertaForTokenClassification',
							    'TFDebertaModel',
							    'TFDebertaPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)