

import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  str  ,      lowerCAmelCase:  bool = True  ,      lowerCAmelCase:  float = math.inf  ,      lowerCAmelCase:  float = -math.inf  ,      lowerCAmelCase:  float = math.inf  ,      lowerCAmelCase:  float = -math.inf  ,      lowerCAmelCase:  bool = False  ,      lowerCAmelCase:  float = 100  ,      lowerCAmelCase:  float = 0.01  ,      lowerCAmelCase:  float = 1  ,      )   ->       Any:
				_UpperCAmelCase       :		Any 		=    False
				_UpperCAmelCase       :		Tuple 		=    search_prob
				_UpperCAmelCase       :		int 		=    start_temperate
				_UpperCAmelCase       :		Optional[int] 		=    []
				_UpperCAmelCase       :		Optional[int] 		=    0
				_UpperCAmelCase       :		str 		=    None

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

				if visualization:
								from matplotlib import pyplot as plt

								plt.plot(range(lowerCAmelCase )  ,      lowerCAmelCase )
								plt.xlabel("Iterations" )
								plt.ylabel("Function values" )
								plt.show()
				return best_state


if __name__ == "__main__":

					def 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Dict  ,      lowerCAmelCase:  Any )   ->       Optional[Any]:
									return (x**2) + (y**2)

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

					# starting the problem with initial coordinates (12, 47)
					SCREAMING_SNAKE_CASE_        =      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_        =      simulated_annealing(
					    prob, find_max=True, max_x=100, min_x=5, max_y=50, 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 							__SCREAMING_SNAKE_CASE							(				lowerCAmelCase:  Any  ,      lowerCAmelCase:  Dict )   ->       List[Any]:
									return (3 * x**2) - (6 * y)

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

					SCREAMING_SNAKE_CASE_        =      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					SCREAMING_SNAKE_CASE_        =      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_        =      {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}

class 				a  (	UpperCAmelCase				):
						_lowercase    						=			"megatron-bert"


						def __init__(       self						,					A_=29056						,					A_=1024						,					A_=24						,					A_=16						,					A_=4096						,					A_="gelu"						,					A_=0.1						,					A_=0.1						,					A_=512						,					A_=2						,					A_=0.02						,					A_=1e-12						,					A_=0						,					A_="absolute"						,					A_=True						,					**A_						,					):







										'''simple docstring'''


										super().__init__(pad_token_id=A_						,					**A_	)

										_UpperCAmelCase       :		Optional[Any] 		=    vocab_size
										_UpperCAmelCase       :		List[str] 		=    hidden_size
										_UpperCAmelCase       :		List[str] 		=    num_hidden_layers
										_UpperCAmelCase       :		Optional[Any] 		=    num_attention_heads
										_UpperCAmelCase       :		Any 		=    hidden_act
										_UpperCAmelCase       :		Optional[Any] 		=    intermediate_size
										_UpperCAmelCase       :		int 		=    hidden_dropout_prob
										_UpperCAmelCase       :		Optional[Any] 		=    attention_probs_dropout_prob
										_UpperCAmelCase       :		Any 		=    max_position_embeddings
										_UpperCAmelCase       :		str 		=    type_vocab_size
										_UpperCAmelCase       :		Optional[int] 		=    initializer_range
										_UpperCAmelCase       :		Dict 		=    layer_norm_eps
										_UpperCAmelCase       :		Tuple 		=    position_embedding_type
										_UpperCAmelCase       :		Tuple 		=    use_cache


