






"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem


def 	SCREAMING_SNAKE_CASE__       (  snake_case       : Optional[int]			,					snake_case       : bool = True			,					snake_case       : float = math.inf			,					snake_case       : float = -math.inf			,					snake_case       : float = math.inf			,					snake_case       : float = -math.inf			,					snake_case       : bool = False			,					snake_case       : float = 100			,					snake_case       : float = 0.01			,					snake_case       : float = 1			,					)->		Any:






     '''simple docstring'''



     UpperCAmelCase__						:    Union[str, Any]							=							False
     UpperCAmelCase__						:    Optional[Any]							=							search_prob
     UpperCAmelCase__						:    str							=							start_temperate
     UpperCAmelCase__						:    int							=							[]
     UpperCAmelCase__						:    Optional[Any]							=							0
     UpperCAmelCase__						:    Union[str, Any]							=							None

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

     if visualization:
          from matplotlib import pyplot as plt

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


if __name__ == "__main__":


 def 	SCREAMING_SNAKE_CASE__       (  snake_case       : int			,					snake_case       : Tuple				)->		Optional[Any]:






      '''simple docstring'''



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

 # starting the problem with initial coordinates (12, 47)
 _lowerCAmelCase	:	List[Any]				=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase	:	List[Any]				=      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)
 _lowerCAmelCase	:	int				=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase	:	str				=      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__       (  snake_case       : List[Any]			,					snake_case       : Optional[Any]				)->		Union[str, Any]:






      '''simple docstring'''



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

 _lowerCAmelCase	:	Tuple				=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase	:	Tuple				=      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	:	int				=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 _lowerCAmelCase	:	Optional[int]				=      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()}"""
 )










"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase	:	Optional[int]				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Tuple				=      {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''mra'''
   def __init__(		self      :	Any							,			snake_case__      :	List[str]=5_0_2_6_5							,			snake_case__      :	Any=7_6_8							,			snake_case__      :	Union[str, Any]=1_2							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	Tuple=3_0_7_2							,			snake_case__      :	str="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Tuple=5_1_2							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[Any]=0.02							,			snake_case__      :	str=1e-5							,			snake_case__      :	List[Any]="absolute"							,			snake_case__      :	str=4							,			snake_case__      :	List[str]="full"							,			snake_case__      :	Tuple=0							,			snake_case__      :	Any=0							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	int=0							,			snake_case__      :	int=2							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[str]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Any							=							type_vocab_size
        UpperCAmelCase__						:    Dict							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							position_embedding_type
        UpperCAmelCase__						:    List[str]							=							block_per_row
        UpperCAmelCase__						:    Optional[Any]							=							approx_mode
        UpperCAmelCase__						:    Any							=							initial_prior_first_n_blocks
        UpperCAmelCase__						:    List[Any]							=							initial_prior_diagonal_n_blocks


