


'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def   _a		(lowercase__			:   Tuple							,   lowercase__			:   bool = True							,   lowercase__			:   float = math.inf							,   lowercase__			:   float = -math.inf							,   lowercase__			:   float = math.inf							,   lowercase__			:   float = -math.inf							,   lowercase__			:   bool = False							,   lowercase__			:   float = 1_0_0							,   lowercase__			:   float = 0.01							,   lowercase__			:   float = 1							,   )			->			Any:






     """simple docstring"""
     __snake_case           =						False
     __snake_case           =						search_prob
     __snake_case           =						start_temperate
     __snake_case           =						[]
     __snake_case           =						0
     __snake_case           =						None

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

     if visualization:
          from matplotlib import pyplot as plt

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


if __name__ == "__main__":





       def   _a		(lowercase__			:   Tuple							,   lowercase__			:   List[str]						)			->			Any:






            """simple docstring"""
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       _a   :   List[Any]												=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       _a   :   Tuple												=   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)
       _a   :   str												=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       _a   :   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   _a		(lowercase__			:   List[str]							,   lowercase__			:   Dict						)			->			str:






            """simple docstring"""
            return (3 * x**2) - (6 * y)

       _a   :   List[str]												=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       _a   :   Dict												=   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()}'''
       )

       _a   :   Tuple												=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       _a   :   List[str]												=   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


_SCREAMING_SNAKE_CASE			=						logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE			=						{
    "facebook/dpr-ctx_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-single-nq-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-single-nq-base": (
        "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json"
    ),
    "facebook/dpr-ctx_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-question_encoder-multiset-base": (
        "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json"
    ),
    "facebook/dpr-reader-multiset-base": (
        "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json"
    ),
}
class   __UpperCAmelCase				(     lowerCAmelCase				):



					'''simple docstring'''





					_UpperCamelCase							=		"""dpr"""



					def __init__(	self     :						Optional[Any]      ,       _lowercase     :						Dict=30_522      ,       _lowercase     :						Tuple=768      ,       _lowercase     :						int=12      ,       _lowercase     :						List[str]=12      ,       _lowercase     :						Union[str, Any]=3_072      ,       _lowercase     :						Dict="gelu"      ,       _lowercase     :						Dict=0.1      ,       _lowercase     :						Any=0.1      ,       _lowercase     :						Union[str, Any]=512      ,       _lowercase     :						Optional[int]=2      ,       _lowercase     :						Union[str, Any]=0.02      ,       _lowercase     :						List[Any]=1E-12      ,       _lowercase     :						List[str]=0      ,       _lowercase     :						Dict="absolute"      ,       _lowercase     :						int = 0      ,       **_lowercase     :						int      ,       )    ->							int:
								super().__init__(pad_token_id=_lowercase      ,       **_lowercase)

								A_									= vocab_size
								A_									= hidden_size
								A_									= num_hidden_layers
								A_									= num_attention_heads
								A_									= hidden_act
								A_									= intermediate_size
								A_									= hidden_dropout_prob
								A_									= attention_probs_dropout_prob
								A_									= max_position_embeddings
								A_									= type_vocab_size
								A_									= initializer_range
								A_									= layer_norm_eps
								A_									= projection_dim
								A_									= position_embedding_type




