'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem

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





       """simple docstring"""


       __UpperCAmelCase  :				List[Any]       				=		False
       __UpperCAmelCase  :				List[str]       				=		search_prob
       __UpperCAmelCase  :				Any       				=		start_temperate
       __UpperCAmelCase  :				Any       				=		[]
       __UpperCAmelCase  :				List[str]       				=		0
       __UpperCAmelCase  :				Union[str, Any]       				=		None

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





          """simple docstring"""


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

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





          """simple docstring"""


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

   _a   :	int 							=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _a   :	Optional[int] 							=   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   :	Optional[int] 							=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   _a   :	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 collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a   :	List[str] 							=   logging.get_logger(__name__)

_a   :	Any 							=   {
    "kssteven/ibert-roberta-base": "https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json",
    "kssteven/ibert-roberta-large": "https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json",
    "kssteven/ibert-roberta-large-mnli": (
        "https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json"
    ),
}






class 		__A       (__magic_name__   ):
       snake_case       :Union[str, Any]				    =					"ibert"






       def __init__(      self				,     UpperCamelCase_=3_05_22				,     UpperCamelCase_=7_68				,     UpperCamelCase_=12				,     UpperCamelCase_=12				,     UpperCamelCase_=30_72				,     UpperCamelCase_="gelu"				,     UpperCamelCase_=0.1				,     UpperCamelCase_=0.1				,     UpperCamelCase_=5_12				,     UpperCamelCase_=2				,     UpperCamelCase_=0.0_2				,     UpperCamelCase_=1E-12				,     UpperCamelCase_=1				,     UpperCamelCase_=0				,     UpperCamelCase_=2				,     UpperCamelCase_="absolute"				,     UpperCamelCase_=False				,     UpperCamelCase_="none"				,     **UpperCamelCase_				,     ):
              super().__init__(pad_token_id=UpperCamelCase_				,     bos_token_id=UpperCamelCase_				,     eos_token_id=UpperCamelCase_				,     **UpperCamelCase_      )

              __UpperCAmelCase  :				List[Any]       				=		vocab_size
              __UpperCAmelCase  :				Optional[Any]       				=		hidden_size
              __UpperCAmelCase  :				List[Any]       				=		num_hidden_layers
              __UpperCAmelCase  :				Any       				=		num_attention_heads
              __UpperCAmelCase  :				List[str]       				=		hidden_act
              __UpperCAmelCase  :				List[str]       				=		intermediate_size
              __UpperCAmelCase  :				Optional[int]       				=		hidden_dropout_prob
              __UpperCAmelCase  :				Union[str, Any]       				=		attention_probs_dropout_prob
              __UpperCAmelCase  :				str       				=		max_position_embeddings
              __UpperCAmelCase  :				List[str]       				=		type_vocab_size
              __UpperCAmelCase  :				Dict       				=		initializer_range
              __UpperCAmelCase  :				Optional[int]       				=		layer_norm_eps
              __UpperCAmelCase  :				Any       				=		position_embedding_type
              __UpperCAmelCase  :				Tuple       				=		quant_mode
              __UpperCAmelCase  :				Union[str, Any]       				=		force_dequant






class 		__A       (__magic_name__   ):






       @property
       def 			_snake_case						(      self      ):
              if self.task == "multiple-choice":
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     __UpperCAmelCase  :				Optional[int]       				=		{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]      )






