





'''simple docstring'''

import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def lowerCamelCase						(     __lowerCamelCase :    Dict					,       __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:
    _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(__lowerCamelCase    )
        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(__lowerCamelCase    ) - 1    )  # picking a random neighbor
            _SCREAMING_SNAKE_CASE									=			neighbors.pop(__lowerCamelCase    )
            _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(__lowerCamelCase    )					,       __lowerCamelCase    )
        plt.xlabel("""Iterations"""    )
        plt.ylabel("""Function values"""    )
        plt.show()
    return best_state


if __name__ == "__main__":





      def lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    List[str]    )						->Optional[Any]:
          return (x**2) + (y**2)

      # starting the problem with initial coordinates (12, 47)
      lowercase_					= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowercase_					= 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)
      lowercase_					= SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      lowercase_					= 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 lowerCamelCase						(     __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    int    )						->int:
          return (3 * x**2) - (6 * y)

      lowercase_					= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowercase_					= 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()}"""
      )

      lowercase_					= SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      lowercase_					= 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


lowercase_					= logging.get_logger(__name__)

lowercase_					= {
    """facebook/data2vec-text-base""": """https://huggingface.co/data2vec/resolve/main/config.json""",
}


class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = '''data2vec-text'''


    def __init__(		self ,		A=3_0522 ,		A=768 ,		A=12 ,		A=12 ,		A=3072 ,		A="gelu" ,		A=0.1 ,		A=0.1 ,		A=512 ,		A=2 ,		A=0.02 ,		A=1e-12 ,		A=1 ,		A=0 ,		A=2 ,		A="absolute" ,		A=True ,		A=None ,		**A ,		)		->   int:
        super().__init__(pad_token_id=A ,		bos_token_id=A ,		eos_token_id=A ,		**A       )

        _SCREAMING_SNAKE_CASE									=			vocab_size
        _SCREAMING_SNAKE_CASE									=			hidden_size
        _SCREAMING_SNAKE_CASE									=			num_hidden_layers
        _SCREAMING_SNAKE_CASE									=			num_attention_heads
        _SCREAMING_SNAKE_CASE									=			hidden_act
        _SCREAMING_SNAKE_CASE									=			intermediate_size
        _SCREAMING_SNAKE_CASE									=			hidden_dropout_prob
        _SCREAMING_SNAKE_CASE									=			attention_probs_dropout_prob
        _SCREAMING_SNAKE_CASE									=			max_position_embeddings
        _SCREAMING_SNAKE_CASE									=			type_vocab_size
        _SCREAMING_SNAKE_CASE									=			initializer_range
        _SCREAMING_SNAKE_CASE									=			layer_norm_eps
        _SCREAMING_SNAKE_CASE									=			position_embedding_type
        _SCREAMING_SNAKE_CASE									=			use_cache
        _SCREAMING_SNAKE_CASE									=			classifier_dropout




class 					a_     (				snake_case_       ):

    '''simple docstring'''


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



