





'''simple docstring'''




import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     :Tuple       =				{'''vocab_file''': '''sentencepiece.model'''}

__SCREAMING_SNAKE_CASE     :Union[str, Any]       =				{
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
}

__SCREAMING_SNAKE_CASE     :List[Any]       =				{
    '''google/rembert''': 256,
}







class   A_	(  lowerCAmelCase_			):
 _lowerCamelCase  : Optional[Any]           =							VOCAB_FILES_NAMES
 _lowerCamelCase  : Union[str, Any]           =							PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  : Any           =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




 def __init__(					self       :    List[str]	,			snake_case_       :    Tuple	,			snake_case_       :    Any=False	,			snake_case_       :    Tuple=True	,			snake_case_       :    Tuple=True	,			snake_case_       :    int="[CLS]"	,			snake_case_       :    Optional[Any]="[SEP]"	,			snake_case_       :    int="[UNK]"	,			snake_case_       :    Optional[int]="[SEP]"	,			snake_case_       :    Any="[PAD]"	,			snake_case_       :    Any="[CLS]"	,			snake_case_       :    int="[MASK]"	,			**snake_case_       :    Optional[int]	,			):
      super().__init__(
          do_lower_case=snake_case_	,			remove_space=snake_case_	,			keep_accents=snake_case_	,			bos_token=snake_case_	,			eos_token=snake_case_	,			unk_token=snake_case_	,			sep_token=snake_case_	,			pad_token=snake_case_	,			cls_token=snake_case_	,			mask_token=snake_case_	,			**snake_case_	,			)

      _UpperCAmelCase     							=   do_lower_case
      _UpperCAmelCase     							=   remove_space
      _UpperCAmelCase     							=   keep_accents
      _UpperCAmelCase     							=   vocab_file

      _UpperCAmelCase     							=   spm.SentencePieceProcessor()
      self.sp_model.Load(snake_case_    )




 @property
 def      lowercase	(					self       :    Union[str, Any]    ):
      return len(self.sp_model    )




 def      lowercase	(					self       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   {self.convert_ids_to_tokens(snake_case_    ): i for i in range(self.vocab_size    )}
      vocab.update(self.added_tokens_encoder    )
      return vocab




 def __getstate__(					self       :    Tuple    ):
      _UpperCAmelCase     							=   self.__dict__.copy()
      _UpperCAmelCase     							=   None
      return state




 def __setstate__(					self       :    Tuple	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   d
      _UpperCAmelCase     							=   spm.SentencePieceProcessor()
      self.sp_model.Load(self.vocab_file    )




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Union[str, Any]	,			snake_case_       :    str=False    ):
      _UpperCAmelCase     							=   self.sp_model.EncodeAsPieces(snake_case_    )
      return pieces




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    Optional[Any]    ):
      return self.sp_model.PieceToId(snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Tuple    ):
      return self.sp_model.IdToPiece(snake_case_    )




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Union[str, Any]    ):
      _UpperCAmelCase     							=   self.sp_model.decode_pieces(snake_case_    )
      return out_string




 def      lowercase	(					self       :    Union[str, Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]
      if token_ids_a is None:
           return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None	,			snake_case_       :    bool = False    ):

      if already_has_special_tokens:
           if token_ids_a is not None:
                raise ValueError(
                    "You should not supply a second sequence if the provided sequence of "
                    "ids is already formatted with special tokens for the model."    )
           return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

      if token_ids_a is not None:
           return [1] + ([0] * len(snake_case_    )) + [1] + ([0] * len(snake_case_    )) + [1]
      return [1] + ([0] * len(snake_case_    )) + [1]




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    List[int]	,			snake_case_       :    Optional[List[int]] = None    ):
      _UpperCAmelCase     							=   [self.sep_token_id]
      _UpperCAmelCase     							=   [self.cls_token_id]

      if token_ids_a is None:
           return len(cls + token_ids_a + sep    ) * [0]
      return len(cls + token_ids_a + sep    ) * [0] + len(token_ids_a + sep    ) * [1]




 def      lowercase	(					self       :    Tuple	,			snake_case_       :    str	,			snake_case_       :    Optional[str] = None    ):
      if not os.path.isdir(snake_case_    ):
           logger.error("Vocabulary path ({}) should be a directory".format(snake_case_    )    )
           return
      _UpperCAmelCase     							=   os.path.join(
          snake_case_	,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]    )

      if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case_    ):
           copyfile(self.vocab_file	,			snake_case_    )

      return (out_vocab_file,)








import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase_		(		_A			:    Optional[int]    ,					_A			:    bool = True    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    bool = False    ,					_A			:    float = 100    ,					_A			:    float = 0.01    ,					_A			:    float = 1    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  False
      lowerCamelCase__		:				Optional[Any]    =  search_prob
      lowerCamelCase__		:				List[str]    =  start_temperate
      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				Any    =  0
      lowerCamelCase__		:				Optional[Any]    =  None

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

      if visualization:
            from matplotlib import pyplot as plt

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


if __name__ == "__main__":

    def       lowercase_		(		_A			:    List[str]    ,					_A			:    int						):

          """simple docstring"""




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

    # starting the problem with initial coordinates (12, 47)
    A      :    Any		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    Dict		=					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      :    int		=					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_		(		_A			:    int    ,					_A			:    Any						):

          """simple docstring"""




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

    A      :    Optional[int]		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Union[str, Any]		=					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      :    Any		=					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()}'
    )


