


"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


__snake_case		=       logging.get_logger(__name__)

__snake_case		=       {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case		=       {
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

__snake_case		=       {'''mobilebert-uncased''': 512}


__snake_case		=       {}


class     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Optional[int]    =		VOCAB_FILES_NAMES
     A_       :	List[Any]    =		PRETRAINED_VOCAB_FILES_MAP
     A_       :	Dict    =		PRETRAINED_INIT_CONFIGURATION
     A_       :	Union[str, Any]    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_       :	Optional[int]    =		MobileBertTokenizer


     def __init__(      self    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=None    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase="[UNK]"    ,				__UpperCAmelCase="[SEP]"    ,				__UpperCAmelCase="[PAD]"    ,				__UpperCAmelCase="[CLS]"    ,				__UpperCAmelCase="[MASK]"    ,				__UpperCAmelCase=True    ,				__UpperCAmelCase=None    ,				**__UpperCAmelCase    ,				)       ->							List[str]:
          super().__init__(
              __UpperCAmelCase    ,				tokenizer_file=__UpperCAmelCase    ,				do_lower_case=__UpperCAmelCase    ,				unk_token=__UpperCAmelCase    ,				sep_token=__UpperCAmelCase    ,				pad_token=__UpperCAmelCase    ,				cls_token=__UpperCAmelCase    ,				mask_token=__UpperCAmelCase    ,				tokenize_chinese_chars=__UpperCAmelCase    ,				strip_accents=__UpperCAmelCase    ,				**__UpperCAmelCase    ,				)

          _a    	=				json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
          if (
              normalizer_state.get('''lowercase'''    ,				__UpperCAmelCase							) != do_lower_case
              or normalizer_state.get('''strip_accents'''    ,				__UpperCAmelCase							) != strip_accents
              or normalizer_state.get('''handle_chinese_chars'''    ,				__UpperCAmelCase							) != tokenize_chinese_chars
          ):
               _a    	=				getattr(__UpperCAmelCase    ,				normalizer_state.pop('''type'''							)							)
               _a    	=				do_lower_case
               _a    	=				strip_accents
               _a    	=				tokenize_chinese_chars
               _a    	=				normalizer_class(**__UpperCAmelCase							)

          _a    	=				do_lower_case


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=None							)       ->							Optional[Any]:
          _a    	=				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

          if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

          return output


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							List[int]:
          _a    	=				[self.sep_token_id]
          _a    	=				[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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase = None							)       ->							Tuple[str]:
          _a    	=				self._tokenizer.model.save(__UpperCAmelCase    ,				name=__UpperCAmelCase							)
          return tuple(__UpperCAmelCase							)



"""simple docstring"""






import math
import random
from typing import Any

from .hill_climbing import SearchProblem
def       A_   (   _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 = 1_00,     _lowerCAmelCase     :					float = 0.0_1,     _lowerCAmelCase     :					float = 1,     ):






     """simple docstring"""

     _a    	=				False
     _a    	=				search_prob
     _a    	=				start_temperate
     _a    	=				[]
     _a    	=				0
     _a    	=				None

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






        """simple docstring"""

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

   # starting the problem with initial coordinates (12, 47)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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)
   __snake_case		=       SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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_   (   _lowerCAmelCase     :					Optional[Any],     _lowerCAmelCase     :					Optional[int]       ):






        """simple docstring"""

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

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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()}'
   )

   __snake_case		=       SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
   __snake_case		=       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()}'
   )