

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mpnet import MPNetTokenizer


_a	 =     logging.get_logger(__name__)

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

_a	 =     {
    '''vocab_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt''',
    },
    '''tokenizer_file''': {
        '''microsoft/mpnet-base''': '''https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json''',
    },
}

_a	 =     {
    '''microsoft/mpnet-base''': 5_1_2,
}

_a	 =     {
    '''microsoft/mpnet-base''': {'''do_lower_case''': True},
}






class 							A_				(	snake_case__      ):
     _lowercase    :  List[str]          =			VOCAB_FILES_NAMES
     _lowercase    :  Optional[Any]          =			PRETRAINED_VOCAB_FILES_MAP
     _lowercase    :  Optional[Any]          =			PRETRAINED_INIT_CONFIGURATION
     _lowercase    :  List[str]          =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowercase    :  int          =			MPNetTokenizer
     _lowercase    :  List[str]          =			['input_ids', 'attention_mask']


     def __init__(							self    :	Tuple    ,						UpperCAmelCase    :	str=None    ,						UpperCAmelCase    :	List[Any]=None    ,						UpperCAmelCase    :	int=True    ,						UpperCAmelCase    :	int="<s>"    ,						UpperCAmelCase    :	Union[str, Any]="</s>"    ,						UpperCAmelCase    :	Tuple="</s>"    ,						UpperCAmelCase    :	Union[str, Any]="<s>"    ,						UpperCAmelCase    :	List[Any]="[UNK]"    ,						UpperCAmelCase    :	Dict="<pad>"    ,						UpperCAmelCase    :	Any="<mask>"    ,						UpperCAmelCase    :	str=True    ,						UpperCAmelCase    :	int=None    ,						**UpperCAmelCase    :	Tuple    ,						)							->			Tuple:
      super().__init__(
          UpperCAmelCase    ,						tokenizer_file=UpperCAmelCase    ,						do_lower_case=UpperCAmelCase    ,						bos_token=UpperCAmelCase    ,						eos_token=UpperCAmelCase    ,						sep_token=UpperCAmelCase    ,						cls_token=UpperCAmelCase    ,						unk_token=UpperCAmelCase    ,						pad_token=UpperCAmelCase    ,						mask_token=UpperCAmelCase    ,						tokenize_chinese_chars=UpperCAmelCase    ,						strip_accents=UpperCAmelCase    ,						**UpperCAmelCase    ,						)

      __lowerCAmelCase:     Dict     			=   json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
      if (
          pre_tok_state.get('lowercase'    ,						UpperCAmelCase      ) != do_lower_case
          or pre_tok_state.get('strip_accents'    ,						UpperCAmelCase      ) != strip_accents
      ):
       __lowerCAmelCase:     List[Any]     			=   getattr(UpperCAmelCase    ,						pre_tok_state.pop('type'      )      )
       __lowerCAmelCase:     str     			=   do_lower_case
       __lowerCAmelCase:     Dict     			=   strip_accents
       __lowerCAmelCase:     List[str]     			=   pre_tok_class(**UpperCAmelCase      )

      __lowerCAmelCase:     Union[str, Any]     			=   do_lower_case


     @property
     def 						UpperCAmelCase						(							self    :	Tuple      )							->			str:
      if self._mask_token is None:
       if self.verbose:
        logger.error('Using mask_token, but it is not set yet.'      )
       return None
      return str(self._mask_token      )


     @mask_token.setter
     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	str      )							->			Dict:
      __lowerCAmelCase:     str     			=   AddedToken(UpperCAmelCase    ,						lstrip=UpperCAmelCase    ,						rstrip=UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ) else value
      __lowerCAmelCase:     int     			=   value


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	Optional[int]    ,						UpperCAmelCase    :	int=None      )							->			Dict:
      __lowerCAmelCase:     Union[str, Any]     			=   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
      if token_ids_a is None:
       return output

      return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]


     def 						UpperCAmelCase						(							self    :	Dict    ,						UpperCAmelCase    :	List[int]    ,						UpperCAmelCase    :	Optional[List[int]] = None      )							->			List[int]:
      __lowerCAmelCase:     List[Any]     			=   [self.sep_token_id]
      __lowerCAmelCase:     Optional[Any]     			=   [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 + sep + token_ids_a + sep      ) * [0]


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	Optional[str] = None      )							->			Tuple[str]:
      __lowerCAmelCase:     List[Any]     			=   self._tokenizer.model.save(UpperCAmelCase    ,						name=UpperCAmelCase      )
      return tuple(UpperCAmelCase      )








import math






class 							A_				:


     def __init__(							self    :	Any    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	Any      )							->			Any:
      __lowerCAmelCase:     Optional[Any]     			=   x
      __lowerCAmelCase:     Any     			=   y
      __lowerCAmelCase:     Tuple     			=   step_size
      __lowerCAmelCase:     Dict     			=   function_to_optimize


     def 						UpperCAmelCase						(							self    :	Dict      )							->			int:
      return self.function(self.x    ,						self.y      )


     def 						UpperCAmelCase						(							self    :	str      )							->			int:
      __lowerCAmelCase:     Union[str, Any]     			=   self.step_size
      return [
          SearchProblem(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						self.function      )
          for x, y in (
              (self.x - step_size, self.y - step_size),
              (self.x - step_size, self.y),
              (self.x - step_size, self.y + step_size),
              (self.x, self.y - step_size),
              (self.x, self.y + step_size),
              (self.x + step_size, self.y - step_size),
              (self.x + step_size, self.y),
              (self.x + step_size, self.y + step_size),
          )
      ]


     def __hash__(							self    :	Optional[Any]      )							->			int:
      return hash(str(self      )      )


     def __eq__(							self    :	Dict    ,						UpperCAmelCase    :	Tuple      )							->			Any:
      if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ):
       return hash(str(self      )      ) == hash(str(UpperCAmelCase      )      )
      return False


     def __str__(							self    :	Optional[Any]      )							->			Dict:
      return F'''x: {self.x} y: {self.y}'''







def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    bool = True					,      SCREAMING_SNAKE_CASE	:    float = math.inf					,      SCREAMING_SNAKE_CASE	:    float = -math.inf					,      SCREAMING_SNAKE_CASE	:    float = math.inf					,      SCREAMING_SNAKE_CASE	:    float = -math.inf					,      SCREAMING_SNAKE_CASE	:    bool = False					,      SCREAMING_SNAKE_CASE	:    int = 1_00_00					,      )					->					SearchProblem:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   search_prob
 __lowerCAmelCase:     str     			=   []  # list to store the current score at each iteration
 __lowerCAmelCase:     Optional[int]     			=   0
 __lowerCAmelCase:     int     			=   False
 __lowerCAmelCase:     Any     			=   set()
 while not solution_found and iterations < max_iter:
  visited.add(SCREAMING_SNAKE_CASE	)
  iterations += 1
  __lowerCAmelCase:     int     			=   current_state.score()
  scores.append(SCREAMING_SNAKE_CASE	)
  __lowerCAmelCase:     Any     			=   current_state.get_neighbors()
  __lowerCAmelCase:     Tuple     			=   -math.inf
  __lowerCAmelCase:     Any     			=   math.inf
  __lowerCAmelCase:     Dict     			=   None  # to hold the next best neighbor
  for neighbor in neighbors:
   if neighbor in visited:
    continue  # do not want to visit the same state again
   if (
       neighbor.x > max_x
       or neighbor.x < min_x
       or neighbor.y > max_y
       or neighbor.y < min_y
   ):
    continue  # neighbor outside our bounds
   __lowerCAmelCase:     List[str]     			=   neighbor.score() - current_score
   if find_max:  # finding max
    # going to direction with greatest ascent
    if change > max_change and change > 0:
     __lowerCAmelCase:     int     			=   change
     __lowerCAmelCase:     int     			=   neighbor
   else:  # finding min
    # to direction with greatest descent
    if change < min_change and change < 0:
     __lowerCAmelCase:     str     			=   change
     __lowerCAmelCase:     str     			=   neighbor
  if next_state is not None:
   # we found at least one neighbor which improved the current state
   __lowerCAmelCase:     Optional[Any]     			=   next_state
  else:
   # since we have no neighbor that improves the solution we stop the search
   __lowerCAmelCase:     Dict     			=   True

 if visualization:
  from matplotlib import pyplot as plt

  plt.plot(range(SCREAMING_SNAKE_CASE	)					,      SCREAMING_SNAKE_CASE	)
  plt.xlabel('Iterations'	)
  plt.ylabel('Function values'	)
  plt.show()

 return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()







  def _a				( SCREAMING_SNAKE_CASE	:    List[str]					,      SCREAMING_SNAKE_CASE	:    List[str]	)					->					Optional[Any]:

   """simple docstring"""






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

  # starting the problem with initial coordinates (3, 4)
  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=False)
  print(
      '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      f"{local_min.score()}"
  )

  # starting the problem with initial coordinates (12, 47)
  _a	 =     SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(
      prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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()}"
  )







  def _a				( SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    List[Any]	)					->					Dict:

   """simple docstring"""






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

  _a	 =     SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  _a	 =     hill_climbing(prob, find_max=True)



  print(
      '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
      f"{local_min.score()}"
  )





