"""simple docstring"""




import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
     import jieba

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


_a      :		Any           =   logging.get_logger(__name__)

_a      :		Dict           =   {'vocab_file': 'vocab.txt'}

_a      :		str           =   {
    'vocab_file': {
        'openbmb/cpm-ant-10b': 'https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt',
    },
}

_a      :		List[str]           =   {
    'openbmb/cpm-ant-10b': 1_024,
}





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]	)       ->      Tuple:
  _lowerCAmelCase						:       List[str]  			=     collections.OrderedDict()
  with open(_lowerCamelCase			,"""r"""			,encoding="""utf-8"""	) as reader:
    _lowerCAmelCase						:       Tuple  			=     reader.readlines()
  for index, token in enumerate(_lowerCamelCase	):
    _lowerCAmelCase						:       str  			=     token.rstrip("""\n"""	)
    _lowerCAmelCase						:       str  			=     index
  return vocab
class 						__A							( SCREAMING_SNAKE_CASE_		):






     def __init__(		self				,     a__				,     a__="<unk>"				,     a__=200  ):
       _lowerCAmelCase						:       Optional[Any]  			=     vocab
       _lowerCAmelCase						:       Any  			=     unk_token
       _lowerCAmelCase						:       int  			=     max_input_chars_per_word






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Any  			=     list(a__  )
       if len(a__  ) > self.max_input_chars_per_word:
         return [self.unk_token]

       _lowerCAmelCase						:       Union[str, Any]  			=     0
       _lowerCAmelCase						:       Dict  			=     []
       while start < len(a__  ):
         _lowerCAmelCase						:       Tuple  			=     len(a__  )
         _lowerCAmelCase						:       int  			=     None
         while start < end:
           _lowerCAmelCase						:       str  			=     """""".join(chars[start:end]  )
           if substr in self.vocab:
             _lowerCAmelCase						:       int  			=     substr
             break
           end -= 1
         if cur_substr is None:
           sub_tokens.append(self.unk_token  )
           start += 1
         else:
           sub_tokens.append(a__  )
           _lowerCAmelCase						:       Union[str, Any]  			=     end

       return sub_tokens







class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Optional[int]							=		VOCAB_FILES_NAMES
     _UpperCamelCase							:			List[Any]							=		PRETRAINED_VOCAB_FILES_MAP
     _UpperCamelCase							:			Optional[int]							=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _UpperCamelCase							:			List[str]							=		["input_ids", "attention_mask"]
     _UpperCamelCase							:			str							=		False






     def __init__(		self				,     a__				,     a__="<d>"				,     a__="</d>"				,     a__="<s>"				,     a__="</s>"				,     a__="<pad>"				,     a__="<unk>"				,     a__="</n>"				,     a__="</_>"				,     a__="left"				,     **a__				,     ):
       requires_backends(self				,     ["""jieba"""]  )
       super().__init__(
           bod_token=a__				,     eod_token=a__				,     bos_token=a__				,     eos_token=a__				,     pad_token=a__				,     unk_token=a__				,     line_token=a__				,     space_token=a__				,     padding_side=a__				,     **a__				,     )
       _lowerCAmelCase						:       Union[str, Any]  			=     bod_token
       _lowerCAmelCase						:       List[Any]  			=     eod_token
       _lowerCAmelCase						:       List[str]  			=     load_vocab(a__  )
       _lowerCAmelCase						:       Tuple  			=     self.encoder[space_token]
       _lowerCAmelCase						:       Optional[Any]  			=     self.encoder[line_token]

       del self.encoder[space_token]
       del self.encoder[line_token]

       _lowerCAmelCase						:       Any  			=     collections.OrderedDict(sorted(self.encoder.items()				,     key=lambda a__  : x[1]  )  )
       _lowerCAmelCase						:       int  			=     {v: k for k, v in self.encoder.items()}

       _lowerCAmelCase						:       Optional[int]  			=     WordpieceTokenizer(vocab=self.encoder				,     unk_token=self.unk_token  )






     @property
     def     __A  (		self  ):
       return self.encoder[self.bod_token]






     @property
     def     __A  (		self  ):
       return self.encoder[self.eod_token]






     @property
     def     __A  (		self  ):
       return self.encoder["\n"]






     @property
     def     __A  (		self  ):
       return len(self.encoder  )






     def     __A  (		self  ):
       return dict(self.encoder				,     **self.added_tokens_encoder  )






     def     __A  (		self				,     a__  ):
       _lowerCAmelCase						:       Dict  			=     []
       for x in jieba.cut(a__				,     cut_all=a__  ):
         output_tokens.extend(self.wordpiece_tokenizer.tokenize(a__  )  )
       return output_tokens






     def     __A  (		self				,     a__				,     **a__  ):
       _lowerCAmelCase						:       Any  			=     [i for i in token_ids if i >= 0]
       _lowerCAmelCase						:       str  			=     [
           x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
       ]
       return super()._decode(a__				,     **a__  )






     def     __A  (		self				,     a__  ):
       return token in self.encoder






     def     __A  (		self				,     a__  ):
       return "".join(a__  )






     def     __A  (		self				,     a__  ):
       return self.encoder.get(a__				,     self.encoder.get(self.unk_token  )  )






     def     __A  (		self				,     a__  ):
       return self.decoder.get(a__				,     self.unk_token  )






     def     __A  (		self				,     a__				,     a__ = None  ):
       if os.path.isdir(a__  ):
         _lowerCAmelCase						:       int  			=     os.path.join(
             a__				,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]  )
       else:
         _lowerCAmelCase						:       List[Any]  			=     (filename_prefix + """-""" if filename_prefix else """""") + save_directory
       _lowerCAmelCase						:       Any  			=     0
       if " " in self.encoder:
         _lowerCAmelCase						:       int  			=     self.encoder[""" """]
         del self.encoder[" "]
       if "\n" in self.encoder:
         _lowerCAmelCase						:       int  			=     self.encoder["""\n"""]
         del self.encoder["\n"]
       _lowerCAmelCase						:       List[Any]  			=     collections.OrderedDict(sorted(self.encoder.items()				,     key=lambda a__  : x[1]  )  )
       with open(a__				,     """w"""				,     encoding="""utf-8"""  ) as writer:
         for token, token_index in self.encoder.items():
           if index != token_index:
             logger.warning(
                 F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                 """ Please check that the vocabulary is not corrupted!"""  )
             _lowerCAmelCase						:       List[str]  			=     token_index
           writer.write(token + """\n"""  )
           index += 1
       return (vocab_file,)






     def     __A  (		self				,     a__				,     a__ = None  ):
       if token_ids_a is None:
         return [self.bos_token_id] + token_ids_a
       return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a






     def     __A  (		self				,     a__				,     a__ = None				,     a__ = False  ):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=a__				,     token_ids_a=a__				,     already_has_special_tokens=a__  )

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








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()}'
    )


