

import math
import random
from typing import Any

from .hill_climbing import SearchProblem







def       lowercase					(	__A :					Any ,    __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 ,    )				->			Any:





    '''simple docstring'''




    snake_case							:		Tuple        =		False
    snake_case							:		List[Any]        =		search_prob
    snake_case							:		Union[str, Any]        =		start_temperate
    snake_case							:		Optional[Any]        =		[]
    snake_case							:		Optional[int]        =		0
    snake_case							:		Any        =		None

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





         '''simple docstring'''




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

     # starting the problem with initial coordinates (12, 47)
     __lowercase     : Union[str, Any]       =						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
     __lowercase     : List[Any]       =						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     : List[Any]       =						SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
     __lowercase     : str       =						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 :					str ,    __A :					Any		)				->			Any:





         '''simple docstring'''




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

     __lowercase     : Any       =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     __lowercase     : Optional[int]       =						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     : Any       =						SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     __lowercase     : List[str]       =						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"""




import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


_A     :			Tuple  						=			logging.get_logger(__name__)

_A     :			Dict  						=			{"""vocab_file""": """vocab.txt"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """facebook/esm2_t6_8M_UR50D""": """https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt""",
        """facebook/esm2_t12_35M_UR50D""": """https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt""",
    },
}

_A     :			Optional[Any]  						=			{
    """facebook/esm2_t6_8M_UR50D""": 10_24,
    """facebook/esm2_t12_35M_UR50D""": 10_24,
}


def     __magic_name__      ( __snake_case							:			Tuple    )					->							str:
       with open(__snake_case	,					"r"    ) as f:
              lowercase       :							Dict									=				f.read().splitlines()
              return [l.strip() for l in lines]

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   def __init__(     self      ,       _a      ,       _a="<unk>"      ,       _a="<cls>"      ,       _a="<pad>"      ,       _a="<mask>"      ,       _a="<eos>"      ,       **_a      ,       ):
          super().__init__(**_a							)
          lowercase       :							Union[str, Any]									=				load_vocab_file(_a							)
          lowercase       :							Union[str, Any]									=				dict(enumerate(self.all_tokens							)							)
          lowercase       :							Any									=				{tok: ind for ind, tok in enumerate(self.all_tokens							)}
          lowercase       :							Dict									=				unk_token
          lowercase       :							Optional[int]									=				cls_token
          lowercase       :							Optional[int]									=				pad_token
          lowercase       :							Any									=				mask_token
          lowercase       :							List[str]									=				eos_token
          lowercase       :							Tuple									=				self.all_tokens
          self._create_trie(self.unique_no_split_tokens							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          return text.split()
   def      __magic_name__     (     self      ,       _a=False							):
          return len(self._id_to_token							)
   def      __magic_name__     (     self							):
          return {token: i for i, token in enumerate(self.all_tokens							)}
   def      __magic_name__     (     self      ,       _a							):
          return self._token_to_id.get(_a      ,       self._token_to_id.get(self.unk_token							)							)
   def      __magic_name__     (     self      ,       _a							):
          return self._id_to_token.get(_a      ,       self.unk_token							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				[self.cls_token_id]
          lowercase       :							Dict									=				[self.eos_token_id]  # No sep token in ESM vocabulary
          if token_ids_a is None:
                 if self.eos_token_id is None:
                        return cls + token_ids_a
                 else:
                        return cls + token_ids_a + sep
          elif self.eos_token_id is None:
                 raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!"							)
          return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token
   def      __magic_name__     (     self      ,       _a      ,       _a = None      ,       _a = 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 token in self.all_special_ids else 0 for token in token_ids_a]
          lowercase       :							Union[str, Any]									=				[1] + ([0] * len(_a							)) + [1]
          if token_ids_a is not None:
                 mask += [0] * len(_a							) + [1]
          return mask
   def      __magic_name__     (     self      ,       _a      ,       _a							):
          lowercase       :							Optional[Any]									=				os.path.join(_a      ,       (filename_prefix + "-" if filename_prefix else "") + "vocab.txt"							)
          with open(_a      ,       "w"							) as f:
                 f.write("\n".join(self.all_tokens							)							)
          return (vocab_file,)
   @property
   def      __magic_name__     (     self							):
          return self.get_vocab_size(with_added_tokens=_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a = False							):
          return super()._add_tokens(_a      ,       special_tokens=_a							)


