




'''simple docstring'''



import math
import random
from typing import Any

from .hill_climbing import SearchProblem




def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					bool = True      ,__UpperCamelCase:					float = math.inf      ,__UpperCamelCase:					float = -math.inf      ,__UpperCamelCase:					float = math.inf      ,__UpperCamelCase:					float = -math.inf      ,__UpperCamelCase:					bool = False      ,__UpperCamelCase:					float = 1_00      ,__UpperCamelCase:					float = 0.0_1      ,__UpperCamelCase:					float = 1      ,):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						False
      SCREAMING_SNAKE_CASE   :      Any    					=						search_prob
      SCREAMING_SNAKE_CASE   :      Any    					=						start_temperate
      SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
      SCREAMING_SNAKE_CASE   :      List[str]    					=						0
      SCREAMING_SNAKE_CASE   :      Tuple    					=						None

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

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(__UpperCamelCase    )      ,__UpperCamelCase    )
            plt.xlabel('Iterations'    )
            plt.ylabel('Function values'    )
            plt.show()
      return best_state


if __name__ == "__main__":




      def   lowercase__(			__UpperCamelCase:					Union[str, Any]      ,__UpperCamelCase:					Any    ):

            """simple docstring"""






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

      # starting the problem with initial coordinates (12, 47)
      UpperCamelCase_    	=				SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
      UpperCamelCase_    	=				simulated_annealing(
          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()}"""
      )

      # starting the problem with initial coordinates (12, 47)
      UpperCamelCase_    	=				SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
      UpperCamelCase_    	=				simulated_annealing(
          prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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__(			__UpperCamelCase:					str      ,__UpperCamelCase:					str    ):

            """simple docstring"""






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

      UpperCamelCase_    	=				SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      UpperCamelCase_    	=				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()}"""
      )

      UpperCamelCase_    	=				SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      UpperCamelCase_    	=				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 json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


_lowerCAmelCase							:   Any				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase							:   List[str]				=				{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase							:   List[str]				=				{
    "gpt2": 10_24,
    "gpt2-medium": 10_24,
    "gpt2-large": 10_24,
    "gpt2-xl": 10_24,
    "distilgpt2": 10_24,
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[Any]						=					VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE		:  int						=					PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE		:  Dict						=					GPTaTokenizer



      def __init__(	self	:      Optional[Any]	,      A	:      Optional[int]=None	,      A	:      List[Any]=None	,      A	:      Optional[int]=None	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Union[str, Any]="<|endoftext|>"	,      A	:      Optional[Any]="<|endoftext|>"	,      A	:      Optional[Any]=False	,      **A	:      str	,      ):
           super().__init__(
               A	,      A	,      tokenizer_file=A	,      unk_token=A	,      bos_token=A	,      eos_token=A	,      add_prefix_space=A	,      **A	,      )

           _UpperCAmelCase     :      List[Any]														=       kwargs.pop("add_bos_token"	,      A		)

           _UpperCAmelCase     :      List[str]														=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
           if pre_tok_state.get("add_prefix_space"	,      A		) != add_prefix_space:
                _UpperCAmelCase     :      Dict														=       getattr(A	,      pre_tok_state.pop("type"		)		)
                _UpperCAmelCase     :      Optional[Any]														=       add_prefix_space
                _UpperCAmelCase     :      Optional[Any]														=       pre_tok_class(**A		)

           _UpperCAmelCase     :      List[Any]														=       add_prefix_space



      def 		snake_case_   (	self	:      List[Any]	,      *A	:      List[Any]	,      **A	:      List[str]		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)
           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[Any]	,      *A	:      List[Any]	,      **A	:      int		):
           _UpperCAmelCase     :      Union[str, Any]														=       kwargs.get("is_split_into_words"	,      A		)

           assert self.add_prefix_space or not is_split_into_words, (
               f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*A	,      **A		)



      def 		snake_case_   (	self	:      Optional[int]	,      A	:      str	,      A	:      Optional[str] = None		):
           _UpperCAmelCase     :      List[Any]														=       self._tokenizer.model.save(A	,      name=A		)
           return tuple(A		)



      def 		snake_case_   (	self	:      int	,      A	:      "Conversation"		):
           _UpperCAmelCase     :      List[str]														=       []
           for is_user, text in conversation.iter_texts():
                input_ids.extend(self.encode(A	,      add_special_tokens=A		) + [self.eos_token_id]		)

           if len(A		) > self.model_max_length:
                _UpperCAmelCase     :      Optional[int]														=       input_ids[-self.model_max_length :]
           return input_ids
