





import math
import random
from typing import Any

from .hill_climbing import SearchProblem



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase = True							,     _lowerCAmelCase = math.inf							,     _lowerCAmelCase = -math.inf							,     _lowerCAmelCase = math.inf							,     _lowerCAmelCase = -math.inf							,     _lowerCAmelCase = False							,     _lowerCAmelCase = 100							,     _lowerCAmelCase = 0.01							,     _lowerCAmelCase = 1							,     )		->				Any:
 UpperCamelCase			:      int 			=     False
 UpperCamelCase			:      Tuple 			=     search_prob
 UpperCamelCase			:      str 			=     start_temperate
 UpperCamelCase			:      Any 			=     []
 UpperCamelCase			:      str 			=     0
 UpperCamelCase			:      str 			=     None

 while not search_end:
  UpperCamelCase			:      Any 			=     current_state.score()
  if best_state is None or current_score > best_state.score():
   UpperCamelCase			:      List[Any] 			=     current_state
  scores.append(_lowerCAmelCase  )
  iterations += 1
  UpperCamelCase			:      List[str] 			=     None
  UpperCamelCase			:      Dict 			=     current_state.get_neighbors()
  while (
      next_state is None and neighbors
  ):  # till we do not find a neighbor that we can move to
   UpperCamelCase			:      List[Any] 			=     random.randint(0							,     len(_lowerCAmelCase  ) - 1  )  # picking a random neighbor
   UpperCamelCase			:      Dict 			=     neighbors.pop(_lowerCAmelCase  )
   UpperCamelCase			:      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:
    UpperCamelCase			:      int 			=     change * -1  # in case we are finding minimum
   if change > 0:  # improves the solution
    UpperCamelCase			:      List[str] 			=     picked_neighbor
   else:
    UpperCamelCase			:      List[str] 			=     (math.e) ** (
        change / current_temp
    )  # probability generation function
    if random.random() < probability:  # random number within probability
     UpperCamelCase			:      Optional[Any] 			=     picked_neighbor
  UpperCamelCase			:      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
   UpperCamelCase			:      Dict 			=     True
  else:
   UpperCamelCase			:      List[str] 			=     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							,     _lowerCAmelCase  )		->				List[Any]:
       return (x**2) + (y**2)

      # starting the problem with initial coordinates (12, 47)
      __lowerCamelCase :  Optional[int]										=  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase :  Union[str, 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)
      __lowerCamelCase :  List[Any]										=  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase :  List[Any]										=  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							,     _lowerCAmelCase  )		->				Any:
       return (3 * x**2) - (6 * y)

      __lowerCamelCase :  int										=  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase :  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()}"""
      )

      __lowerCamelCase :  Tuple										=  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
      __lowerCamelCase :  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()}"""
      )







from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


__lowerCamelCase :  Dict										=  logging.get_logger(__name__)

__lowerCamelCase :  Union[str, Any]										=  {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

__lowerCamelCase :  Dict										=  {
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

__lowerCamelCase :  Tuple										=  {
    """facebook/blenderbot_small-90M""": 512,
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     VOCAB_FILES_NAMES
      _UpperCAmelCase			:Dict							=     PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase			:List[Any]							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase			:Optional[Any]							=     BlenderbotSmallTokenizer





      def __init__(				self ,							A_=None ,							A_=None ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_="<|endoftext|>" ,							A_=False ,							A_=True ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(
           ByteLevelBPETokenizer(
               vocab=A_ ,							merges=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_ ,							) ,							bos_token=A_ ,							eos_token=A_ ,							unk_token=A_ ,							**A_ ,							)
       UpperCamelCase			:      Union[str, Any] 			=     add_prefix_space





      def  __UpperCamelCase(				self ,							A_ ,							A_=None     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     [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 ,							A_ ,							A_ = None     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     [self.sep_token_id]
       UpperCamelCase			:      int 			=     [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]
