


import os
import unittest

from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir

from ...test_tokenization_common import TokenizerTesterMixin


A      :    List[str]		=					get_tests_dir("fixtures/test_sentencepiece_bpe.model")







class 			_lowercase (							lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			BartphoTokenizer
       A__											=			False
       A__											=			True





       def 						lowerCAmelCase     (   self							:			Any       ):


             '''simple docstring'''



             super().setUp()

             lowerCamelCase__		:				Tuple    =  ["▁This", "▁is", "▁a", "▁t", "est"]
             lowerCamelCase__		:				Union[str, Any]    =  dict(zip(__lowerCamelCase					,      range(len(__lowerCamelCase       )       )       )       )
             lowerCamelCase__		:				List[str]    =  {"unk_token": "<unk>"}

             lowerCamelCase__		:				Optional[int]    =  os.path.join(self.tmpdirname					,      VOCAB_FILES_NAMES["monolingual_vocab_file"]       )
             with open(self.monolingual_vocab_file					,      "w"					,      encoding="utf-8"       ) as fp:
                   for token in vocab_tokens:
                         fp.write(f"{token} {vocab_tokens[token]}\n"       )

             lowerCamelCase__		:				str    =  BartphoTokenizer(__lowerCamelCase					,      self.monolingual_vocab_file					,      **self.special_tokens_map       )
             tokenizer.save_pretrained(self.tmpdirname       )





       def 						lowerCAmelCase     (   self							:			str					,      **__lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             kwargs.update(self.special_tokens_map       )
             return BartphoTokenizer.from_pretrained(self.tmpdirname					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			Union[str, Any]					,      __lowerCamelCase							:			Union[str, Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  "This is a là test"
             lowerCamelCase__		:				List[Any]    =  "This is a<unk><unk> test"
             return input_text, output_text





       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             lowerCamelCase__		:				Union[str, Any]    =  BartphoTokenizer(__lowerCamelCase					,      self.monolingual_vocab_file					,      **self.special_tokens_map       )
             lowerCamelCase__		:				Tuple    =  "This is a là test"
             lowerCamelCase__		:				str    =  "▁This ▁is ▁a ▁l à ▁t est".split()
             lowerCamelCase__		:				Union[str, Any]    =  tokenizer.tokenize(__lowerCamelCase       )
             self.assertListEqual(__lowerCamelCase					,      __lowerCamelCase       )

             lowerCamelCase__		:				Dict    =  tokens + [tokenizer.unk_token]
             lowerCamelCase__		:				Optional[int]    =  [4, 5, 6, 3, 3, 7, 8, 3]
             self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCamelCase       )					,      __lowerCamelCase       )






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()}'
    )


