



"""simple docstring"""
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()
def    A_  (			_lowercase,					_lowercase	):







						'''simple docstring'''


						snake_case_ :Optional[int]						=			torch.load(_lowercase,					map_location="""cpu"""	)

						snake_case_ :Any						=			chkpt["""model"""]

						# We have the base model one level deeper than the original XLM repository
						snake_case_ :Dict						=			{}
						for k, v in state_dict.items():
												if "pred_layer" in k:
																		snake_case_ :Optional[Any]						=			v
												else:
																		snake_case_ :List[str]						=			v

						snake_case_ :List[Any]						=			chkpt["""params"""]
						snake_case_ :str						=			{n: v for n, v in config.items() if not isinstance(_lowercase,					(torch.FloatTensor, numpy.ndarray)	)}

						snake_case_ :List[Any]						=			chkpt["""dico_word2id"""]
						snake_case_ :Optional[Any]						=			{s + """</w>""" if s.find("""@@"""	) == -1 and i > 13 else s.replace("""@@""",					""""""	): i for s, i in vocab.items()}

						# Save pytorch-model
						snake_case_ :Dict						=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
						snake_case_ :List[Any]						=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
						snake_case_ :Optional[int]						=			pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

						print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""	)
						torch.save(_lowercase,					_lowercase	)

						print(f"""Save configuration file to {pytorch_config_dump_path}"""	)
						with open(_lowercase,					"""w""",					encoding="""utf-8"""	) as f:
												f.write(json.dumps(_lowercase,					indent=2	) + """\n"""	)

						print(f"""Save vocab file to {pytorch_config_dump_path}"""	)
						with open(_lowercase,					"""w""",					encoding="""utf-8"""	) as f:
												f.write(json.dumps(_lowercase,					indent=2	) + """\n"""	)


if __name__ == "__main__":
						__a      =				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						__a      =				parser.parse_args()
						convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)




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()}'
    )


