




import math

class 			_lowerCamelCase    :

      """simple docstring"""

      def __init__(  self       :			Union[str, Any]			,							snake_case       :			int			,							snake_case       :			int			,							snake_case       :			int			,							snake_case       :			Optional[Any]					):
          __UpperCamelCase			=    x
          __UpperCamelCase			=    y
          __UpperCamelCase			=    step_size
          __UpperCamelCase			=    function_to_optimize







      def    snake_case						(  self       :			List[Any]					):
          return self.function(self.x			,							self.y					)







      def    snake_case						(  self       :			Dict					):
          __UpperCamelCase			=    self.step_size
          return [
              SearchProblem(snake_case			,							snake_case			,							snake_case			,							self.function					)
              for x, y in (
                  (self.x - step_size, self.y - step_size),
                  (self.x - step_size, self.y),
                  (self.x - step_size, self.y + step_size),
                  (self.x, self.y - step_size),
                  (self.x, self.y + step_size),
                  (self.x + step_size, self.y - step_size),
                  (self.x + step_size, self.y),
                  (self.x + step_size, self.y + step_size),
              )
          ]







      def __hash__(  self       :			Optional[int]					):
          return hash(str(self					)					)







      def __eq__(  self       :			str			,							snake_case       :			Tuple					):
          if isinstance(snake_case			,							snake_case					):
              return hash(str(self					)					) == hash(str(snake_case					)					)
          return False







      def __str__(  self       :			Tuple					):
          return F"x: {self.x} y: {self.y}"







def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_ = True       ,  lowercase_ = math.inf       ,  lowercase_ = -math.inf       ,  lowercase_ = math.inf       ,  lowercase_ = -math.inf       ,  lowercase_ = False       ,  lowercase_ = 1_00_00       ,  )							->						SearchProblem:

    """simple docstring"""


    __UpperCamelCase			=    search_prob
    __UpperCamelCase			=    []  # list to store the current score at each iteration
    __UpperCamelCase			=    0
    __UpperCamelCase			=    False
    __UpperCamelCase			=    set()
    while not solution_found and iterations < max_iter:
        visited.add(lowercase_   )
        iterations += 1
        __UpperCamelCase			=    current_state.score()
        scores.append(lowercase_   )
        __UpperCamelCase			=    current_state.get_neighbors()
        __UpperCamelCase			=    -math.inf
        __UpperCamelCase			=    math.inf
        __UpperCamelCase			=    None  # to hold the next best neighbor
        for neighbor in neighbors:
            if neighbor in visited:
                continue  # do not want to visit the same state again
            if (
                neighbor.x > max_x
                or neighbor.x < min_x
                or neighbor.y > max_y
                or neighbor.y < min_y
            ):
                continue  # neighbor outside our bounds
            __UpperCamelCase			=    neighbor.score() - current_score
            if find_max:  # finding max
                # going to direction with greatest ascent
                if change > max_change and change > 0:
                    __UpperCamelCase			=    change
                    __UpperCamelCase			=    neighbor
            else:  # finding min
                # to direction with greatest descent
                if change < min_change and change < 0:
                    __UpperCamelCase			=    change
                    __UpperCamelCase			=    neighbor
        if next_state is not None:
            # we found at least one neighbor which improved the current state
            __UpperCamelCase			=    next_state
        else:
            # since we have no neighbor that improves the solution we stop the search
            __UpperCamelCase			=    True

    if visualization:
        from matplotlib import pyplot as plt

        plt.plot(range(lowercase_   )       ,  lowercase_   )
        plt.xlabel('''Iterations'''   )
        plt.ylabel('''Function values'''   )
        plt.show()

    return current_state


if __name__ == "__main__":
 import doctest

 doctest.testmod()




 def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

     """simple docstring"""


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

 # starting the problem with initial coordinates (3, 4)
 a_ 		=  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 a_ 		=  hill_climbing(prob, find_max=False)
 print(
     "The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
     f"""{local_min.score()}"""
 )

 # starting the problem with initial coordinates (12, 47)
 a_ 		=  SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
 a_ 		=  hill_climbing(
     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()}"""
 )




 def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

     """simple docstring"""


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

 a_ 		=  SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
 a_ 		=  hill_climbing(prob, find_max=True)


 print(
     "The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
     f"""{local_min.score()}"""
 )












import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						Any:

    """simple docstring"""


    if "model" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''model.'''       ,  ''''''   )
    if "norm1" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm1'''       ,  '''attention.output.LayerNorm'''   )
    if "norm2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm2'''       ,  '''output.LayerNorm'''   )
    if "norm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''norm'''       ,  '''LayerNorm'''   )
    if "transformer" in orig_key:
        __UpperCamelCase			=    orig_key.split('''.'''   )[0].split('''_'''   )[-1]
        __UpperCamelCase			=    orig_key.replace(F"transformer_{layer_num}"       ,  F"encoder.layer.{layer_num}"   )
    if "mha.attn" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha.attn'''       ,  '''attention.self'''   )
    if "mha" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mha'''       ,  '''attention'''   )
    if "W_q" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_q'''       ,  '''self.query'''   )
    if "W_k" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_k'''       ,  '''self.key'''   )
    if "W_v" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''W_v'''       ,  '''self.value'''   )
    if "ff.0" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.0'''       ,  '''intermediate.dense'''   )
    if "ff.2" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff.2'''       ,  '''output.dense'''   )
    if "ff" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''ff'''       ,  '''output.dense'''   )
    if "mlm_class" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm.mlm_class'''       ,  '''cls.predictions.decoder'''   )
    if "mlm" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''mlm'''       ,  '''cls.predictions.transform'''   )
    if "backbone.backbone.encoders" in orig_key:
        __UpperCamelCase			=    orig_key.replace('''backbone.backbone.encoders'''       ,  '''encoder.layer'''   )
    if "cls" not in orig_key:
        __UpperCamelCase			=    '''mra.''' + orig_key

    return orig_key




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase			=    orig_state_dict.pop(lowercase_   )

        if ("pooler" in key) or ("sen_class" in key):
            continue
        else:
            __UpperCamelCase			=    val

    __UpperCamelCase			=    orig_state_dict['''cls.predictions.decoder.bias''']
    __UpperCamelCase			=    torch.arange(lowercase_   ).expand((1, -1)   ) + 2

    return orig_state_dict




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    __UpperCamelCase			=    torch.load(lowercase_       ,  map_location='''cpu'''   )['''model_state_dict''']
    __UpperCamelCase			=    MraConfig.from_json_file(lowercase_   )
    __UpperCamelCase			=    MraForMaskedLM(lowercase_   )

    __UpperCamelCase			=    convert_checkpoint_helper(config.max_position_embeddings       ,  lowercase_   )

    print(model.load_state_dict(lowercase_   )   )
    model.eval()
    model.save_pretrained(lowercase_   )

    print(F"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
 )
 parser.add_argument(
     "--config_file",
     default=None,
     type=str,
     required=True,
     help="The json file for Mra model config.",
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_ 		=  parser.parse_args()
 convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






