code: 
  
  
  
  
  """simple docstring"""
  
  
  
  import math
  
  
  
  
  class 			UpperCAmelCase_						:
  
  
  
  
      def __init__(					self		: Optional[int]			,				snake_case_		: int			,				snake_case_		: int			,				snake_case_		: int			,				snake_case_		: Tuple   )   ->							str:
  
  
  
  
       '''simple docstring'''
  
       A__ =       x
       A__ =       y
       A__ =       step_size
       A__ =       function_to_optimize
  
  
  
  
      def 							__magic_name__							(					self		: Optional[Any]   )   ->							int:
  
  
  
  
       '''simple docstring'''
  
       return self.function(self.x			,				self.y   )
  
  
  
  
      def 							__magic_name__							(					self		: List[str]   )   ->							Union[str, Any]:
  
  
  
  
       '''simple docstring'''
  
       A__ =       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[Any]   )   ->							Tuple:
  
  
  
  
       '''simple docstring'''
  
       return hash(str(self   )   )
  
  
  
  
      def __eq__(					self		: Union[str, Any]			,				snake_case_		: Optional[Any]   )   ->							List[Any]:
  
  
  
  
       '''simple docstring'''
  
       if isinstance(snake_case_			,				snake_case_   ):
        return hash(str(self   )   ) == hash(str(snake_case_   )   )
       return False
  
  
  
  
      def __str__(					self		: str   )   ->							Any:
  
  
  
  
       '''simple docstring'''
  
       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:
   A__ =       search_prob
   A__ =       []  # list to store the current score at each iteration
   A__ =       0
   A__ =       False
   A__ =       set()
   while not solution_found and iterations < max_iter:
    visited.add(lowercase_		)
    iterations += 1
    A__ =       current_state.score()
    scores.append(lowercase_		)
    A__ =       current_state.get_neighbors()
    A__ =       -math.inf
    A__ =       math.inf
    A__ =       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
     A__ =       neighbor.score() - current_score
     if find_max:  # finding max
      # going to direction with greatest ascent
      if change > max_change and change > 0:
       A__ =       change
       A__ =       neighbor
     else:  # finding min
      # to direction with greatest descent
      if change < min_change and change < 0:
       A__ =       change
       A__ =       neighbor
    if next_state is not None:
     # we found at least one neighbor which improved the current state
     A__ =       next_state
    else:
     # since we have no neighbor that improves the solution we stop the search
     A__ =       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_		)		->	Any:
         return (x**2) + (y**2)
  
        # starting the problem with initial coordinates (3, 4)
        SCREAMING_SNAKE_CASE       = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
        SCREAMING_SNAKE_CASE       = 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)
        SCREAMING_SNAKE_CASE       = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
        SCREAMING_SNAKE_CASE       = 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_		)		->	str:
         return (3 * x**2) - (6 * y)
  
        SCREAMING_SNAKE_CASE       = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
        SCREAMING_SNAKE_CASE       = 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()}'
        )
  
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE       = logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForSequenceClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["projector.weight"]
   A__ =       downstream_dict["projector.bias"]
   A__ =       downstream_dict["model.post_net.linear.weight"]
   A__ =       downstream_dict["model.post_net.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	Optional[Any]:
   A__ =       WavLMForAudioFrameClassification.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["model.linear.weight"]
   A__ =       downstream_dict["model.linear.bias"]
   return model
  
  
  
  
  
  
  
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_		)		->	int:
   A__ =       WavLMForXVector.from_pretrained(lowercase_    ,				config=lowercase_		)
   A__ =       downstream_dict["connector.weight"]
   A__ =       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
    A__ =       downstream_dict[
        f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
    ]
    A__ =       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   A__ =       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   A__ =       downstream_dict["objective.W"]
   return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 						_SCREAMING_SNAKE_CASE     (							lowercase_    ,				lowercase_    ,				lowercase_    ,				lowercase_		)		->	Union[str, Any]:
   A__ =       torch.load(lowercase_    ,				map_location="cpu"		)
  
   A__ =       checkpoint["Downstream"]
  
   A__ =       WavLMConfig.from_pretrained(lowercase_		)
   A__ =       WavaVecaFeatureExtractor.from_pretrained(
       lowercase_    ,				return_attention_mask=lowercase_    ,				do_normalize=lowercase_		)
  
   A__ =       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"		):
    A__ =       convert_classification(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForAudioFrameClassification"		):
    A__ =       convert_diarization(lowercase_    ,				lowercase_    ,				lowercase_		)
   elif arch.endswith("ForXVector"		):
    A__ =       convert_xvector(lowercase_    ,				lowercase_    ,				lowercase_		)
   else:
    raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""		)
  
   if hf_config.use_weighted_layer_sum:
    A__ =       checkpoint["Featurizer"]["weights"]
  
   hf_feature_extractor.save_pretrained(lowercase_		)
   hf_model.save_pretrained(lowercase_		)
  
  
  if __name__ == "__main__":
        SCREAMING_SNAKE_CASE       = argparse.ArgumentParser()
        parser.add_argument(
            "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
        )
        parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
        parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
        parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
        SCREAMING_SNAKE_CASE       = parser.parse_args()
        convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  