



import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer

def 		UpperCamelCase	(      lowercase_   )		->	Tuple:


  '''simple docstring'''






  lowercase__  :   Dict    		=      args.pruning_method
  lowercase__  :   Optional[int]    		=      args.threshold

  lowercase__  :   Tuple    		=      args.model_name_or_path.rstrip("""/"""   )
  lowercase__  :   Union[str, Any]    		=      args.target_model_path

  print(F'Load fine-pruned model from {model_name_or_path}'   )
  lowercase__  :   Any    		=      torch.load(os.path.join(__lowercase , """pytorch_model.bin"""   )   )
  lowercase__  :   Optional[Any]    		=      {}

  for name, tensor in model.items():
    if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
      lowercase__  :   str    		=      tensor
      print(F'Copied layer {name}'   )
    elif "classifier" in name or "qa_output" in name:
      lowercase__  :   Union[str, Any]    		=      tensor
      print(F'Copied layer {name}'   )
    elif "bias" in name:
      lowercase__  :   str    		=      tensor
      print(F'Copied layer {name}'   )
    else:
      if pruning_method == "magnitude":
        lowercase__  :   Optional[int]    		=      MagnitudeBinarizer.apply(inputs=__lowercase , threshold=__lowercase   )
        lowercase__  :   Tuple    		=      tensor * mask
        print(F'Pruned layer {name}'   )
      elif pruning_method == "topK":
        if "mask_scores" in name:
          continue
        lowercase__  :   int    		=      name[:-6]
        lowercase__  :   int    		=      model[F'{prefix_}mask_scores']
        lowercase__  :   List[Any]    		=      TopKBinarizer.apply(__lowercase , __lowercase   )
        lowercase__  :   List[str]    		=      tensor * mask
        print(F'Pruned layer {name}'   )
      elif pruning_method == "sigmoied_threshold":
        if "mask_scores" in name:
          continue
        lowercase__  :   Optional[int]    		=      name[:-6]
        lowercase__  :   Optional[Any]    		=      model[F'{prefix_}mask_scores']
        lowercase__  :   Optional[Any]    		=      ThresholdBinarizer.apply(__lowercase , __lowercase , __lowercase   )
        lowercase__  :   Any    		=      tensor * mask
        print(F'Pruned layer {name}'   )
      elif pruning_method == "l0":
        if "mask_scores" in name:
          continue
        lowercase__  :   Dict    		=      name[:-6]
        lowercase__  :   str    		=      model[F'{prefix_}mask_scores']
        lowercase__  :   int    		=      -0.1, 1.1
        lowercase__  :   Union[str, Any]    		=      torch.sigmoid(__lowercase   )
        lowercase__  :   List[Any]    		=      s * (r - l) + l
        lowercase__  :   Tuple    		=      s_bar.clamp(min=0.0 , max=1.0   )
        lowercase__  :   Optional[int]    		=      tensor * mask
        print(F'Pruned layer {name}'   )
      else:
        raise ValueError("""Unknown pruning method"""   )

  if target_model_path is None:
    lowercase__  :   int    		=      os.path.join(
        os.path.dirname(__lowercase   ) , F'bertarized_{os.path.basename(__lowercase   )}'   )

  if not os.path.isdir(__lowercase   ):
    shutil.copytree(__lowercase , __lowercase   )
    print(F'\nCreated folder {target_model_path}'   )

  torch.save(__lowercase , os.path.join(__lowercase , """pytorch_model.bin"""   )   )
  print("""\nPruned model saved! See you later!"""   )


if __name__ == "__main__":
     lowerCamelCase__    :		Optional[Any]          =   argparse.ArgumentParser()

     parser.add_argument(
         """--pruning_method""",
         choices=["""l0""", """magnitude""", """topK""", """sigmoied_threshold"""],
         type=str,
         required=True,
         help=(
             """Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"""
             """ sigmoied_threshold = Soft movement pruning)"""
         ),
     )
     parser.add_argument(
         """--threshold""",
         type=float,
         required=False,
         help=(
             """For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."""
             """For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."""
             """Not needed for `l0`"""
         ),
     )
     parser.add_argument(
         """--model_name_or_path""",
         type=str,
         required=True,
         help="""Folder containing the model that was previously fine-pruned""",
     )
     parser.add_argument(
         """--target_model_path""",
         default=None,
         type=str,
         required=False,
         help="""Folder containing the model that was previously fine-pruned""",
     )

     lowerCamelCase__    :		Dict          =   parser.parse_args()

     main(args)








import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 		UpperCamelCase	(      lowercase_ , lowercase_ = True , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = math.inf , lowercase_ = -math.inf , lowercase_ = False , lowercase_ = 1_00 , lowercase_ = 0.01 , lowercase_ = 1 , )		->	Any:


  '''simple docstring'''






  lowercase__  :   Any    		=      False
  lowercase__  :   Union[str, Any]    		=      search_prob
  lowercase__  :   int    		=      start_temperate
  lowercase__  :   Union[str, Any]    		=      []
  lowercase__  :   List[str]    		=      0
  lowercase__  :   Optional[int]    		=      None

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

  if visualization:
    from matplotlib import pyplot as plt

    plt.plot(range(lowercase_   ) , lowercase_   )
    plt.xlabel("""Iterations"""   )
    plt.ylabel("""Function values"""   )
    plt.show()
  return best_state


if __name__ == "__main__":

     def 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	str:


       '''simple docstring'''






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

     # starting the problem with initial coordinates (12, 47)
     lowerCamelCase__    :		str          =   SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		List[Any]          =   simulated_annealing(
         prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, 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=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Optional[int]          =   simulated_annealing(
         prob, find_max=True, max_x=1_0_0, min_x=5, max_y=5_0, 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 		UpperCamelCase	(      lowercase_ , lowercase_   )		->	Union[str, Any]:


       '''simple docstring'''






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

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

     lowerCamelCase__    :		Dict          =   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     lowerCamelCase__    :		Tuple          =   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()}'''
     )






