





"""simple docstring"""
from manim import *
class lowerCamelCase__	(     __UpperCamelCase					):



   def  _UpperCAmelCase						(							self						)			->     Optional[Any]:







      """simple docstring"""
      lowercase     :      Dict	=							Rectangle(height=0.5   , width=0.5						)
      lowercase     :      List[Any]	=							Rectangle(height=0.46   , width=0.46						).set_stroke(width=0						)

      lowercase     :      List[Any]	=							[mem.copy() for i in range(6						)]
      lowercase     :      Optional[Any]	=							[mem.copy() for i in range(6						)]
      lowercase     :      List[str]	=							VGroup(*snake_case__						).arrange(snake_case__   , buff=0						)
      lowercase     :      int	=							VGroup(*snake_case__						).arrange(snake_case__   , buff=0						)
      lowercase     :      List[str]	=							VGroup(snake_case__   , snake_case__						).arrange(snake_case__   , buff=0						)
      lowercase     :      Union[str, Any]	=							Text("""CPU"""   , font_size=2_4						)
      lowercase     :      Tuple	=							Group(snake_case__   , snake_case__						).arrange(snake_case__   , buff=0.5   , aligned_edge=snake_case__						)
      cpu.move_to([-2.5, -0.5, 0]						)
      self.add(snake_case__						)

      lowercase     :      Tuple	=							[mem.copy() for i in range(4						)]
      lowercase     :      List[str]	=							VGroup(*snake_case__						).arrange(snake_case__   , buff=0						)
      lowercase     :      List[str]	=							Text("""GPU"""   , font_size=2_4						)
      lowercase     :      Union[str, Any]	=							Group(snake_case__   , snake_case__						).arrange(snake_case__   , buff=0.5   , aligned_edge=snake_case__						)
      gpu.move_to([-1, -1, 0]						)
      self.add(snake_case__						)

      lowercase     :      Union[str, Any]	=							[mem.copy() for i in range(6						)]
      lowercase     :      Union[str, Any]	=							VGroup(*snake_case__						).arrange(snake_case__   , buff=0						)

      lowercase     :      Union[str, Any]	=							Text("""Model"""   , font_size=2_4						)
      lowercase     :      Union[str, Any]	=							Group(snake_case__   , snake_case__						).arrange(snake_case__   , buff=0.5   , aligned_edge=snake_case__						)
      model.move_to([3, -1.0, 0]						)
      self.add(snake_case__						)

      lowercase     :      Any	=							[]
      for i, rect in enumerate(snake_case__						):
         rect.set_stroke(snake_case__						)
         # target = fill.copy().set_fill(YELLOW, opacity=0.7)
         # target.move_to(rect)
         # self.add(target)

         lowercase     :      Any	=							Rectangle(height=0.46 / 4   , width=0.46 / 3						).set_stroke(width=0.0						).set_fill(snake_case__   , opacity=0.7						)

         if i == 0:
            cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT						)   , buff=0.02   , direction=snake_case__						)
            cpu_target.set_x(cpu_target.get_x() + 0.1						)
         elif i == 3:
            cpu_target.next_to(cpu_targs[0]   , direction=snake_case__   , buff=0.0						)
         else:
            cpu_target.next_to(cpu_targs[i - 1]   , direction=snake_case__   , buff=0.0						)
         self.add(snake_case__						)
         cpu_targs.append(snake_case__						)

      lowercase     :      List[Any]	=							[mem.copy() for i in range(6						)]
      lowercase     :      Union[str, Any]	=							VGroup(*snake_case__						).arrange(snake_case__   , buff=0						)

      lowercase     :      Union[str, Any]	=							Text("""Loaded Checkpoint"""   , font_size=2_4						)
      lowercase     :      List[Any]	=							Group(snake_case__   , snake_case__						).arrange(snake_case__   , aligned_edge=snake_case__   , buff=0.4						)
      checkpoint.move_to([3, 0.5, 0]						)

      lowercase     :      int	=							Square(side_length=2.2						)
      key.move_to([-5, 2, 0]						)

      lowercase     :      Optional[int]	=							MarkupText(
          f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''   , font_size=1_8   , )

      key_text.move_to([-5, 2.4, 0]						)

      self.add(snake_case__   , snake_case__						)

      lowercase     :      List[Any]	=							MarkupText(
          f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint'''   , font_size=1_8   , )

      blue_text.next_to(snake_case__   , DOWN * 2.4   , aligned_edge=key_text.get_left()						)

      lowercase     :      Dict	=							MarkupText(
          f'''Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.'''   , font_size=2_4   , )
      step_a.move_to([2, 2, 0]						)
      self.play(Write(snake_case__						)   , Write(snake_case__						)						)

      self.play(Write(snake_case__   , run_time=1						)   , Create(snake_case__   , run_time=1						)						)

      lowercase     :      List[str]	=							[]
      lowercase     :      Any	=							[]
      for i, rect in enumerate(snake_case__						):
         lowercase     :      int	=							fill.copy().set_fill(snake_case__   , opacity=0.7						)
         target.move_to(snake_case__						)
         first_animations.append(GrowFromCenter(snake_case__   , run_time=1						)						)

         lowercase     :      str	=							target.copy()
         cpu_target.generate_target()
         if i < 5:
            cpu_target.target.move_to(cpu_left_col_base[i + 1]						)
         else:
            cpu_target.target.move_to(cpu_right_col_base[i - 5]						)
         second_animations.append(MoveToTarget(snake_case__   , run_time=1.5						)						)

      self.play(*snake_case__						)
      self.play(*snake_case__						)
      self.wait()





import argparse
import os
import shutil

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


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Optional[int]						)		->				int:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      args.pruning_method
 SCREAMING_SNAKE_CASE		    =      args.threshold

 SCREAMING_SNAKE_CASE		    =      args.model_name_or_path.rstrip('/'						)
 SCREAMING_SNAKE_CASE		    =      args.target_model_path

 print(f"""Load fine-pruned model from {model_name_or_path}"""						)
 SCREAMING_SNAKE_CASE		    =      torch.load(os.path.join(_UpperCamelCase		,				'pytorch_model.bin'						)						)
 SCREAMING_SNAKE_CASE		    =      {}

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

 if target_model_path is None:
  SCREAMING_SNAKE_CASE		    =      os.path.join(
      os.path.dirname(_UpperCamelCase						)		,				f"""bertarized_{os.path.basename(_UpperCamelCase						)}"""						)

 if not os.path.isdir(_UpperCamelCase						):
  shutil.copytree(_UpperCamelCase		,				_UpperCamelCase						)
  print(f"""\nCreated folder {target_model_path}"""						)

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


if __name__ == "__main__":
      a_     :    Union[str, 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",
      )

      a_     :    Dict     		=   parser.parse_args()

      main(args)

