





"""simple docstring"""




import argparse
import os
import shutil

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



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       List[Any]:


      '''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(SCREAMING_SNAKE_CASE__			,						"""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=SCREAMING_SNAKE_CASE__			,						threshold=SCREAMING_SNAKE_CASE__     )
                        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(SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__     )
                        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(SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__			,						SCREAMING_SNAKE_CASE__     )
                        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(SCREAMING_SNAKE_CASE__     )
                        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(SCREAMING_SNAKE_CASE__     )			,						F"""bertarized_{os.path.basename(SCREAMING_SNAKE_CASE__     )}"""     )

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	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""",
      )

      SCREAMING_SNAKE_CASE_						       =	parser.parse_args()

      main(args)









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 50     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[1] * (length + 1)

      for row_length in range(3			,						length + 1     ):
            for block_length in range(3			,						row_length + 1     ):
                  for block_start in range(row_length - block_length     ):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


if __name__ == "__main__":
      print(F'''{solution() = }''')


