


import argparse
import os
import shutil

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







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Optional[Any]:
  snake_case__						= args.pruning_method
  snake_case__						= args.threshold

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

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

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

  if target_model_path is None:
    snake_case__						= os.path.join(
        os.path.dirname(__lowerCAmelCase				)							,   F"""bertarized_{os.path.basename(__lowerCAmelCase				)}"""				)

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

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


if __name__ == "__main__":
   lowerCamelCase__							:						int							     =   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__							:						Optional[int]							     =   parser.parse_args()

   main(args)





import os
import sys

lowerCamelCase__							:						Optional[int]							     =   os.path.join(os.path.dirname(__file__), """src""")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowerCamelCase__							:						Optional[int]							     =   [
    """torch""",
    """numpy""",
    """tokenizers""",
    """filelock""",
    """requests""",
    """tqdm""",
    """regex""",
    """sentencepiece""",
    """sacremoses""",
    """importlib_metadata""",
    """huggingface_hub""",
]







@add_start_docstrings(AutoConfig.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Any:
  return AutoConfig.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoTokenizer.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoTokenizer.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModel.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Tuple:
  return AutoModel.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForCausalLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForMaskedLM.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[Any]:
  return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForSequenceClassification.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				List[str]:
  return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)







@add_start_docstrings(AutoModelForQuestionAnswering.__doc__				)
def SCREAMING_SNAKE_CASE	(			*__lowerCAmelCase							,   **__lowerCAmelCase				)    ->				Union[str, Any]:
  return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase							,   **__lowerCAmelCase				)

