
import argparse
import os
import shutil

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





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Any							=      args.pruning_method
 lowerCamelCase		:	Dict							=      args.threshold

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

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

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

 if target_model_path is None:
  lowerCamelCase		:	Optional[int]							=      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					   =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					   =parser.parse_args()

      main(args)






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       warnings.warn(
           """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
           """ use OwlViTImageProcessor instead."""    ,  __magic_name__    ,  )
       super().__init__(*__magic_name__    ,  **__magic_name__      )




