


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)



"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''mobilenet_v1'''


						def __init__(			self  :   Optional[int]	,A_  :   Optional[int]=3	,A_  :   Any=224	,A_  :   List[Any]=1.0	,A_  :   Union[str, Any]=8	,A_  :   Union[str, Any]="relu6"	,A_  :   Optional[Any]=True	,A_  :   List[str]=0.9_99	,A_  :   int=0.02	,A_  :   int=0.0_01	,**A_  :   Union[str, Any]	,)  -> Dict:
							super().__init__(**A_   )

							if depth_multiplier <= 0:
								raise ValueError('depth_multiplier must be greater than zero.'   )

							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = hidden_act
							A     = tf_padding
							A     = classifier_dropout_prob
							A     = initializer_range
							A     = layer_norm_eps







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     version.parse('''1.11'''     )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict([('pixel_values', {0: 'batch'})]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							if self.task == "image-classification":
								return OrderedDict([('logits', {0: 'batch'})]   )
							else:
								return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> float:
							return 1e-4