





"""simple docstring"""
import argparse
import os
import shutil

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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    List[Any]			):
 lowerCAmelCase    				= args.pruning_method
 lowerCAmelCase    				= args.threshold

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

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

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

 if target_model_path is None:
  lowerCAmelCase    				= 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__":
       __UpperCamelCase     :						Optional[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''',
       )

       __UpperCamelCase     :						List[Any]						       =  parser.parse_args()

       main(args)












"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


__UpperCamelCase     :						List[str]						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Tuple						       =  {
    '''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class       a							(   a__  ):
 snake_case__             =       '''glpn'''



 def __init__(     self				,       _snake_case=3				,       _snake_case=4				,       _snake_case=[2, 2, 2, 2]				,       _snake_case=[8, 4, 2, 1]				,       _snake_case=[32, 64, 1_60, 2_56]				,       _snake_case=[7, 3, 3, 3]				,       _snake_case=[4, 2, 2, 2]				,       _snake_case=[1, 2, 5, 8]				,       _snake_case=[4, 4, 4, 4]				,       _snake_case="gelu"				,       _snake_case=0.0				,       _snake_case=0.0				,       _snake_case=0.02				,       _snake_case=0.1				,       _snake_case=1E-6				,       _snake_case=64				,       _snake_case=10				,       _snake_case=-1				,       **_snake_case				,       ):







  """simple docstring"""






  super().__init__(**_snake_case     )

  lowerCAmelCase    				= num_channels
  lowerCAmelCase    				= num_encoder_blocks
  lowerCAmelCase    				= depths
  lowerCAmelCase    				= sr_ratios
  lowerCAmelCase    				= hidden_sizes
  lowerCAmelCase    				= patch_sizes
  lowerCAmelCase    				= strides
  lowerCAmelCase    				= mlp_ratios
  lowerCAmelCase    				= num_attention_heads
  lowerCAmelCase    				= hidden_act
  lowerCAmelCase    				= hidden_dropout_prob
  lowerCAmelCase    				= attention_probs_dropout_prob
  lowerCAmelCase    				= initializer_range
  lowerCAmelCase    				= drop_path_rate
  lowerCAmelCase    				= layer_norm_eps
  lowerCAmelCase    				= decoder_hidden_size
  lowerCAmelCase    				= max_depth
  lowerCAmelCase    				= head_in_index





