


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			Any 						=args.serialization_dir
   UpperCamelCase__ :			Tuple 						=args.pruning_method
   UpperCamelCase__ :			Any 						=args.threshold

   UpperCamelCase__ :			Optional[int] 						=torch.load(os.path.join(A_							,  "pytorch_model.bin"      )							,  map_location="cpu"      )

   UpperCamelCase__ :			List[str] 						=0  # Number of remaining (not pruned) params in the encoder
   UpperCamelCase__ :			List[str] 						=0  # Number of params in the encoder

   print("name".ljust(6_0							,  " "      )							,  "Remaining Weights %"							,  "Remaining Weight"      )
   for name, param in st.items():
      if "encoder" not in name:
         continue

      if "mask_scores" in name:
         if pruning_method == "topK":
            UpperCamelCase__ :			Optional[Any] 						=TopKBinarizer.apply(A_							,  A_      ).sum().item()
         elif pruning_method == "sigmoied_threshold":
            UpperCamelCase__ :			List[Any] 						=ThresholdBinarizer.apply(A_							,  A_							,  A_      ).sum().item()
         elif pruning_method == "l0":
            UpperCamelCase__						,	UpperCamelCase__ :			List[Any] 						=-0.1, 1.1
            UpperCamelCase__ :			Dict 						=torch.sigmoid(A_      )
            UpperCamelCase__ :			Optional[int] 						=s * (r - l) + l
            UpperCamelCase__ :			Any 						=s_bar.clamp(min=0.0							,  max=1.0      )
            UpperCamelCase__ :			Any 						=(mask > 0.0).sum().item()
         else:
            raise ValueError("Unknown pruning method"      )
         remaining_count += mask_ones
         print(name.ljust(6_0							,  " "      )							,  str(round(1_0_0 * mask_ones / param.numel()							,  3      )      ).ljust(2_0							,  " "      )							,  str(A_      )      )
      else:
         encoder_count += param.numel()
         if "bias" in name or "LayerNorm" in name:
            remaining_count += param.numel()

   print(""      )
   print("Remaining Weights (global) %: "							,  1_0_0 * remaining_count / encoder_count      )


if __name__ == "__main__":
     __UpperCAmelCase      			=  argparse.ArgumentParser()

     parser.add_argument(
         """--pruning_method""",
         choices=["""l0""", """topK""", """sigmoied_threshold"""],
         type=str,
         required=True,
         help=(
             """Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement"""
             """ pruning)"""
         ),
     )
     parser.add_argument(
         """--threshold""",
         type=float,
         required=False,
         help=(
             """For `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(
         """--serialization_dir""",
         type=str,
         required=True,
         help="""Folder containing the model that was previously fine-pruned""",
     )

     __UpperCAmelCase      			=  parser.parse_args()

     main(args)





import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image
def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	List[Any]=2_5_6      )			->			Tuple:

   '''simple docstring'''






   UpperCamelCase__ :			Optional[Any] 						=min(img.size      )

   if s < target_image_size:
      raise ValueError(f'''min dim for image {s} < {target_image_size}'''      )

   UpperCamelCase__ :			Optional[Any] 						=target_image_size / s
   UpperCamelCase__ :			Optional[Any] 						=(round(r * img.size[1]      ), round(r * img.size[0]      ))
   UpperCamelCase__ :			List[Any] 						=TF.resize(A_							,  A_							,  interpolation=PIL.Image.LANCZOS      )
   UpperCamelCase__ :			Optional[int] 						=TF.center_crop(A_							,  output_size=2 * [target_image_size]      )
   UpperCamelCase__ :			Union[str, Any] 						=torch.unsqueeze(T.ToTensor()(A_      )							,  0      )
   return img
def 	_lowerCamelCase  (		A_  :	Optional[int]      )			->			Optional[Any]:

   '''simple docstring'''






   UpperCamelCase__ :			List[Any] 						=2.0 * x - 1.0
   return x
def 	_lowerCamelCase  (		A_  :	Union[str, Any]							,  A_  :	Any=True							,  A_  :	int="RGB"      )			->			int:

   '''simple docstring'''






   UpperCamelCase__ :			Dict 						=x.detach().cpu()
   if process:
      UpperCamelCase__ :			Optional[int] 						=post_process_tensor(A_      )
   UpperCamelCase__ :			Optional[int] 						=x.numpy()
   if process:
      UpperCamelCase__ :			Dict 						=(2_5_5 * x).astype(np.uinta      )
   UpperCamelCase__ :			int 						=Image.fromarray(A_      )
   if not x.mode == mode:
      UpperCamelCase__ :			Tuple 						=x.convert(A_      )
   return x
def 	_lowerCamelCase  (		A_  :	Dict      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=torch.clamp(A_							,  -1.0							,  1.0      )
   UpperCamelCase__ :			Union[str, Any] 						=(x + 1.0) / 2.0
   UpperCamelCase__ :			Union[str, Any] 						=x.permute(1							,  2							,  0      )
   return x
def 	_lowerCamelCase  (		A_  :	Tuple      )			->			List[str]:

   '''simple docstring'''






   UpperCamelCase__ :			List[str] 						=post_process_tensor(x.squeeze()      )
   return x.permute(2							,  0							,  1      ).unsqueeze(0      )

