





'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer







def 				_a	(					_lowerCamelCase   )   ->							List[Any]:



			"""simple docstring"""


			__snake_case				:			List[str]       			=    args.serialization_dir
			__snake_case				:			Union[str, Any]       			=    args.pruning_method
			__snake_case				:			List[str]       			=    args.threshold

			__snake_case				:			Optional[Any]       			=    torch.load(os.path.join(_lowerCamelCase						,  """pytorch_model.bin"""   )						,  map_location="""cpu"""   )

			__snake_case				:			Dict       			=    0  # Number of remaining (not pruned) params in the encoder
			__snake_case				:			Tuple       			=    0  # Number of params in the encoder

			print("""name""".ljust(60						,  """ """   )						,  """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":
												__snake_case				:			Tuple       			=    TopKBinarizer.apply(_lowerCamelCase						,  _lowerCamelCase   ).sum().item()
									elif pruning_method == "sigmoied_threshold":
												__snake_case				:			Tuple       			=    ThresholdBinarizer.apply(_lowerCamelCase						,  _lowerCamelCase						,  _lowerCamelCase   ).sum().item()
									elif pruning_method == "l0":
												__snake_case			,    __snake_case				:			str       			=    -0.1, 1.1
												__snake_case				:			Dict       			=    torch.sigmoid(_lowerCamelCase   )
												__snake_case				:			Tuple       			=    s * (r - l) + l
												__snake_case				:			str       			=    s_bar.clamp(min=0.0						,  max=1.0   )
												__snake_case				:			Tuple       			=    (mask > 0.0).sum().item()
									else:
												raise ValueError("""Unknown pruning method"""   )
									remaining_count += mask_ones
									print(name.ljust(60						,  """ """   )						,  str(round(100 * mask_ones / param.numel()						,  3   )   ).ljust(20						,  """ """   )						,  str(_lowerCamelCase   )   )
						else:
									encoder_count += param.numel()
									if "bias" in name or "LayerNorm" in name:
												remaining_count += param.numel()

			print(""""""   )
			print("""Remaining Weights (global) %: """						,  100 * 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)









'''simple docstring'''






import torch
from torch import autograd




class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[Any]     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float     ,			__magic_name__				:      bool )				->						Optional[int]:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    inputs.numel()
					__snake_case				:			List[Any]       			=    int(0.005 * nb_elems ) + 1
					if sigmoid:
								__snake_case				:			Dict       			=    (torch.sigmoid(__magic_name__ ) > threshold).type(inputs.type() )
					else:
								__snake_case				:			Tuple       			=    (inputs > threshold).type(inputs.type() )
					if mask.sum() < nb_min:
								# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
								__snake_case				:			Tuple       			=    inputs.flatten().kthvalue(max(nb_elems - nb_min     ,			1 ) ).values
								__snake_case				:			Union[str, Any]       			=    (inputs > k_threshold).type(inputs.type() )
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      int     ,			__magic_name__				:      Any )				->						Tuple:


					"""simple docstring"""

					return gradOutput, None, None





class    _A   (				autograd.Function		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      Tuple     ,			__magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						int:


					"""simple docstring"""

					__snake_case				:			Tuple       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Dict       			=    inputs.flatten().sort(descending=__magic_name__ )
					__snake_case				:			List[Any]       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Optional[Any]       			=    mask.flatten()
					__snake_case				:			List[str]       			=    0
					__snake_case				:			Any       			=    1
					return mask

		@staticmethod
		def   lowercase__    (      __magic_name__				:      List[str]     ,			__magic_name__				:      Any )				->						Optional[Any]:


					"""simple docstring"""

					return gradOutput, None





class    _A   (				__lowercase		):

		@staticmethod
		def   lowercase__    (      __magic_name__				:      torch.tensor     ,			__magic_name__				:      float )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			List[str]       			=    inputs.clone()
					__snake_case			,    __snake_case				:			Tuple       			=    inputs.abs().flatten().sort(descending=__magic_name__ )
					__snake_case				:			Any       			=    int(threshold * inputs.numel() )

					# flat_out and mask access the same memory.
					__snake_case				:			Union[str, Any]       			=    mask.flatten()
					__snake_case				:			Tuple       			=    0
					__snake_case				:			int       			=    1
					return mask


