


'''simple docstring'''




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def      _a		(lowercase__ :      Optional[int]	) -> Any:






     """simple docstring"""
     __snake_case    			=	args.serialization_dir
     __snake_case    			=	args.pruning_method
     __snake_case    			=	args.threshold

     __snake_case    			=	torch.load(os.path.join(lowercase__       ,      'pytorch_model.bin'	)       ,      map_location='cpu'	)

     __snake_case    			=	0  # Number of remaining (not pruned) params in the encoder
     __snake_case    			=	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":
                    __snake_case    			=	TopKBinarizer.apply(lowercase__       ,      lowercase__	).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    __snake_case    			=	ThresholdBinarizer.apply(lowercase__       ,      lowercase__       ,      lowercase__	).sum().item()
               elif pruning_method == "l0":
                    __snake_case			,					__snake_case    			=	-0.1, 1.1
                    __snake_case    			=	torch.sigmoid(lowercase__	)
                    __snake_case    			=	s * (r - l) + l
                    __snake_case    			=	s_bar.clamp(min=0.0       ,      max=1.0	)
                    __snake_case    			=	(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(lowercase__	)	)
          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__":
       _a      :      Optional[Any]       				=      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",
       )

       _a      :      int       				=      parser.parse_args()

       main(args)








'''simple docstring'''




from functools import partial
from typing import Any, Callable, Dict, List, Type, TypeVar, Union, overload

import torch
import torch.nn as nn
import torch.types





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      bool	) -> torch.Tensor:






     """simple docstring"""
     # The first operation in a checkpoint can't be in-place, but it's
     # nice to have in-place addition during inference. Thus...
     if not inplace:
          __snake_case    			=	ma + ma
     else:
          ma += ma

     return ma





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      List[int]	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	-1 * len(lowercase__	)
     __snake_case    			=	list(range(len(tensor.shape[:zero_index]	)	)	)
     return tensor.permute(first_inds + [zero_index + i for i in inds]	)





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      int	) -> torch.Tensor:






     """simple docstring"""
     return t.reshape(t.shape[:-no_dims] + (-1,)	)





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      int       ,      lowercase__ :      float = 1e-4	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	mask.expand(*value.shape	)
     return torch.sum(mask * value       ,      dim=lowercase__	) / (eps + torch.sum(lowercase__       ,      dim=lowercase__	))





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.types.Number = 2.31_25       ,      lowercase__ :      torch.types.Number = 21.68_75       ,      lowercase__ :      int = 6_4	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	torch.linspace(lowercase__       ,      lowercase__       ,      no_bins - 1       ,      device=pts.device	)
     __snake_case    			=	torch.sqrt(torch.sum((pts.unsqueeze(-2	) - pts.unsqueeze(-3	)) ** 2       ,      dim=-1	)	)
     return torch.bucketize(lowercase__       ,      lowercase__	)





def      _a		(lowercase__ :      Callable[[list], Any]       ,      lowercase__ :      List[dict]	) -> dict:






     """simple docstring"""
     __snake_case    			=	dicts[0]
     __snake_case    			=	{}
     for k, v in first.items():
          __snake_case    			=	[d[k] for d in dicts]
          if isinstance(lowercase__       ,      lowercase__	):
               __snake_case    			=	dict_multimap(lowercase__       ,      lowercase__	)
          else:
               __snake_case    			=	fn(lowercase__	)

     return new_dict





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	v_bins.view(((1,) * len(x.shape	)) + (len(lowercase__	),)	)
     __snake_case    			=	x[..., None] - reshaped_bins
     __snake_case    			=	torch.argmin(torch.abs(lowercase__	)       ,      dim=-1	)
     return nn.functional.one_hot(lowercase__       ,      num_classes=len(lowercase__	)	).float()





def      _a		(lowercase__ :      torch.Tensor       ,      lowercase__ :      torch.Tensor       ,      lowercase__ :      int = 0       ,      lowercase__ :      int = 0	) -> torch.Tensor:






     """simple docstring"""
     __snake_case    			=	[]
     for i, s in enumerate(data.shape[:no_batch_dims]	):
          __snake_case    			=	torch.arange(lowercase__	)
          __snake_case    			=	r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape	) - i - 1)))	)
          ranges.append(lowercase__	)

     __snake_case    			=	[slice(lowercase__	) for _ in range(len(data.shape	) - no_batch_dims	)]
     __snake_case    			=	inds
     ranges.extend(lowercase__	)
     # Matt note: Editing this to get around the behaviour of using a list as an array index changing
     # in recent Numpy versions
     return data[tuple(lowercase__	)]


_a      :      str       				=      TypeVar("T")





def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      Dict[Any, Union[dict, list, tuple, T]]       ,      lowercase__ :      Type[T]	) -> Dict[Any, Union[dict, list, tuple, Any]]:






     """simple docstring"""
     __snake_case    			=	{}
     for k, v in dic.items():
          if isinstance(lowercase__       ,      lowercase__	):
               __snake_case    			=	dict_map(lowercase__       ,      lowercase__       ,      lowercase__	)
          else:
               __snake_case    			=	tree_map(lowercase__       ,      lowercase__       ,      lowercase__	)

     return new_dict





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      T       ,      lowercase__ :      Type[T]	) -> Any:






     """simple docstring"""
     ...





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      dict       ,      lowercase__ :      Type[T]	) -> dict:






     """simple docstring"""
     ...





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      list       ,      lowercase__ :      Type[T]	) -> list:






     """simple docstring"""
     ...





@overload
def      _a		(lowercase__ :      Callable[[T], Any]       ,      lowercase__ :      tuple       ,      lowercase__ :      Type[T]	) -> tuple:






     """simple docstring"""
     ...





def      _a		(lowercase__ :      str       ,      lowercase__ :      Any       ,      lowercase__ :      Dict	) -> Union[str, Any]:






     """simple docstring"""
     if isinstance(lowercase__       ,      lowercase__	):
          return dict_map(lowercase__       ,      lowercase__       ,      lowercase__	)
     elif isinstance(lowercase__       ,      lowercase__	):
          return [tree_map(lowercase__       ,      lowercase__       ,      lowercase__	) for x in tree]
     elif isinstance(lowercase__       ,      lowercase__	):
          return tuple(tree_map(lowercase__       ,      lowercase__       ,      lowercase__	) for x in tree	)
     elif isinstance(lowercase__       ,      lowercase__	):
          return fn(lowercase__	)
     else:
          print(type(lowercase__	)	)
          raise ValueError('Not supported'	)


_a      :      Union[str, Any]       				=      partial(tree_map, leaf_type=torch.Tensor)




