




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		str:

    """simple docstring"""


    __UpperCamelCase     						=  args.serialization_dir
    __UpperCamelCase     						=  args.pruning_method
    __UpperCamelCase     						=  args.threshold

    __UpperCamelCase     						=  torch.load(os.path.join(lowercase_    ,   '''pytorch_model.bin'''					)    ,   map_location='''cpu'''					)

    __UpperCamelCase     						=  0  # Number of remaining (not pruned) params in the encoder
    __UpperCamelCase     						=  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":
                __UpperCamelCase     						=  TopKBinarizer.apply(lowercase_    ,   lowercase_					).sum().item()
            elif pruning_method == "sigmoied_threshold":
                __UpperCamelCase     						=  ThresholdBinarizer.apply(lowercase_    ,   lowercase_    ,   lowercase_					).sum().item()
            elif pruning_method == "l0":
                __UpperCamelCase						,					__UpperCamelCase     						=  -0.1, 1.1
                __UpperCamelCase     						=  torch.sigmoid(lowercase_					)
                __UpperCamelCase     						=  s * (r - l) + l
                __UpperCamelCase     						=  s_bar.clamp(min=0.0    ,   max=1.0					)
                __UpperCamelCase     						=  (mask > 0.0).sum().item()
            else:
                raise ValueError('''Unknown pruning method'''					)
            remaining_count += mask_ones
            print(name.ljust(60    ,   ''' '''					)    ,   str(round(1_00 * mask_ones / param.numel()    ,   3					)					).ljust(20    ,   ''' '''					)    ,   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_00 * remaining_count / encoder_count					)


if __name__ == "__main__":
 a_      						=   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_      						=   parser.parse_args()

 main(args)












def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_					)				->		int:

    """simple docstring"""


    __UpperCamelCase     						=  1.0
    for i in range(1    ,   r + 1					):
        ret *= (n + 1 - i) / i
    return round(lowercase_					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return choose(8 + n    ,   lowercase_					) + choose(9 + n    ,   lowercase_					) - 10




def     __SCREAMING_SNAKE_CASE      (     lowercase_					)				->		int:

    """simple docstring"""


    return sum(non_bouncy_exact(lowercase_					) for i in range(1    ,   n + 1					)					)




def     __SCREAMING_SNAKE_CASE      (     lowercase_ = 1_00					)				->		int:

    """simple docstring"""


    return non_bouncy_upto(lowercase_					)


if __name__ == "__main__":
 print(f"""{solution() = }""")






