import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer







def     A__	(       __A						):







    '''simple docstring'''
    _lowerCamelCase  :		Optional[Any]			    =    args.serialization_dir
    _lowerCamelCase  :		Any			    =    args.pruning_method
    _lowerCamelCase  :		List[Any]			    =    args.threshold

    _lowerCamelCase  :		str			    =    torch.load(os.path.join(__A ,			"""pytorch_model.bin"""						) ,			map_location="""cpu"""						)

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

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

    main(args)





lowerCAmelCase      :				Union[str, Any]     			=1000







def     A__	(       __A						):







    '''simple docstring'''
    return x * x







def     A__	(       __A ,			__A ,			__A ,			__A = 4						):







    '''simple docstring'''
    assert callable(
        __A						), F"""the function(object) passed should be callable your input : {function}"""
    assert isinstance(__A ,			(float, int)						), F"""a should be float or integer your input : {a}"""
    assert isinstance(function(__A						) ,			(float, int)						), (
        "the function should return integer or float return type of your function, "
        F"""{type(__A						)}"""
    )
    assert isinstance(__A ,			(float, int)						), F"""b should be float or integer your input : {b}"""
    assert (
        isinstance(__A ,			__A						) and precision > 0
    ), F"""precision should be positive integer your input : {precision}"""

    # just applying the formula of simpson for approximate integration written in
    # mentioned article in first comment of this file and above this function

    _lowerCamelCase  :		Optional[Any]			    =    (b - a) / N_STEPS
    _lowerCamelCase  :		str			    =    function(__A						) + function(__A						)

    for i in range(1 ,			__A						):
        _lowerCamelCase  :		str			    =    a + h * i
        result += function(__A						) * (4 if i % 2 else 2)

    result *= h / 3
    return round(__A ,			__A						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




