


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer



def 				UpperCamelCase_(      lowerCamelCase_ )    ->						Tuple:
    _lowercase       :							Optional[Any]      		=		args.pruning_method
    _lowercase       :							Tuple      		=		args.threshold

    _lowercase       :							int      		=		args.model_name_or_path.rstrip('/' )
    _lowercase       :							List[str]      		=		args.target_model_path

    print(F'''Load fine-pruned model from {model_name_or_path}''' )
    _lowercase       :							Optional[int]      		=		torch.load(os.path.join(lowerCamelCase_							, 'pytorch_model.bin' ) )
    _lowercase       :							Dict      		=		{}

    for name, tensor in model.items():
        if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
            _lowercase       :							Union[str, Any]      		=		tensor
            print(F'''Copied layer {name}''' )
        elif "classifier" in name or "qa_output" in name:
            _lowercase       :							Optional[Any]      		=		tensor
            print(F'''Copied layer {name}''' )
        elif "bias" in name:
            _lowercase       :							Union[str, Any]      		=		tensor
            print(F'''Copied layer {name}''' )
        else:
            if pruning_method == "magnitude":
                _lowercase       :							List[str]      		=		MagnitudeBinarizer.apply(inputs=lowerCamelCase_							, threshold=lowerCamelCase_ )
                _lowercase       :							str      		=		tensor * mask
                print(F'''Pruned layer {name}''' )
            elif pruning_method == "topK":
                if "mask_scores" in name:
                    continue
                _lowercase       :							Dict      		=		name[:-6]
                _lowercase       :							Optional[Any]      		=		model[F'''{prefix_}mask_scores''']
                _lowercase       :							Union[str, Any]      		=		TopKBinarizer.apply(lowerCamelCase_							, lowerCamelCase_ )
                _lowercase       :							Union[str, Any]      		=		tensor * mask
                print(F'''Pruned layer {name}''' )
            elif pruning_method == "sigmoied_threshold":
                if "mask_scores" in name:
                    continue
                _lowercase       :							str      		=		name[:-6]
                _lowercase       :							Dict      		=		model[F'''{prefix_}mask_scores''']
                _lowercase       :							List[Any]      		=		ThresholdBinarizer.apply(lowerCamelCase_							, lowerCamelCase_							, lowerCamelCase_ )
                _lowercase       :							List[Any]      		=		tensor * mask
                print(F'''Pruned layer {name}''' )
            elif pruning_method == "l0":
                if "mask_scores" in name:
                    continue
                _lowercase       :							Optional[Any]      		=		name[:-6]
                _lowercase       :							Tuple      		=		model[F'''{prefix_}mask_scores''']
                _lowercase		,				_lowercase       :							Any      		=		-0.1, 1.1
                _lowercase       :							Dict      		=		torch.sigmoid(lowerCamelCase_ )
                _lowercase       :							List[Any]      		=		s * (r - l) + l
                _lowercase       :							Optional[Any]      		=		s_bar.clamp(min=0.0							, max=1.0 )
                _lowercase       :							Optional[Any]      		=		tensor * mask
                print(F'''Pruned layer {name}''' )
            else:
                raise ValueError('Unknown pruning method' )

    if target_model_path is None:
        _lowercase       :							Tuple      		=		os.path.join(
            os.path.dirname(lowerCamelCase_ )							, F'''bertarized_{os.path.basename(lowerCamelCase_ )}''' )

    if not os.path.isdir(lowerCamelCase_ ):
        shutil.copytree(lowerCamelCase_							, lowerCamelCase_ )
        print(F'''\nCreated folder {target_model_path}''' )

    torch.save(lowerCamelCase_							, os.path.join(lowerCamelCase_							, 'pytorch_model.bin' ) )
    print('\nPruned model saved! See you later!' )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE       :      Dict												= argparse.ArgumentParser()

 parser.add_argument(
     "--pruning_method",
     choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
     type=str,
     required=True,
     help=(
         "Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
         " sigmoied_threshold = Soft movement pruning)"
     ),
 )
 parser.add_argument(
     "--threshold",
     type=float,
     required=False,
     help=(
         "For `magnitude` and `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(
     "--model_name_or_path",
     type=str,
     required=True,
     help="Folder containing the model that was previously fine-pruned",
 )
 parser.add_argument(
     "--target_model_path",
     default=None,
     type=str,
     required=False,
     help="Folder containing the model that was previously fine-pruned",
 )

 SCREAMING_SNAKE_CASE       :      Dict												= parser.parse_args()

 main(args)






def 				UpperCamelCase_(      lowerCamelCase_ )    ->						int:
    if not numbers:
        return 0

    if not isinstance(lowerCamelCase_							, (list, tuple) ) or not all(
        isinstance(lowerCamelCase_							, lowerCamelCase_ ) for number in numbers ):
        raise ValueError('numbers must be an iterable of integers' )

    _lowercase       :							int      		=		numbers[0]

    for i in range(1							, len(lowerCamelCase_ ) ):
        # update the maximum and minimum subarray products
        _lowercase       :							Union[str, Any]      		=		numbers[i]
        if number < 0:
            _lowercase		,				_lowercase       :							Any      		=		min_till_now, max_till_now
        _lowercase       :							Union[str, Any]      		=		max(lowerCamelCase_							, max_till_now * number )
        _lowercase       :							Union[str, Any]      		=		min(lowerCamelCase_							, min_till_now * number )

        # update the maximum product found till now
        _lowercase       :							Optional[Any]      		=		max(lowerCamelCase_							, lowerCamelCase_ )

    return max_prod


