





'''simple docstring'''


import argparse
import os
import shutil

import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any )							->     int:

       """simple docstring"""


       UpperCAmelCase  =						args.pruning_method
       UpperCAmelCase  =						args.threshold

       UpperCAmelCase  =						args.model_name_or_path.rstrip('''/''' )
       UpperCAmelCase  =						args.target_model_path

       print(f"Load fine-pruned model from {model_name_or_path}" )
       UpperCAmelCase  =						torch.load(os.path.join(SCREAMING_SNAKE_CASE_			,	'''pytorch_model.bin''' ) )
       UpperCAmelCase  =						{}

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

       if target_model_path is None:
              UpperCAmelCase  =						os.path.join(
                  os.path.dirname(SCREAMING_SNAKE_CASE_ )			,	f"bertarized_{os.path.basename(SCREAMING_SNAKE_CASE_ )}" )

       if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
              shutil.copytree(SCREAMING_SNAKE_CASE_			,	SCREAMING_SNAKE_CASE_ )
              print(f"\nCreated folder {target_model_path}" )

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


if __name__ == "__main__":
    a__			:					List[str]       						=						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',
    )

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

    main(args)






'''simple docstring'''
from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase   : Dict             =				{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """VanForImageClassification""",
					    """VanModel""",
					    """VanPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_van import (
										    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
										    VanForImageClassification,
										    VanModel,
										    VanPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : Tuple             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure)


