


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 						_A						(						_lowercase				)			->				List[Any]:


     """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__":
    __snake_case     =		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''',
    )

    __snake_case     =		parser.parse_args()

    main(args)






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


__snake_case     =		{
    '''configuration_convnextv2''': [
        '''CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ConvNextV2Config''',
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case     =		[
        '''CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''ConvNextV2ForImageClassification''',
        '''ConvNextV2Model''',
        '''ConvNextV2PreTrainedModel''',
        '''ConvNextV2Backbone''',
    ]


if TYPE_CHECKING:
    from .configuration_convnextva import (
        CONVNEXTV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
        ConvNextVaConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convnextva import (
            CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvNextVaBackbone,
            ConvNextVaForImageClassification,
            ConvNextVaModel,
            ConvNextVaPreTrainedModel,
        )

else:
    import sys

    __snake_case     =		_LazyModule(__name__, globals()['''__file__'''], _import_structure)


