

import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer



def    UpperCamelCase(					lowercase_							)	->				int:





  '''simple docstring'''





  snake_case_       						=      args.serialization_dir
  snake_case_       						=      args.pruning_method
  snake_case_       						=      args.threshold

  snake_case_       						=      torch.load(os.path.join(lowercase_			,					"""pytorch_model.bin"""							)			,					map_location="""cpu"""							)

  snake_case_       						=      0  # Number of remaining (not pruned) params in the encoder
  snake_case_       						=      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":
        snake_case_       						=      TopKBinarizer.apply(lowercase_			,					lowercase_							).sum().item()
      elif pruning_method == "sigmoied_threshold":
        snake_case_       						=      ThresholdBinarizer.apply(lowercase_			,					lowercase_			,					lowercase_							).sum().item()
      elif pruning_method == "l0":
        snake_case_						,				snake_case_       						=      -0.1, 1.1
        snake_case_       						=      torch.sigmoid(lowercase_							)
        snake_case_       						=      s * (r - l) + l
        snake_case_       						=      s_bar.clamp(min=0.0			,					max=1.0							)
        snake_case_       						=      (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(lowercase_							)							)
    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_    =					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_    =					parser.parse_args()

    main(args)


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_    =					{
    '''configuration_mpnet''': ['''MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MPNetConfig'''],
    '''tokenization_mpnet''': ['''MPNetTokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					['''MPNetTokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''MPNetForMaskedLM''',
        '''MPNetForMultipleChoice''',
        '''MPNetForQuestionAnswering''',
        '''MPNetForSequenceClassification''',
        '''MPNetForTokenClassification''',
        '''MPNetLayer''',
        '''MPNetModel''',
        '''MPNetPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase_    =					[
        '''TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFMPNetEmbeddings''',
        '''TFMPNetForMaskedLM''',
        '''TFMPNetForMultipleChoice''',
        '''TFMPNetForQuestionAnswering''',
        '''TFMPNetForSequenceClassification''',
        '''TFMPNetForTokenClassification''',
        '''TFMPNetMainLayer''',
        '''TFMPNetModel''',
        '''TFMPNetPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
    from .tokenization_mpnet import MPNetTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mpnet_fast import MPNetTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mpnet import (
            MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            MPNetForMaskedLM,
            MPNetForMultipleChoice,
            MPNetForQuestionAnswering,
            MPNetForSequenceClassification,
            MPNetForTokenClassification,
            MPNetLayer,
            MPNetModel,
            MPNetPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mpnet import (
            TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMPNetEmbeddings,
            TFMPNetForMaskedLM,
            TFMPNetForMultipleChoice,
            TFMPNetForQuestionAnswering,
            TFMPNetForSequenceClassification,
            TFMPNetForTokenClassification,
            TFMPNetMainLayer,
            TFMPNetModel,
            TFMPNetPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase_    =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)