


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer
def 							UpperCAmelCase_   (				__snake_case		)	->     Optional[Any]:







 """simple docstring"""


 _lowercase				 =args.serialization_dir
 _lowercase				 =args.pruning_method
 _lowercase				 =args.threshold

 _lowercase				 =torch.load(os.path.join(UpperCAmelCase_	,		'''pytorch_model.bin'''		)	,		map_location='''cpu'''		)

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

  UpperCAmelCase__        =				parser.parse_args()

  main(args)




from typing import TYPE_CHECKING

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


lowerCAmelCase__             =		{
    """configuration_markuplm""": ["""MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MarkupLMConfig"""],
    """feature_extraction_markuplm""": ["""MarkupLMFeatureExtractor"""],
    """processing_markuplm""": ["""MarkupLMProcessor"""],
    """tokenization_markuplm""": ["""MarkupLMTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		["""MarkupLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase__             =		[
							    """MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MarkupLMForQuestionAnswering""",
							    """MarkupLMForSequenceClassification""",
							    """MarkupLMForTokenClassification""",
							    """MarkupLMModel""",
							    """MarkupLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
							from .feature_extraction_markuplm import MarkupLMFeatureExtractor
							from .processing_markuplm import MarkupLMProcessor
							from .tokenization_markuplm import MarkupLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_markuplm_fast import MarkupLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_markuplm import (
														    MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MarkupLMForQuestionAnswering,
														    MarkupLMForSequenceClassification,
														    MarkupLMForTokenClassification,
														    MarkupLMModel,
														    MarkupLMPreTrainedModel,
														)


else:
							import sys

							lowerCAmelCase__             =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)




