

"""simple docstring"""



import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 			_snake_case  (     UpperCAmelCase_	:       Union[str, Any]      ):
     A__				      =	args.serialization_dir
     A__				      =	args.pruning_method
     A__				      =	args.threshold

     A__				      =	torch.load(os.path.join(UpperCAmelCase_       , """pytorch_model.bin"""      )       , map_location="""cpu"""      )

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

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

 main(args)









"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_       :						Union[str, Any]					=    {'configuration_gptj': ['GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTJConfig', 'GPTJOnnxConfig']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Optional[int]					=    [
     'GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST',
     'GPTJForCausalLM',
     'GPTJForQuestionAnswering',
     'GPTJForSequenceClassification',
     'GPTJModel',
     'GPTJPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						List[str]					=    [
     'TFGPTJForCausalLM',
     'TFGPTJForQuestionAnswering',
     'TFGPTJForSequenceClassification',
     'TFGPTJModel',
     'TFGPTJPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_       :						Tuple					=    [
     'FlaxGPTJForCausalLM',
     'FlaxGPTJModel',
     'FlaxGPTJPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gptj import (
      GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTJForCausalLM,
      GPTJForQuestionAnswering,
      GPTJForSequenceClassification,
      GPTJModel,
      GPTJPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_gptj import (
      TFGPTJForCausalLM,
      TFGPTJForQuestionAnswering,
      TFGPTJForSequenceClassification,
      TFGPTJModel,
      TFGPTJPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
 import sys

 SCREAMING_SNAKE_CASE_       :						List[str]					=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






