'''simple docstring'''




import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							str  					=       args.serialization_dir
     lowercase_       :							Union[str, Any]  					=       args.pruning_method
     lowercase_       :							List[Any]  					=       args.threshold

     lowercase_       :							Optional[int]  					=       torch.load(os.path.join(SCREAMING_SNAKE_CASE_	,							'pytorch_model.bin'				)	,							map_location='cpu'				)

     lowercase_       :							str  					=       0  # Number of remaining (not pruned) params in the encoder
     lowercase_       :							Optional[int]  					=       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_       :							Optional[int]  					=       TopKBinarizer.apply(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    lowercase_       :							List[Any]  					=       ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				).sum().item()
               elif pruning_method == "l0":
                    lowercase_					,lowercase_       :							Optional[Any]  					=       -0.1, 1.1
                    lowercase_       :							Tuple  					=       torch.sigmoid(SCREAMING_SNAKE_CASE_				)
                    lowercase_       :							Union[str, Any]  					=       s * (r - l) + l
                    lowercase_       :							Tuple  					=       s_bar.clamp(min=0.0	,							max=1.0				)
                    lowercase_       :							List[str]  					=       (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(SCREAMING_SNAKE_CASE_				)				)
          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__":
 _A										= 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',
 )

 _A										= parser.parse_args()

 main(args)





'''simple docstring'''




from typing import TYPE_CHECKING

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


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

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




