
'''simple docstring'''





import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 							snake_case				(		UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      __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(6_0   ,	' '						)   ,	'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(6_0   ,	' '						)   ,	str(round(1_0_0 * mask_ones / param.numel()   ,	3						)						).ljust(2_0   ,	' '						)   ,	str(UpperCAmelCase						)						)
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(''						)
      print('Remaining Weights (global) %: '   ,	1_0_0 * remaining_count / encoder_count						)


if __name__ == "__main__":
 a__		: 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",
 )

 a__		: Tuple  							=							parser.parse_args()

 main(args)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    'tau/splinter-base': 'https://huggingface.co/tau/splinter-base/resolve/main/config.json',
    'tau/splinter-base-qass': 'https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json',
    'tau/splinter-large': 'https://huggingface.co/tau/splinter-large/resolve/main/config.json',
    'tau/splinter-large-qass': 'https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json',
    # See all Splinter models at https://huggingface.co/models?filter=splinter
}




class   __snake_case			(	__lowerCAmelCase							):
      a__								=						"""splinter"""



      def __init__(		self       ,     lowercase=3_05_22       ,     lowercase=7_68       ,     lowercase=12       ,     lowercase=12       ,     lowercase=30_72       ,     lowercase="gelu"       ,     lowercase=0.1       ,     lowercase=0.1       ,     lowercase=5_12       ,     lowercase=2       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=True       ,     lowercase=0       ,     lowercase=1_04       ,     **lowercase       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__(pad_token_id=lowercase       ,     **lowercase)

        a__:	Dict     				=      vocab_size
        a__:	Dict     				=      max_position_embeddings
        a__:	Dict     				=      hidden_size
        a__:	int     				=      num_hidden_layers
        a__:	str     				=      num_attention_heads
        a__:	Dict     				=      intermediate_size
        a__:	Tuple     				=      hidden_act
        a__:	Optional[Any]     				=      hidden_dropout_prob
        a__:	List[Any]     				=      attention_probs_dropout_prob
        a__:	Tuple     				=      initializer_range
        a__:	str     				=      type_vocab_size
        a__:	Optional[Any]     				=      layer_norm_eps
        a__:	Any     				=      use_cache
        a__:	List[Any]     				=      question_token_id


