import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer





def 	lowerCAmelCase_			(					__lowerCAmelCase    )->	List[str]:






      '''simple docstring'''





      UpperCAmelCase     :			int     	=args.serialization_dir
      UpperCAmelCase     :			Any     	=args.pruning_method
      UpperCAmelCase     :			Union[str, Any]     	=args.threshold

      UpperCAmelCase     :			Union[str, Any]     	=torch.load(os.path.join(__lowerCAmelCase		,       '''pytorch_model.bin'''    )		,       map_location='''cpu'''    )

      UpperCAmelCase     :			Optional[Any]     	=0  # Number of remaining (not pruned) params in the encoder
      UpperCAmelCase     :			Tuple     	=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":
                        UpperCAmelCase     :			List[str]     	=TopKBinarizer.apply(__lowerCAmelCase		,       __lowerCAmelCase    ).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        UpperCAmelCase     :			Tuple     	=ThresholdBinarizer.apply(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase    ).sum().item()
                  elif pruning_method == "l0":
                        UpperCAmelCase	,    UpperCAmelCase     :			Any     	=-0.1, 1.1
                        UpperCAmelCase     :			Optional[Any]     	=torch.sigmoid(__lowerCAmelCase    )
                        UpperCAmelCase     :			List[Any]     	=s * (r - l) + l
                        UpperCAmelCase     :			Optional[int]     	=s_bar.clamp(min=0.0		,       max=1.0    )
                        UpperCAmelCase     :			List[Any]     	=(mask > 0.0).sum().item()
                  else:
                        raise ValueError('''Unknown pruning method'''    )
                  remaining_count += mask_ones
                  print(name.ljust(60		,       ''' '''    )		,       str(round(1_00 * mask_ones / param.numel()		,       3    )    ).ljust(20		,       ''' '''    )		,       str(__lowerCAmelCase    )    )
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(''''''    )
      print('''Remaining Weights (global) %: '''		,       1_00 * remaining_count / encoder_count    )


if __name__ == "__main__":
     __snake_case         =     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''',
     )

     __snake_case         =     parser.parse_args()

     main(args)







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/

