


import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   Any     ,	SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Optional[int]     ,	SCREAMING_SNAKE_CASE__     :   Dict							):
     __UpperCamelCase       				=BertConfig.from_pretrained(
         'bert-large-cased'     ,	vocab_size=lowercase__     ,	max_position_embeddings=5_12     ,	is_decoder=lowercase__     ,	add_cross_attention=lowercase__     ,	)
     __UpperCamelCase       				=bert_config.to_dict()
     del bert_config_dict["type_vocab_size"]
     __UpperCamelCase       				=BertGenerationConfig(**lowercase__							)
     if is_encoder:
          __UpperCamelCase       				=BertGenerationEncoder(lowercase__							)
     else:
          __UpperCamelCase       				=BertGenerationDecoder(lowercase__							)
     print(F'Building PyTorch model from configuration: {config}'							)

     # Load weights from tf checkpoint
     load_tf_weights_in_bert_generation(
         lowercase__     ,	lowercase__     ,	model_class='bert'     ,	is_encoder_named_decoder=lowercase__     ,	is_encoder=lowercase__     ,	)

     # Save pytorch-model
     print(F'Save PyTorch model and config to {pytorch_dump_path}'							)
     model.save_pretrained(lowercase__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_hub_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   parser.add_argument(
       '--is_encoder_named_decoder',
       action='store_true',
       help='If decoder has to be renamed to encoder in PyTorch model.',
   )
   parser.add_argument('--is_encoder', action='store_true', help='If model is an encoder.')
   parser.add_argument('--vocab_size', default=5_0358, type=int, help='Vocab size of model')
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )










'''simple docstring'''


import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=args.serialization_dir
     a_									=args.pruning_method
     a_									=args.threshold

     a_									=torch.load(os.path.join(lowercase__							,						"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(lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "sigmoied_threshold":
                    a_									=ThresholdBinarizer.apply(lowercase__							,						lowercase__							,						lowercase__							).sum().item()
               elif pruning_method == "l0":
                    a_		,    a_									=-0.1, 1.1
                    a_									=torch.sigmoid(lowercase__							)
                    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(lowercase__							)							)
          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__":
  lowercase											=      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''',
  )

  lowercase											=      parser.parse_args()

  main(args)


