





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 _lowerCAmelCase							:					Any		    =		argparse.ArgumentParser(
     description=(
         '''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
         ''' Distillation'''
     )
 )
 parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
 parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
 parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
 parser.add_argument('''--vocab_transform''', action='''store_true''')
 _lowerCAmelCase							:					Union[str, Any]		    =		parser.parse_args()

 if args.model_type == "roberta":
  _lowerCAmelCase							:					int		    =		RobertaForMaskedLM.from_pretrained(args.model_name)
  _lowerCAmelCase							:					int		    =		"""roberta"""
 elif args.model_type == "gpt2":
  _lowerCAmelCase							:					List[str]		    =		GPTaLMHeadModel.from_pretrained(args.model_name)
  _lowerCAmelCase							:					Dict		    =		"""transformer"""

 _lowerCAmelCase							:					Union[str, Any]		    =		model.state_dict()
 _lowerCAmelCase							:					Optional[Any]		    =		{}

 # Embeddings #
 if args.model_type == "gpt2":
  for param_name in ["wte.weight", "wpe.weight"]:
   _lowerCAmelCase							:					Optional[int]		    =		state_dict[F'''{prefix}.{param_name}''']
 else:
  for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
   _lowerCAmelCase							:					Optional[Any]		    =		F'''{prefix}.embeddings.{w}.weight'''
   _lowerCAmelCase							:					List[Any]		    =		state_dict[param_name]
  for w in ["weight", "bias"]:
   _lowerCAmelCase							:					Optional[int]		    =		F'''{prefix}.embeddings.LayerNorm.{w}'''
   _lowerCAmelCase							:					Optional[Any]		    =		state_dict[param_name]

    # Transformer Blocks #
 _lowerCAmelCase							:					Tuple		    =		0
 for teacher_idx in [0, 2, 4, 7, 9, 11]:
  if args.model_type == "gpt2":
   for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
    for w in ["weight", "bias"]:
     _lowerCAmelCase							:					Optional[Any]		    =		state_dict[
         F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
     ]
   _lowerCAmelCase							:					str		    =		state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
  else:
   for layer in [
       "attention.self.query",
       "attention.self.key",
       "attention.self.value",
       "attention.output.dense",
       "attention.output.LayerNorm",
       "intermediate.dense",
       "output.dense",
       "output.LayerNorm",
   ]:
    for w in ["weight", "bias"]:
     _lowerCAmelCase							:					Optional[Any]		    =		state_dict[
         F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
     ]
  std_idx += 1

 # Language Modeling Head ###s
 if args.model_type == "roberta":
  for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
   _lowerCAmelCase							:					Tuple		    =		state_dict[F'''{layer}''']
  if args.vocab_transform:
   for w in ["weight", "bias"]:
    _lowerCAmelCase							:					List[Any]		    =		state_dict[F'''lm_head.dense.{w}''']
    _lowerCAmelCase							:					Any		    =		state_dict[F'''lm_head.layer_norm.{w}''']
 elif args.model_type == "gpt2":
  for w in ["weight", "bias"]:
   _lowerCAmelCase							:					str		    =		state_dict[F'''{prefix}.ln_f.{w}''']
  _lowerCAmelCase							:					List[Any]		    =		state_dict["""lm_head.weight"""]

 print(F'''N layers selected for distillation: {std_idx}''')
 print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

 print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
 torch.save(compressed_sd, args.dump_checkpoint)





"""simple docstring"""







import re

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   list:

  """simple docstring"""




  return [char.split() for char in re.split(R'[^ a-z A-Z 0-9 \s]'    ,   str_						)]

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  lowerCAmelCase_   :				str								= split_input(str_						)
  return "".join(
      [''.join([char.capitalize() for char in sub_str]						) for sub_str in string_split]						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				bool    ,   lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  try:
    lowerCAmelCase_   :				int								= split_input(lowerCAmelCase__						)
    if upper:
      lowerCAmelCase_   :				Optional[int]								= ''.join(
          [
              separator.join([char.upper() for char in sub_str]						)
              for sub_str in string_split
          ]						)
    else:
      lowerCAmelCase_   :				Any								= ''.join(
          [
              separator.join([char.lower() for char in sub_str]						)
              for sub_str in string_split
          ]						)
    return res_str
  except IndexError:
    return "not valid string"

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  return to_simple_case(lowerCAmelCase__						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str						)						->   str:

  """simple docstring"""




  try:
    lowerCAmelCase_   :				Dict								= to_simple_case(lowerCAmelCase__						)
    return res_str[0].lower() + res_str[1:]
  except IndexError:
    return "not valid string"

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				bool						)						->   str:

  """simple docstring"""




  return to_complex_case(lowerCAmelCase__    ,   lowerCAmelCase__    ,   '_'						)

def 				UpperCamelCase_    (    lowerCAmelCase__      :				str    ,   lowerCAmelCase__      :				bool						)						->   str:

  """simple docstring"""




  return to_complex_case(lowerCAmelCase__    ,   lowerCAmelCase__    ,   '-'						)


if __name__ == "__main__":
     __import__("""doctest""").testmod()





