

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
 __A      		=     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''')
 __A      		=     parser.parse_args()

 if args.model_type == "roberta":
  __A      		=     RobertaForMaskedLM.from_pretrained(args.model_name)
  __A      		=     '''roberta'''
 elif args.model_type == "gpt2":
  __A      		=     GPTaLMHeadModel.from_pretrained(args.model_name)
  __A      		=     '''transformer'''

 __A      		=     model.state_dict()
 __A      		=     {}

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

    # Transformer Blocks #
 __A      		=     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"]:
     __A      		=     state_dict[
         f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
     ]
   __A      		=     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"]:
     __A      		=     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"]:
   __A      		=     state_dict[f'''{layer}''']
  if args.vocab_transform:
   for w in ["weight", "bias"]:
    __A      		=     state_dict[f'''lm_head.dense.{w}''']
    __A      		=     state_dict[f'''lm_head.layer_norm.{w}''']
 elif args.model_type == "gpt2":
  for w in ["weight", "bias"]:
   __A      		=     state_dict[f'''{prefix}.ln_f.{w}''']
  __A      		=     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)




import cmath
import math



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     complex:



  """simple docstring"""






  _snake_case						=							math.radians(_UpperCamelCase					)
  _snake_case						=							math.radians(_UpperCamelCase					)

  # Convert voltage and current to rectangular form
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)
  _snake_case						=							cmath.rect(_UpperCamelCase			,	_UpperCamelCase					)

  # Calculate apparent power
  return voltage_rect * current_rect


if __name__ == "__main__":
 import doctest

 doctest.testmod()

