

'''simple docstring'''

import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

       UpperCAmelCase					 =	model.state_dict()
       UpperCAmelCase					 =	{}

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

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

def 					__UpperCamelCase				(						lowercase__    :	list[int]			):






     '''simple docstring'''





     if not nums:  # Makes sure that the list is not empty
          raise ValueError('List is empty'			)

     __lowercase      		=sum(lowercase__			) / len(lowercase__			)  # Calculate the average
     return sum(abs(x - average			) for x in nums			) / len(lowercase__			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()


