
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

     __magic_name__  							=	model.state_dict()
     __magic_name__  							=	{}

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

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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if any(not isinstance(__lowerCAmelCase     ,		__lowerCAmelCase				) or x < 0 for x in sequence				):
        raise TypeError("Sequence must be list of non-negative integers"				)
    for _ in range(len(__lowerCAmelCase				)				):
        for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCAmelCase     ,		sequence[1:]				)				):
            if rod_upper > rod_lower:
                sequence[i] -= rod_upper - rod_lower
                sequence[i + 1] += rod_upper - rod_lower
    return sequence


if __name__ == "__main__":
     assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
     assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]



