
import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  lowercase    :   List[str]		       =				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""")
  lowercase    :   Dict		       =				parser.parse_args()

  if args.model_type == "roberta":
    lowercase    :   int		       =				RobertaForMaskedLM.from_pretrained(args.model_name)
    lowercase    :   Any		       =				'roberta'
  elif args.model_type == "gpt2":
    lowercase    :   List[str]		       =				GPTaLMHeadModel.from_pretrained(args.model_name)
    lowercase    :   Optional[Any]		       =				'transformer'

  lowercase    :   List[Any]		       =				model.state_dict()
  lowercase    :   Optional[Any]		       =				{}

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

    # Transformer Blocks #
  lowercase    :   str		       =				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"]:
          lowercase    :   Optional[int]		       =				state_dict[
              F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
          ]
      lowercase    :   Tuple		       =				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"]:
          lowercase    :   Dict		       =				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"]:
      lowercase    :   List[Any]		       =				state_dict[F'''{layer}''']
    if args.vocab_transform:
      for w in ["weight", "bias"]:
        lowercase    :   Any		       =				state_dict[F'''lm_head.dense.{w}''']
        lowercase    :   Tuple		       =				state_dict[F'''lm_head.layer_norm.{w}''']
  elif args.model_type == "gpt2":
    for w in ["weight", "bias"]:
      lowercase    :   str		       =				state_dict[F'''{prefix}.ln_f.{w}''']
    lowercase    :   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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case     :Optional[Any]			       =logging.get_logger(__name__)

__snake_case     :Tuple			       ={
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Any								=     'transfo-xl'
				A_   :     Dict								=     ['mems']
				A_   :     int								=     {
				    'n_token': 'vocab_size',
				    'hidden_size': 'd_model',
				    'num_attention_heads': 'n_head',
				    'num_hidden_layers': 'n_layer',
				}


				def __init__(							self      :  Optional[Any]						, __UpperCamelCase      :  str=267_735						, __UpperCamelCase      :  int=[20_000, 40_000, 200_000]						, __UpperCamelCase      :  List[str]=1_024						, __UpperCamelCase      :  Union[str, Any]=1_024						, __UpperCamelCase      :  Optional[Any]=16						, __UpperCamelCase      :  Dict=64						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  List[str]=False						, __UpperCamelCase      :  Optional[int]=18						, __UpperCamelCase      :  int=1_600						, __UpperCamelCase      :  Dict=1_000						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=True						, __UpperCamelCase      :  Optional[Any]=0						, __UpperCamelCase      :  Any=-1						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  List[Any]=0.1						, __UpperCamelCase      :  str=0.0						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  int="normal"						, __UpperCamelCase      :  List[Any]=0.0_1						, __UpperCamelCase      :  str=0.0_1						, __UpperCamelCase      :  Any=0.0_2						, __UpperCamelCase      :  str=1e-5						, __UpperCamelCase      :  Optional[int]=0						, **__UpperCamelCase      :  Any						, )		->     int:
											A      						=    vocab_size
											A      						=    []
											self.cutoffs.extend(__UpperCamelCase       )
											if proj_share_all_but_first:
																		A      						=    [False] + [True] * len(self.cutoffs       )
											else:
																		A      						=    [False] + [False] * len(self.cutoffs       )
											A      						=    d_model
											A      						=    d_embed
											A      						=    d_head
											A      						=    d_inner
											A      						=    div_val
											A      						=    pre_lnorm
											A      						=    n_layer
											A      						=    n_head
											A      						=    mem_len
											A      						=    same_length
											A      						=    attn_type
											A      						=    clamp_len
											A      						=    sample_softmax
											A      						=    adaptive
											A      						=    dropout
											A      						=    dropatt
											A      						=    untie_r
											A      						=    init
											A      						=    init_range
											A      						=    proj_init_std
											A      						=    init_std
											A      						=    layer_norm_epsilon
											super().__init__(eos_token_id=__UpperCamelCase						, **__UpperCamelCase       )


				@property
				def __UpperCamelCase						(							self      :  Any       )		->     Optional[int]:
											# Message copied from Transformer-XL documentation
											logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )
											return -1


				@max_position_embeddings.setter
				def __UpperCamelCase						(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]       )		->     List[Any]:
											# Message copied from Transformer-XL documentation
											raise NotImplementedError(
											    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''       )