


"""simple docstring"""



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  __snake_case   			=    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""")
  __snake_case   			=    parser.parse_args()

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

  __snake_case   			=    model.state_dict()
  __snake_case   			=    {}

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

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



from typing import Dict

from .base import GenericTensor, Pipeline







class _lowerCAmelCase				(      snake_case_	):







   def  lowerCamelCase		(	self						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	UpperCamelCase__=None						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       if tokenize_kwargs is None:
           snake_case    :      Optional[Any]      					=    {}

       if truncation is not None:
           if "truncation" in tokenize_kwargs:
               raise ValueError(
                   "truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"						)
           snake_case    :      List[str]      					=    truncation

       snake_case    :      Union[str, Any]      					=    tokenize_kwargs

       snake_case    :      List[Any]      					=    {}
       if return_tensors is not None:
           snake_case    :      Tuple      					=    return_tensors

       return preprocess_params, {}, postprocess_params







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	**UpperCamelCase__						)	->		Dict[str, GenericTensor]:

       '''simple docstring'''




       snake_case    :      List[Any]      					=    self.framework
       snake_case    :      str      					=    self.tokenizer(UpperCamelCase__						,	return_tensors=UpperCamelCase__						,	**UpperCamelCase__						)
       return model_inputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						)	->		Tuple:

       '''simple docstring'''




       snake_case    :      int      					=    self.model(**UpperCamelCase__						)
       return model_outputs







   def  lowerCamelCase		(	self						,	UpperCamelCase__						,	UpperCamelCase__=False						)	->		Union[str, Any]:

       '''simple docstring'''




       if return_tensors:
           return model_outputs[0]
       if self.framework == "pt":
           return model_outputs[0].tolist()
       elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







   def __call__(	self						,	*UpperCamelCase__						,	**UpperCamelCase__						)	->		Union[str, Any]:

       '''simple docstring'''




       return super().__call__(*UpperCamelCase__						,	**UpperCamelCase__						)





