


'''simple docstring'''






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
  __A :  List[Any]    							=				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 :  List[Any]    							=				parser.parse_args()

  if args.model_type == "roberta":
    __A :  List[str]    							=				RobertaForMaskedLM.from_pretrained(args.model_name)
    __A :  Tuple    							=				"roberta"
  elif args.model_type == "gpt2":
    __A :  Dict    							=				GPTaLMHeadModel.from_pretrained(args.model_name)
    __A :  Optional[int]    							=				"transformer"

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

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

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






import requests







def     UpperCamelCase_      (   A__ :       str							,   A__ :       str ):
  '''simple docstring'''





  lowerCAmelCase_      :				Optional[Any]      =       {"""Content-Type""": """application/json"""}
  lowerCAmelCase_      :				Union[str, Any]      =       requests.post(A__							,   json={"""text""": message_body}							,   headers=A__ )
  if response.status_code != 2_00:
    lowerCAmelCase_      :				Dict      =       (
        """Request to slack returned an error """
        f'{response.status_code}, the response is:\n{response.text}'
    )
    raise ValueError(A__ )


if __name__ == "__main__":
  # Set the slack url to the one provided by Slack when you create the webhook at
  # https://my.slack.com/services/new/incoming-webhook/
  send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")



