





import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case    :       List[str]		       =    logging.get_logger(__name__)

def     a_       (					lowerCAmelCase_ :     str,      lowerCAmelCase_ :     str      ):
   __lowerCAmelCase				      =      RobertaPreLayerNormConfig.from_pretrained(
       lowerCAmelCase_,      architectures=['RobertaPreLayerNormForMaskedLM']      )

   # convert state_dict
   __lowerCAmelCase				      =      torch.load(hf_hub_download(repo_id=lowerCAmelCase_,      filename='pytorch_model.bin'      )      )
   __lowerCAmelCase				      =      {}
   for tensor_key, tensor_value in original_state_dict.items():
      # The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
      if tensor_key.startswith('roberta.'      ):
         __lowerCAmelCase				      =      'roberta_prelayernorm.' + tensor_key[len('roberta.'      ) :]

      # The original implementation contains weights which are not used, remove them from the state_dict
      if tensor_key.endswith('.self.LayerNorm.weight'      ) or tensor_key.endswith('.self.LayerNorm.bias'      ):
         continue

      __lowerCAmelCase				      =      tensor_value

   __lowerCAmelCase				      =      RobertaPreLayerNormForMaskedLM.from_pretrained(
       pretrained_model_name_or_path=lowerCAmelCase_,      config=lowerCAmelCase_,      state_dict=lowerCAmelCase_      )
   model.save_pretrained(lowerCAmelCase_      )

   # convert tokenizer
   __lowerCAmelCase				      =      AutoTokenizer.from_pretrained(lowerCAmelCase_      )
   tokenizer.save_pretrained(lowerCAmelCase_      )


if __name__ == "__main__":
     _snake_case    :       str		       =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--checkpoint-repo',
         default=None,
         type=str,
         required=True,
         help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     _snake_case    :       Union[str, Any]		       =    parser.parse_args()
     convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__							  =						logging.get_logger(__name__)

UpperCAmelCase__							  =						[
    ["attention", "attn"],
    ["encoder_attention", "encoder_attn"],
    ["q_lin", "q_proj"],
    ["k_lin", "k_proj"],
    ["v_lin", "v_proj"],
    ["out_lin", "out_proj"],
    ["norm_embeddings", "layernorm_embedding"],
    ["position_embeddings", "embed_positions"],
    ["embeddings", "embed_tokens"],
    ["ffn.lin", "fc"],
]
def 		_a       ( a					:Optional[Any]					)				->     List[Any]:
 if k == "embeddings.weight":
  return "shared.weight"

 for parlai_name, hf_name in PATTERNS:
  a						    = k.replace(a      ,				a					)

 if k.startswith('''encoder'''					):
  a						    = k.replace('''.attn'''      ,				'''.self_attn'''					)
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''final_layer_norm'''					)
 elif k.startswith('''decoder'''					):
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''encoder_attn_layer_norm'''					)
  a						    = k.replace('''norm3'''      ,				'''final_layer_norm'''					)
 return k
def 		_a       ( a					:Dict					)				->     Tuple:
 a						    = [
     '''model.encoder.layernorm_embedding.weight''',
     '''model.encoder.layernorm_embedding.bias''',
     '''model.decoder.layernorm_embedding.weight''',
     '''model.decoder.layernorm_embedding.bias''',
 ]
 for k in keys:
  a						    = sd.pop(a					)
  a						    = k.replace('''layernorm_embedding'''      ,				'''layer_norm'''					)
  assert new_k not in sd
  a						    = v


UpperCAmelCase__							  =						["START"]
@torch.no_grad()
def 		_a       ( a					:Dict      ,				a					:str      ,				a					:int					)				->     int:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 a						    = model['''model''']
 a						    = BlenderbotConfig.from_json_file(a					)
 a						    = BlenderbotForConditionalGeneration(a					)
 a						    = m.model.state_dict().keys()
 a						    = []
 a						    = {}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  a						    = rename_state_dict_key(a					)
  if new_k not in valid_keys:
   failures.append([k, new_k]					)
  else:
   a						    = v
 if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
  rename_layernorm_keys(a					)
 m.model.load_state_dict(a      ,				strict=a					)
 m.half()
 m.save_pretrained(a					)


if __name__ == "__main__":
     UpperCAmelCase__							  =						argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("--src_path", type=str, help="like blenderbot-model.bin")
     parser.add_argument("--save_dir", default="hf_blenderbot", type=str, help="Where to save converted model.")
     parser.add_argument(
         "--hf_config_json", default="blenderbot-3b-config.json", type=str, help="Path to config to use"
     )
     UpperCAmelCase__							  =						parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


