


"""simple docstring"""







import argparse

import torch

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


logging.set_verbosity_info()
lowerCamelCase_   :   Optional[int]									=  logging.get_logger(__name__)

lowerCamelCase_   :   List[Any]									=  [
    ["""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						(						lowercase		):







    """simple docstring"""
    if k == "embeddings.weight":
        return "shared.weight"

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

    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						(						lowercase		):







    """simple docstring"""
    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(lowercase		)
        a 		=k.replace('''layernorm_embedding'''       ,    '''layer_norm'''		)
        assert new_k not in sd
        a 		=v


lowerCamelCase_   :   List[str]									=  ["""START"""]
@torch.no_grad()
def _A						(						lowercase       ,    lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=torch.load(lowercase       ,    map_location='''cpu'''		)
    a 		=model['''model''']
    a 		=BlenderbotConfig.from_json_file(lowercase		)
    a 		=BlenderbotForConditionalGeneration(lowercase		)
    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(lowercase		)
        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(lowercase		)
    m.model.load_state_dict(lowercase       ,    strict=lowercase		)
    m.half()
    m.save_pretrained(lowercase		)


if __name__ == "__main__":
       lowerCamelCase_   :   Union[str, Any]									=  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"""
       )
       lowerCamelCase_   :   Dict									=  parser.parse_args()
       convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 					_A			(__a    ,				__a    ,				__a							) ->       Dict:
    """simple docstring"""






    if gpta_config_file == "":
        SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		GPTaConfig()
    else:
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		GPTaConfig.from_json_file(__a							)
    SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		GPTaModel(__a							)

    # Load weights from numpy
    load_tf_weights_in_gpta(__a    ,				__a    ,				__a							)

    # Save pytorch-model
    SCREAMING_SNAKE_CASE_      :  List[str]    						=		pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
    SCREAMING_SNAKE_CASE_      :  List[Any]    						=		pytorch_dump_folder_path + '''/''' + CONFIG_NAME
    print(f'Save PyTorch model to {pytorch_weights_dump_path}'							)
    torch.save(model.state_dict()    ,				__a							)
    print(f'Save configuration file to {pytorch_config_dump_path}'							)
    with open(__a    ,				'''w'''    ,				encoding='''utf-8'''							) as f:
        f.write(config.to_json_string()							)


if __name__ == "__main__":
  UpperCAmelCase_   : str   			= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  parser.add_argument(
      """--gpt2_config_file""",
      default="""""",
      type=str,
      help=(
          """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
          """This specifies the model architecture."""
      ),
  )
  UpperCAmelCase_   : Union[str, Any]   			= parser.parse_args()
  convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)


