






'''simple docstring'''

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 				snake_case       (  a_				:			List[Any]						)							->					Optional[int]:





      """simple docstring"""

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

      for parlai_name, hf_name in PATTERNS:
            UpperCamelCase_					: Dict            =				k.replace(a_     , a_						)

      if k.startswith("""encoder"""						):
            UpperCamelCase_					: Tuple            =				k.replace(""".attn"""     , """.self_attn"""						)
            UpperCamelCase_					: Any            =				k.replace("""norm1"""     , """self_attn_layer_norm"""						)
            UpperCamelCase_					: Any            =				k.replace("""norm2"""     , """final_layer_norm"""						)
      elif k.startswith("""decoder"""						):
            UpperCamelCase_					: int            =				k.replace("""norm1"""     , """self_attn_layer_norm"""						)
            UpperCamelCase_					: Optional[Any]            =				k.replace("""norm2"""     , """encoder_attn_layer_norm"""						)
            UpperCamelCase_					: Optional[Any]            =				k.replace("""norm3"""     , """final_layer_norm"""						)
      return k



def 				snake_case       (  a_				:			Dict						)							->					Any:





      """simple docstring"""

      UpperCamelCase_					: int            =				[
          """model.encoder.layernorm_embedding.weight""",
          """model.encoder.layernorm_embedding.bias""",
          """model.decoder.layernorm_embedding.weight""",
          """model.decoder.layernorm_embedding.bias""",
      ]
      for k in keys:
            UpperCamelCase_					: Dict            =				sd.pop(a_						)
            UpperCamelCase_					: str            =				k.replace("""layernorm_embedding"""     , """layer_norm"""						)
            assert new_k not in sd
            UpperCamelCase_					: str            =				v


UpperCamelCase      					=["START"]



@torch.no_grad()
def 				snake_case       (  a_				:			List[str]     , a_				:			List[Any]     , a_				:			int						)							->					List[str]:





      """simple docstring"""

      UpperCamelCase_					: Optional[Any]            =				torch.load(a_     , map_location="""cpu"""						)
      UpperCamelCase_					: Union[str, Any]            =				model["""model"""]
      UpperCamelCase_					: Optional[Any]            =				BlenderbotConfig.from_json_file(a_						)
      UpperCamelCase_					: int            =				BlenderbotForConditionalGeneration(a_						)
      UpperCamelCase_					: Any            =				m.model.state_dict().keys()
      UpperCamelCase_					: int            =				[]
      UpperCamelCase_					: List[str]            =				{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

            UpperCamelCase_					: int            =				rename_state_dict_key(a_						)
            if new_k not in valid_keys:
                  failures.append([k, new_k]						)
            else:
                  UpperCamelCase_					: Tuple            =				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)














'''simple docstring'''

from abc import ABC, abstractmethod
from argparse import ArgumentParser




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 @staticmethod
 @abstractmethod
 def 	_UpperCAmelCase							(				__lowerCAmelCase			):
       raise NotImplementedError()





 @abstractmethod
 def 	_UpperCAmelCase							(				self			):
       raise NotImplementedError()






