





"""simple docstring"""




import argparse

import torch

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


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

SCREAMING_SNAKE_CASE_						       =	[
    ["""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        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Union[str, Any]:


      '''simple docstring'''


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

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

      if k.startswith("""encoder"""     ):
            SCREAMING_SNAKE_CASE		=						k.replace(""".attn"""			,						""".self_attn"""     )
            SCREAMING_SNAKE_CASE		=						k.replace("""norm1"""			,						"""self_attn_layer_norm"""     )
            SCREAMING_SNAKE_CASE		=						k.replace("""norm2"""			,						"""final_layer_norm"""     )
      elif k.startswith("""decoder"""     ):
            SCREAMING_SNAKE_CASE		=						k.replace("""norm1"""			,						"""self_attn_layer_norm"""     )
            SCREAMING_SNAKE_CASE		=						k.replace("""norm2"""			,						"""encoder_attn_layer_norm"""     )
            SCREAMING_SNAKE_CASE		=						k.replace("""norm3"""			,						"""final_layer_norm"""     )
      return k



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[
          """model.encoder.layernorm_embedding.weight""",
          """model.encoder.layernorm_embedding.bias""",
          """model.decoder.layernorm_embedding.weight""",
          """model.decoder.layernorm_embedding.bias""",
      ]
      for k in keys:
            SCREAMING_SNAKE_CASE		=						sd.pop(_SCREAMING_SNAKE_CASE     )
            SCREAMING_SNAKE_CASE		=						k.replace("""layernorm_embedding"""			,						"""layer_norm"""     )
            assert new_k not in sd
            SCREAMING_SNAKE_CASE		=						v


SCREAMING_SNAKE_CASE_						       =	["""START"""]



@torch.no_grad()
def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						torch.load(_SCREAMING_SNAKE_CASE			,						map_location="""cpu"""     )
      SCREAMING_SNAKE_CASE		=						model["""model"""]
      SCREAMING_SNAKE_CASE		=						BlenderbotConfig.from_json_file(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						BlenderbotForConditionalGeneration(_SCREAMING_SNAKE_CASE     )
      SCREAMING_SNAKE_CASE		=						m.model.state_dict().keys()
      SCREAMING_SNAKE_CASE		=						[]
      SCREAMING_SNAKE_CASE		=						{}
      for k, v in sd.items():
            if k in IGNORE_KEYS:
                  continue

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_						       =	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"""
      )
      SCREAMING_SNAKE_CASE_						       =	parser.parse_args()
      convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      if index == number_of_items:
            return 0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						knapsack(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						index + 1     )
      if weights[index] <= max_weight:
            SCREAMING_SNAKE_CASE		=						values[index] + knapsack(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						max_weight - weights[index]			,						index + 1     )
      return max(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


