

import argparse

import torch

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


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

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       snake_case__     (       SCREAMING_SNAKE_CASE_						:       str     ):



     '''simple docstring'''





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

     for parlai_name, hf_name in PATTERNS:
          lowercase__       :  int =			k.replace(UpperCamelCase__      ,	UpperCamelCase__     )

     if k.startswith('encoder'     ):
          lowercase__       :  List[Any] =			k.replace('.attn'      ,	'.self_attn'     )
          lowercase__       :  Any =			k.replace('norm1'      ,	'self_attn_layer_norm'     )
          lowercase__       :  List[str] =			k.replace('norm2'      ,	'final_layer_norm'     )
     elif k.startswith('decoder'     ):
          lowercase__       :  Optional[int] =			k.replace('norm1'      ,	'self_attn_layer_norm'     )
          lowercase__       :  Dict =			k.replace('norm2'      ,	'encoder_attn_layer_norm'     )
          lowercase__       :  Dict =			k.replace('norm3'      ,	'final_layer_norm'     )
     return k

def       snake_case__     (       SCREAMING_SNAKE_CASE_						:       Tuple     ):



     '''simple docstring'''





     lowercase__       :  Dict =			[
         'model.encoder.layernorm_embedding.weight',
         'model.encoder.layernorm_embedding.bias',
         'model.decoder.layernorm_embedding.weight',
         'model.decoder.layernorm_embedding.bias',
     ]
     for k in keys:
          lowercase__       :  Dict =			sd.pop(UpperCamelCase__     )
          lowercase__       :  Union[str, Any] =			k.replace('layernorm_embedding'      ,	'layer_norm'     )
          assert new_k not in sd
          lowercase__       :  Dict =			v


snake_case_         =					["START"]

@torch.no_grad()
def       snake_case__     (       SCREAMING_SNAKE_CASE_						:       List[Any]      ,	SCREAMING_SNAKE_CASE_						:       Any      ,	SCREAMING_SNAKE_CASE_						:       Optional[int]     ):



     '''simple docstring'''





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

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


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





def 	_lowerCAmelCase					( UpperCamelCase__:  str				,       UpperCamelCase__:  int				,       UpperCamelCase__:  Any=False    )			-> str:





	"""simple docstring"""







	if isinstance(UpperCamelCase__				,       UpperCamelCase__    ) and isinstance(UpperCamelCase__				,       UpperCamelCase__    ):
		A             =					len(set_a.intersection(UpperCamelCase__    )    )

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
		else:
			A             =					len(set_a.union(UpperCamelCase__    )    )

		return intersection / union

	if isinstance(UpperCamelCase__				,       (list, tuple)    ) and isinstance(UpperCamelCase__				,       (list, tuple)    ):
		A             =					[element for element in set_a if element in set_b]

		if alternative_union:
			A             =					len(UpperCamelCase__    ) + len(UpperCamelCase__    )
			return len(UpperCamelCase__    ) / union
		else:
			A             =					set_a + [element for element in set_b if element not in set_a]
			return len(UpperCamelCase__    ) / len(UpperCamelCase__    )

		return len(UpperCamelCase__    ) / len(UpperCamelCase__    )
	return None


if __name__ == "__main__":
				_lowercase							:   List[Any]						=						{"a", "b", "c", "d", "e"}
				_lowercase							:   List[Any]						=						{"c", "d", "e", "f", "h", "i"}
				print(jaccard_similarity(set_a, set_b))



