






import argparse

import torch

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


logging.set_verbosity_info()
__UpperCAmelCase							:   Dict							=  logging.get_logger(__name__)

__UpperCAmelCase							:   Optional[int]							=  [
    ["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_ (    __snake_case     :							List[str]      )     -> Dict:





				'''simple docstring'''


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

				for parlai_name, hf_name in PATTERNS:
								snake_case__      :Optional[Any]         =   k.replace(__snake_case	,	__snake_case      )

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





def 					lowercase_ (    __snake_case     :							Optional[Any]      )     -> List[Any]:





				'''simple docstring'''


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


__UpperCAmelCase							:   Tuple							=  ["START"]





@torch.no_grad()
def 					lowercase_ (    __snake_case     :							int	,	__snake_case     :							Dict	,	__snake_case     :							List[Any]      )     -> Union[str, Any]:





				'''simple docstring'''


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

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


if __name__ == "__main__":
	__UpperCAmelCase							:   List[str]							=  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							:   Optional[Any]							=  parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)







def 					lowercase_ (    __snake_case     :							str      )     -> list:





				'''simple docstring'''


				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(__snake_case      )      )
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
	__import__("doctest").testmod()