


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        UpperCamelCase_(				_A   :Tuple  )->		Optional[Any]:
			if k == "embeddings.weight":
						return "shared.weight"

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

			if k.startswith("encoder"  ):
						UpperCamelCase__					=       k.replace(".attn"				,     ".self_attn"  )
						UpperCamelCase__					=       k.replace("norm1"				,     "self_attn_layer_norm"  )
						UpperCamelCase__					=       k.replace("norm2"				,     "final_layer_norm"  )
			elif k.startswith("decoder"  ):
						UpperCamelCase__					=       k.replace("norm1"				,     "self_attn_layer_norm"  )
						UpperCamelCase__					=       k.replace("norm2"				,     "encoder_attn_layer_norm"  )
						UpperCamelCase__					=       k.replace("norm3"				,     "final_layer_norm"  )
			return k
def        UpperCamelCase_(				_A   :str  )->		Tuple:
			UpperCamelCase__					=       [
			    "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__					=       sd.pop(_A  )
						UpperCamelCase__					=       k.replace("layernorm_embedding"				,     "layer_norm"  )
						assert new_k not in sd
						UpperCamelCase__					=       v


__UpperCamelCase    		=       ['START']
@torch.no_grad()
def        UpperCamelCase_(				_A   :Union[str, Any]				,     _A   :List[str]				,     _A   :Any  )->		Optional[int]:
			UpperCamelCase__					=       torch.load(_A				,     map_location="cpu"  )
			UpperCamelCase__					=       model["model"]
			UpperCamelCase__					=       BlenderbotConfig.from_json_file(_A  )
			UpperCamelCase__					=       BlenderbotForConditionalGeneration(_A  )
			UpperCamelCase__					=       m.model.state_dict().keys()
			UpperCamelCase__					=       []
			UpperCamelCase__					=       {}
			for k, v in sd.items():
						if k in IGNORE_KEYS:
									continue

						UpperCamelCase__					=       rename_state_dict_key(_A  )
						if new_k not in valid_keys:
									failures.append([k, new_k]  )
						else:
									UpperCamelCase__					=       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)






def        UpperCamelCase_(				_A   :str  )->		str:
			UpperCamelCase__					=       ""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups
def        UpperCamelCase_(				_A   :str  )->		dict[str, str]:
			UpperCamelCase__					=       [chr(i + 65  ) for i in range(26  )]
			# Remove duplicate characters from key
			UpperCamelCase__					=       remove_duplicates(key.upper()  )
			UpperCamelCase__					=       len(_A  )
			# First fill cipher with key characters
			UpperCamelCase__					=       {alphabet[i]: char for i, char in enumerate(_A  )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(_A  )				,     26  ):
						UpperCamelCase__					=       alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									UpperCamelCase__					=       alphabet[i - offset]
						UpperCamelCase__					=       char
			return cipher_alphabet
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			return "".join(cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			UpperCamelCase__					=       {v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				)->		None:
			UpperCamelCase__					=       input("Enter message to encode or decode: "  ).strip()
			UpperCamelCase__					=       input("Enter keyword: "  ).strip()
			UpperCamelCase__					=       input("Encipher or decipher? E/D:"  ).strip()[0].lower()
			try:
						UpperCamelCase__					=       {"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"  )
			UpperCamelCase__					=       create_cipher_map(_A  )
			print(func(_A				,     _A  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()


