






from string import ascii_uppercase

UpperCAmelCase__							  =						{str(ord(c) - 55): c for c in ascii_uppercase}
def 		_a       ( a					:int      ,				a					:int					)				->     str:
 if isinstance(a      ,				a					):
  raise TypeError('''int() can\'t convert non-string with explicit base'''					)
 if num < 0:
  raise ValueError('''parameter must be positive int'''					)
 if isinstance(a      ,				a					):
  raise TypeError('''\'str\' object cannot be interpreted as an integer'''					)
 if isinstance(a      ,				a					):
  raise TypeError('''\'float\' object cannot be interpreted as an integer'''					)
 if base in (0, 1):
  raise ValueError('''base must be >= 2'''					)
 if base > 36:
  raise ValueError('''base must be <= 36'''					)
 a						    = ''''''
 a						    = 0
 a						    = 0
 while div != 1:
  a    ,	a						    = divmod(a      ,				a					)
  if base >= 11 and 9 < mod < 36:
   a						    = ALPHABET_VALUES[str(a					)]
  else:
   a						    = str(a					)
  new_value += actual_value
  a						    = num // base
  a						    = div
  if div == 0:
   return str(new_value[::-1]					)
  elif div == 1:
   new_value += str(a					)
   return str(new_value[::-1]					)

 return new_value[::-1]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     for base in range(2, 37):
          for num in range(1000):
               assert int(decimal_to_any(num, base), base) == num, (
                   num,
                   base,
                   decimal_to_any(num, base),
                   int(decimal_to_any(num, base), base),
               )










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 		_a       ( a					:Optional[Any]					)				->     List[Any]:
 if k == "embeddings.weight":
  return "shared.weight"

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

 if k.startswith('''encoder'''					):
  a						    = k.replace('''.attn'''      ,				'''.self_attn'''					)
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''final_layer_norm'''					)
 elif k.startswith('''decoder'''					):
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''encoder_attn_layer_norm'''					)
  a						    = k.replace('''norm3'''      ,				'''final_layer_norm'''					)
 return k
def 		_a       ( a					:Dict					)				->     Tuple:
 a						    = [
     '''model.encoder.layernorm_embedding.weight''',
     '''model.encoder.layernorm_embedding.bias''',
     '''model.decoder.layernorm_embedding.weight''',
     '''model.decoder.layernorm_embedding.bias''',
 ]
 for k in keys:
  a						    = sd.pop(a					)
  a						    = k.replace('''layernorm_embedding'''      ,				'''layer_norm'''					)
  assert new_k not in sd
  a						    = v


UpperCAmelCase__							  =						["START"]
@torch.no_grad()
def 		_a       ( a					:Dict      ,				a					:str      ,				a					:int					)				->     int:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 a						    = model['''model''']
 a						    = BlenderbotConfig.from_json_file(a					)
 a						    = BlenderbotForConditionalGeneration(a					)
 a						    = m.model.state_dict().keys()
 a						    = []
 a						    = {}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

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


