






def 		_a       ( a					:List[Any]					)				->     str:
 return [
     {
         0: [1, 2],
         1: [0, 2],
         2: [0, 1, 3, 5],
         3: [2, 4],
         4: [3],
         5: [2, 6, 8],
         6: [5, 7],
         7: [6, 8],
         8: [5, 7],
     },
     {
         0: [6],
         1: [9],
         2: [4, 5],
         3: [4],
         4: [2, 3],
         5: [2],
         6: [0, 7],
         7: [6],
         8: [],
         9: [1],
     },
     {
         0: [4],
         1: [6],
         2: [],
         3: [5, 6, 7],
         4: [0, 6],
         5: [3, 8, 9],
         6: [1, 3, 4, 7],
         7: [3, 6, 8, 9],
         8: [5, 7],
         9: [5, 7],
     },
     {
         0: [1, 3],
         1: [0, 2, 4],
         2: [1, 3, 4],
         3: [0, 2, 4],
         4: [1, 2, 3],
     },
 ][index]
def 		_a       ( a					:dict[int, list[int]]					)				->     list[tuple[int, int]]:
 a						    = 0
 a						    = len(a					)  # No of vertices in graph
 a						    = [0] * n
 a						    = [False] * n

 def dfs(a					:Tuple      ,				a					:Any      ,				a					:Optional[int]      ,				a					:Any					):
  a						    = True
  a						    = id_
  id_ += 1
  for to in graph[at]:
   if to == parent:
    pass
   elif not visited[to]:
    dfs(a      ,				a      ,				a      ,				id_					)
    a						    = min(low[at]      ,				low[to]					)
    if id_ <= low[to]:
     bridges.append((at, to) if at < to else (to, at)					)
   else:
    # This edge is a back edge and cannot be a bridge
    a						    = min(low[at]      ,				low[to]					)

 a						    = []
 for i in range(a					):
  if not visited[i]:
   dfs(a      ,				-1      ,				a      ,				id_					)
 return bridges


if __name__ == "__main__":
     import doctest

     doctest.testmod()










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)


