



'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	List[str]				)  ->     Union[str, Any]:
 _snake_case    :  Union[str, Any]					=					[
     """encoder.version""",
     """decoder.version""",
     """model.encoder.version""",
     """model.decoder.version""",
     """_float_tensor""",
     """decoder.output_projection.weight""",
 ]
 for k in ignore_keys:
  state_dict.pop(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				)



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Any				)  ->     List[Any]:
 _snake_case	,    _snake_case    :  Any					=					emb.weight.shape
 _snake_case    :  List[Any]					=					nn.Linear(SCREAMING_SNAKE_CASE__				,       SCREAMING_SNAKE_CASE__				,       bias=SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Any					=					emb.weight.data
 return lin_layer



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Tuple				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]="facebook/mbart-large-en-ro"				,       SCREAMING_SNAKE_CASE__       :	List[str]=False				,       SCREAMING_SNAKE_CASE__       :	Optional[Any]=False				)  ->     Optional[int]:
 _snake_case    :  Tuple					=					torch.load(SCREAMING_SNAKE_CASE__				,       map_location="""cpu"""				)["""model"""]
 remove_ignore_keys_(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  List[Any]					=					state_dict["""encoder.embed_tokens.weight"""].shape[0]

 _snake_case    :  Optional[Any]					=					MBartConfig.from_pretrained(SCREAMING_SNAKE_CASE__				,       vocab_size=SCREAMING_SNAKE_CASE__				)
 if mbart_aa and finetuned:
  _snake_case    :  Optional[int]					=					"""relu"""

 _snake_case    :  Any					=					state_dict["""decoder.embed_tokens.weight"""]
 _snake_case    :  Union[str, Any]					=					MBartForConditionalGeneration(SCREAMING_SNAKE_CASE__				)
 model.model.load_state_dict(SCREAMING_SNAKE_CASE__				)

 if finetuned:
  _snake_case    :  Optional[Any]					=					make_linear_from_emb(model.model.shared				)

 return model


if __name__ == "__main__":
  a__    =	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
  )
  parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
  parser.add_argument(
      """--hf_config""",
      default="""facebook/mbart-large-cc25""",
      type=str,
      help="""Which huggingface architecture to use: mbart-large""",
  )
  parser.add_argument("""--mbart_50""", action="""store_true""", help="""whether the model is mMART-50 checkpoint""")
  parser.add_argument("""--finetuned""", action="""store_true""", help="""whether the model is a fine-tuned checkpoint""")
  a__    =	parser.parse_args()
  a__    =	convert_fairseq_mbart_checkpoint_from_disk(
      args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
  )
  model.save_pretrained(args.pytorch_dump_folder_path)





# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.



def       lowercase       (   SCREAMING_SNAKE_CASE__       :	Dict				)  ->     Union[str, Any]:
 _snake_case    :  str					=					[False] * len(SCREAMING_SNAKE_CASE__				)
 _snake_case    :  Dict					=					[-1] * len(SCREAMING_SNAKE_CASE__				)

 def dfs(SCREAMING_SNAKE_CASE__       :	Optional[int]				,       SCREAMING_SNAKE_CASE__       :	Union[str, Any]				):
  _snake_case    :  Optional[Any]					=					True
  _snake_case    :  Tuple					=					c
  for u in graph[v]:
   if not visited[u]:
    dfs(SCREAMING_SNAKE_CASE__				,       1 - c				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  if not visited[i]:
   dfs(SCREAMING_SNAKE_CASE__				,       0				)

 for i in range(len(SCREAMING_SNAKE_CASE__				)				):
  for j in graph[i]:
   if color[i] == color[j]:
    return False

 return True


# Adjacency list of graph
a__    =	{0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}

print(check_bipartite_dfs(graph))
