



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     Optional[Any]:
   _A      =       [
       '''encoder.version''',
       '''decoder.version''',
       '''model.encoder.version''',
       '''model.decoder.version''',
       '''_float_tensor''',
       '''decoder.output_projection.weight''',
   ]
   for k in ignore_keys:
      state_dict.pop(_snake_case	,   _snake_case				)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]				)  ->     Optional[int]:
   _A  ,    _A      =       emb.weight.shape
   _A      =       nn.Linear(_snake_case	,   _snake_case	,   bias=_snake_case				)
   _A      =       emb.weight.data
   return lin_layer

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

   _A      =       MBartConfig.from_pretrained(_snake_case	,   vocab_size=_snake_case				)
   if mbart_aa and finetuned:
      _A      =       '''relu'''

   _A      =       state_dict['''decoder.embed_tokens.weight''']
   _A      =       MBartForConditionalGeneration(_snake_case				)
   model.model.load_state_dict(_snake_case				)

   if finetuned:
      _A      =       make_linear_from_emb(model.model.shared				)

   return model


if __name__ == "__main__":
   UpperCAmelCase_		 =					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""")
   UpperCAmelCase_		 =					parser.parse_args()
   UpperCAmelCase_		 =					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)











class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       def __init__(				self	:List[Any]):



          """simple docstring"""

          _lowercase              ={}





       def 				UpperCamelCase__						(				self	:str):



          """simple docstring"""

          print(self.vertex)
          for i in self.vertex:
             print(snake_case,      ' -> ',      ' -> '.join([str(snake_case) for j in self.vertex[i]]))





       def 				UpperCamelCase__						(				self	:Any,      snake_case	:int,      snake_case	:int):



          """simple docstring"""

          if from_vertex in self.vertex:
             self.vertex[from_vertex].append(snake_case)
          else:
             # else make a new vertex
             _lowercase              =[to_vertex]





       def 				UpperCamelCase__						(				self	:Union[str, Any]):



          """simple docstring"""

          _lowercase              =[False] * len(self.vertex)

          # call the recursive helper function
          for i in range(len(self.vertex)):
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:list):



          """simple docstring"""

          _lowercase              =True

          print(snake_case,      end=' ')

          # Recur for all the vertices that are adjacent to this node
          for i in self.vertex:
             if not visited[i]:
                self.dfs_recursive(snake_case,      snake_case)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE   =  Graph()
    g.add_edge(0, 1)
    g.add_edge(0, 2)
    g.add_edge(1, 2)
    g.add_edge(2, 0)
    g.add_edge(2, 3)
    g.add_edge(3, 3)

    g.print_graph()
    print("DFS:")
    g.dfs()

    # OUTPUT:
    # 0  ->  1 -> 2
    # 1  ->  2
    # 2  ->  0 -> 3
    # 3  ->  3
    # DFS:
    #  0 1 2 3


