



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)








from graphs.minimum_spanning_tree_kruskal import kruskal

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Tuple:
   _A      =       9
   _A      =       [
       [0, 1, 4],
       [0, 7, 8],
       [1, 2, 8],
       [7, 8, 7],
       [7, 6, 1],
       [2, 8, 2],
       [8, 6, 6],
       [2, 3, 7],
       [2, 5, 4],
       [6, 5, 2],
       [3, 5, 14],
       [3, 4, 9],
       [5, 4, 10],
       [1, 7, 11],
   ]

   _A      =       kruskal(_snake_case	,   _snake_case				)

   _A      =       [
       [7, 6, 1],
       [2, 8, 2],
       [6, 5, 2],
       [0, 1, 4],
       [2, 5, 4],
       [2, 3, 7],
       [0, 7, 8],
       [3, 4, 9],
   ]

   assert sorted(_snake_case				) == sorted(_snake_case				)



