






'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Dict:
							lowerCamelCase__						,	lowerCamelCase__      :			Any 						= emb.weight.shape
							lowerCamelCase__      :			Union[str, Any] 						= nn.Linear(UpperCamelCase       ,    UpperCamelCase       ,    bias=UpperCamelCase   )
							lowerCamelCase__      :			List[Any] 						= emb.weight.data
							return lin_layer





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase="facebook/mbart-large-en-ro"       ,    UpperCamelCase=False       ,    UpperCamelCase=False   )   -> str:
							lowerCamelCase__      :			Any 						= torch.load(UpperCamelCase       ,    map_location="""cpu"""   )["""model"""]
							remove_ignore_keys_(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= state_dict["""encoder.embed_tokens.weight"""].shape[0]

							lowerCamelCase__      :			Dict 						= MBartConfig.from_pretrained(UpperCamelCase       ,    vocab_size=UpperCamelCase   )
							if mbart_aa and finetuned:
														lowerCamelCase__      :			Tuple 						= """relu"""

							lowerCamelCase__      :			Optional[int] 						= state_dict["""decoder.embed_tokens.weight"""]
							lowerCamelCase__      :			Optional[int] 						= MBartForConditionalGeneration(UpperCamelCase   )
							model.model.load_state_dict(UpperCamelCase   )

							if finetuned:
														lowerCamelCase__      :			Union[str, Any] 						= make_linear_from_emb(model.model.shared   )

							return model


if __name__ == "__main__":
			_A			:    List[str]					  =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			:    Union[str, Any]					  =parser.parse_args()
			_A			:    Dict					  =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)









'''simple docstring'''







# 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 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			str 						= [False] * len(UpperCamelCase   )
							lowerCamelCase__      :			str 						= [-1] * len(UpperCamelCase   )

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

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

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

							return True


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



print(check_bipartite_dfs(graph))

