



'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Any  )		->int:






							'''simple docstring'''
							_lowercase							:		Optional[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(snake_case_ ,       snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Union[str, Any]  )		->List[Any]:






							'''simple docstring'''
							_lowercase			, _lowercase							:		Any				=   emb.weight.shape
							_lowercase							:		Optional[int]				=   nn.Linear(snake_case_ ,       snake_case_ ,       bias=snake_case_  )
							_lowercase							:		Dict				=   emb.weight.data
							return lin_layer





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Tuple ,       snake_case_    :     Optional[Any]="uclanlp/plbart-base" ,       snake_case_    :     List[Any]=False ,       snake_case_    :     Tuple=False  )		->List[Any]:






							'''simple docstring'''
							_lowercase							:		List[Any]				=   torch.load(snake_case_ ,       map_location='''cpu'''  )['''model''']
							remove_ignore_keys_(snake_case_  )
							_lowercase							:		Any				=   state_dict['''encoder.embed_tokens.weight'''].shape[0]

							_lowercase							:		Tuple				=   PLBartConfig.from_pretrained(snake_case_ ,       vocab_size=snake_case_  )

							_lowercase							:		Optional[int]				=   state_dict['''decoder.embed_tokens.weight''']
							if not classification:
														_lowercase							:		str				=   PLBartForConditionalGeneration(snake_case_  )
														model.model.load_state_dict(snake_case_  )
														if finetuned:
																					_lowercase							:		Dict				=   make_linear_from_emb(model.model.shared  )

							else:
														_lowercase							:		Optional[int]				=   {}
														for key, value in state_dict.copy().items():
																					if key.startswith('''classification_heads.sentence_classification_head'''  ):
																												_lowercase							:		List[Any]				=   value
																												state_dict.pop(snake_case_  )
														_lowercase							:		Any				=   PLBartForSequenceClassification(snake_case_  )
														model.model.load_state_dict(snake_case_  )
														model.classification_head.load_state_dict(snake_case_  )

							return model


if __name__ == "__main__":
						lowerCamelCase__	  =		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('fairseq_path', type=str, help='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='uclanlp/plbart-base',
						    type=str,
						    help='Which huggingface architecture to use: plbart-base',
						)
						parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
						parser.add_argument(
						    '--classification', action='store_true', help='whether the model is a classification checkpoint'
						)
						lowerCamelCase__	  =		parser.parse_args()
						lowerCamelCase__	  =		convert_fairseq_plbart_checkpoint_from_disk(
						    args.fairseq_path,
						    hf_config_path=args.hf_config,
						    finetuned=args.finetuned,
						    classification=args.classification,
						)
						model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''






class 	_lowerCAmelCase     :






		'''simple docstring'''
		def __init__(						self							:       int      ,		UpperCamelCase_							:       int		)      ->			None:







									'''simple docstring'''





									_lowercase							:		Optional[Any]				=   data
									_lowercase							:		int
									_lowercase							:		Node





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase							:		str				=   0
							_lowercase							:		List[Any]				=   x





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node ,       snake_case_    :     Node  )		->None:






							'''simple docstring'''
							_lowercase			, _lowercase							:		Union[str, Any]				=   find_set(snake_case_  ), find_set(snake_case_  )
							if x == y:
														return

							elif x.rank > y.rank:
														_lowercase							:		Optional[Any]				=   x
							else:
														_lowercase							:		Optional[int]				=   y
														if x.rank == y.rank:
																					y.rank += 1





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->Node:






							'''simple docstring'''
							if x != x.parent:
														_lowercase							:		str				=   find_set(x.parent  )
							return x.parent





def 				_SCREAMING_SNAKE_CASE(					snake_case_    :     Node  )		->set:






							'''simple docstring'''
							_lowercase							:		str				=   ({0, 1, 2}, {3, 4, 5})
							for s in sets:
														if node.data in s:
																					return s
							_lowercase							:		Optional[Any]				=   F"{node.data} is not in {sets}"
							raise ValueError(snake_case_  )





def 				_SCREAMING_SNAKE_CASE(					)		->None:






							'''simple docstring'''
							_lowercase							:		Any				=   [Node(snake_case_  ) for i in range(6  )]
							for v in vertex:
														make_set(snake_case_  )

							union_set(vertex[0] ,       vertex[1]  )
							union_set(vertex[1] ,       vertex[2]  )
							union_set(vertex[3] ,       vertex[4]  )
							union_set(vertex[3] ,       vertex[5]  )

							for nodea in vertex:
														for nodea in vertex:
																					if find_python_set(snake_case_  ).isdisjoint(find_python_set(snake_case_  )  ):
																												assert find_set(snake_case_  ) != find_set(snake_case_  )
																					else:
																												assert find_set(snake_case_  ) == find_set(snake_case_  )


if __name__ == "__main__":
						test_disjoint_set()



