

import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				[
					    '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 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase,						__lowerCAmelCase       =				emb.weight.shape
					__lowerCAmelCase       =				nn.Linear(UpperCAmelCase__		,       UpperCAmelCase__		,       bias=UpperCAmelCase__   )
					__lowerCAmelCase       =				emb.weight.data
					return lin_layer


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__="uclanlp/plbart-base"		,       UpperCAmelCase__=False		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					__lowerCAmelCase       =				torch.load(UpperCAmelCase__		,       map_location='cpu'   )['model']
					remove_ignore_keys_(UpperCAmelCase__   )
					__lowerCAmelCase       =				state_dict['encoder.embed_tokens.weight'].shape[0]

					__lowerCAmelCase       =				PLBartConfig.from_pretrained(UpperCAmelCase__		,       vocab_size=UpperCAmelCase__   )

					__lowerCAmelCase       =				state_dict['decoder.embed_tokens.weight']
					if not classification:
										__lowerCAmelCase       =				PLBartForConditionalGeneration(UpperCAmelCase__   )
										model.model.load_state_dict(UpperCAmelCase__   )
										if finetuned:
															__lowerCAmelCase       =				make_linear_from_emb(model.model.shared   )

					else:
										__lowerCAmelCase       =				{}
										for key, value in state_dict.copy().items():
															if key.startswith('classification_heads.sentence_classification_head'   ):
																				__lowerCAmelCase       =				value
																				state_dict.pop(UpperCAmelCase__   )
										__lowerCAmelCase       =				PLBartForSequenceClassification(UpperCAmelCase__   )
										model.model.load_state_dict(UpperCAmelCase__   )
										model.classification_head.load_state_dict(UpperCAmelCase__   )

					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)











_lowercase:					Optional[Any]      =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]



class       _lowercase				:





	"""simple docstring"""




	def __init__(self						,			lowerCamelCase_						):
						"""simple docstring"""

						a   	=  name
						a   	=  []
						a   	=  []




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.inbound.append(lowerCamelCase_						)




	def 	UpperCamelCase_			(self						,			lowerCamelCase_						):
						"""simple docstring"""

						self.outbound.append(lowerCamelCase_						)




	def __repr__(self						):
						"""simple docstring"""

						return F'''<node={self.name} inbound={self.inbound} outbound={self.outbound}>'''






def        a( A			:       Optional[Any]					,	A			:       Optional[int]=3					,	A			:       str=0.85			)						->			Optional[Any]:
					"""simple docstring"""





					a   	=  {}
					for node in nodes:
										a   	=  1

					a   	=  {}
					for node in nodes:
										a   	=  len(node.outbound			)

					for i in range(A			):
										print(f'''======= Iteration {i + 1} ======='''			)
										for _, node in enumerate(A			):
															a   	=  (1 - d) + d * sum(
															    ranks[ib] / outbounds[ib] for ib in node.inbound			)
										print(A			)






def        a( )						->			List[Any]:
					"""simple docstring"""





					a   	=  list(input("Enter Names of the Nodes: "			).split()			)

					a   	=  [Node(A			) for name in names]

					for ri, row in enumerate(A			):
										for ci, col in enumerate(A			):
															if col == 1:
																				nodes[ci].add_inbound(names[ri]			)
																				nodes[ri].add_outbound(names[ci]			)

					print("======= Nodes ======="			)
					for node in nodes:
										print(A			)

					page_rank(A			)


if __name__ == "__main__":
				main()
