
'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				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(lowercase__   ,   lowercase__							)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				str							):



						"""simple docstring"""





						lowercase_		,				lowercase_			:  Optional[Any]									= emb.weight.shape
						lowercase_			:  Optional[int]									= nn.Linear(lowercase__   ,   lowercase__   ,   bias=lowercase__							)
						lowercase_			:  Any									= emb.weight.data
						return lin_layer
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				List[str]   ,   __SCREAMING_SNAKE_CASE      :				int="uclanlp/plbart-base"   ,   __SCREAMING_SNAKE_CASE      :				Tuple=False   ,   __SCREAMING_SNAKE_CASE      :				Optional[Any]=False							):



						"""simple docstring"""





						lowercase_			:  int									= torch.load(lowercase__   ,   map_location='''cpu'''							)['''model''']
						remove_ignore_keys_(lowercase__							)
						lowercase_			:  List[Any]									= state_dict['''encoder.embed_tokens.weight'''].shape[0]

						lowercase_			:  Any									= PLBartConfig.from_pretrained(lowercase__   ,   vocab_size=lowercase__							)

						lowercase_			:  Dict									= state_dict['''decoder.embed_tokens.weight''']
						if not classification:
												lowercase_			:  Dict									= PLBartForConditionalGeneration(lowercase__							)
												model.model.load_state_dict(lowercase__							)
												if finetuned:
																		lowercase_			:  Tuple									= make_linear_from_emb(model.model.shared							)

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

						return model


if __name__ == "__main__":
					_lowercase				:						Optional[int]												= 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"
					)
					_lowercase				:						Optional[int]												= parser.parse_args()
					_lowercase				:						List[str]												= 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'''







from __future__ import annotations

from queue import Queue






class 				lowerCAmelCase__     :






					def __init__(     self	):


											"""simple docstring"""





											lowercase_			:  dict[int, list[int]]									= {}






					def 			_snake_case					(     self	):


											"""simple docstring"""





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






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											if from_vertex in self.vertices:
																	self.vertices[from_vertex].append(__SCREAMING_SNAKE_CASE	)
											else:
																	lowercase_			:  Optional[Any]									= [to_vertex]






					def 			_snake_case					(     self       ,							__SCREAMING_SNAKE_CASE	):


											"""simple docstring"""





											lowercase_			:  List[Any]									= set()

											# create a first in first out queue to store all the vertices for BFS
											lowercase_			:  Queue									= Queue()

											# mark the source node as visited and enqueue it
											visited.add(__SCREAMING_SNAKE_CASE	)
											queue.put(__SCREAMING_SNAKE_CASE	)

											while not queue.empty():
																	lowercase_			:  List[Any]									= queue.get()

																	# loop through all adjacent vertex and enqueue it if not yet visited
																	for adjacent_vertex in self.vertices[vertex]:
																							if adjacent_vertex not in visited:
																													queue.put(__SCREAMING_SNAKE_CASE	)
																													visited.add(__SCREAMING_SNAKE_CASE	)
											return visited


if __name__ == "__main__":
							from doctest import testmod

							testmod(verbose=True)

							_lowercase				:						Optional[int]												= 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()
							# 0  :  1 -> 2
							# 1  :  2
							# 2  :  0 -> 3
							# 3  :  3

							assert sorted(g.bfs(2)) == [0, 1, 2, 3]



