






from __future__ import annotations

UpperCamelCase 				=	{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class       __UpperCAmelCase		:







			def __init__(	self:  Optional[Any]      ,	UpperCAmelCase_:  dict[str, list[str]]      ,	UpperCAmelCase_:  str     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  graph
									# mapping node to its parent in resulting breadth first tree
									_SCREAMING_SNAKE_CASE	      =  {}
									_SCREAMING_SNAKE_CASE	      =  source_vertex







			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  {self.source_vertex}
									_SCREAMING_SNAKE_CASE	      =  None
									_SCREAMING_SNAKE_CASE	      =  [self.source_vertex]  # first in first out queue

									while queue:
															_SCREAMING_SNAKE_CASE	      =  queue.pop(0     )
															for adjacent_vertex in self.graph[vertex]:
																					if adjacent_vertex not in visited:
																											visited.add(lowerCAmelCase_     )
																											_SCREAMING_SNAKE_CASE	      =  vertex
																											queue.append(lowerCAmelCase_     )







			def 							UpperCamelCase      (	self:  int      ,	UpperCAmelCase_:  str     ):







									'''simple docstring'''




									if target_vertex == self.source_vertex:
															return self.source_vertex

									_SCREAMING_SNAKE_CASE	      =  self.parent.get(lowerCAmelCase_     )
									if target_vertex_parent is None:
															_SCREAMING_SNAKE_CASE	      =  (
															    F'No path from vertex: {self.source_vertex} to vertex: {target_vertex}'
															)
															raise ValueError(lowerCAmelCase_     )

									return self.shortest_path(lowerCAmelCase_     ) + F'->{target_vertex}'


if __name__ == "__main__":
		UpperCamelCase 				=	Graph(graph, '''G''')
		g.breath_first_search()
		print(g.shortest_path('''D'''))
		print(g.shortest_path('''G'''))
		print(g.shortest_path('''Foo'''))








import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save

def   a_    ( lowerCAmelCase_      :			List[str],	lowerCAmelCase_      :			Dict,	lowerCAmelCase_      :			Tuple=1024,	lowerCAmelCase_      :			Optional[Any]=1024,	lowerCAmelCase_      :			Tuple=False,	**lowerCAmelCase_      :			Union[str, Any]	):
   __lowerCAmelCase								=	AutoTokenizer.from_pretrained(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='train',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	tok.pad_token_id

   def get_lens(lowerCAmelCase_      :			Optional[Any]	):
      __lowerCAmelCase								=	tqdm(
          DataLoader(lowerCAmelCase_,	batch_size=512,	num_workers=8,	shuffle=lowerCAmelCase_,	collate_fn=ds.collate_fn	),	desc=str(ds.len_file	),	)
      __lowerCAmelCase								=	[]
      for batch in dl:
         __lowerCAmelCase								=	batch['input_ids'].ne(lowerCAmelCase_	).sum(1	).tolist()
         __lowerCAmelCase								=	batch['labels'].ne(lowerCAmelCase_	).sum(1	).tolist()
         if consider_target:
            for src, tgt in zip(lowerCAmelCase_,	lowerCAmelCase_	):
               max_lens.append(max(lowerCAmelCase_,	lowerCAmelCase_	)	)
         else:
            max_lens.extend(lowerCAmelCase_	)
      return max_lens

   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   __lowerCAmelCase								=	SeqaSeqDataset(lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	lowerCAmelCase_,	type_path='val',	**lowerCAmelCase_	)
   __lowerCAmelCase								=	get_lens(lowerCAmelCase_	)
   pickle_save(lowerCAmelCase_,	train_ds.len_file	)
   pickle_save(lowerCAmelCase_,	val_ds.len_file	)


if __name__ == "__main__":
     fire.Fire(save_len_file)

