


from __future__ import annotations

from queue import Queue







class 				lowerCamelCase__       :





     def __init__(self	)					->       None:
      _lowercase											={}





     def 					__A (self	)					->       None:
      for i in self.vertices:
       print(UpperCAmelCase		,					''' : '''		,					''' -> '''.join([str(UpperCAmelCase	) for j in self.vertices[i]]	)	)





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase	)					->       None:
      if from_vertex in self.vertices:
       self.vertices[from_vertex].append(UpperCAmelCase	)
      else:
       _lowercase											=[to_vertex]





     def 					__A (self		,					UpperCAmelCase	)					->       set[int]:
      _lowercase											=set()

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

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

      while not queue.empty():
       _lowercase											=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(UpperCAmelCase	)
         visited.add(UpperCAmelCase	)
      return visited


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

  testmod(verbose=True)

  UpperCAmelCase__            =							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]





import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      Tuple:







 """simple docstring"""


 _lowercase											=BertConfig.from_pretrained(
     '''bert-large-cased'''					,						vocab_size=__snake_case					,						max_position_embeddings=512					,						is_decoder=__snake_case					,						add_cross_attention=__snake_case					,						)
 _lowercase											=bert_config.to_dict()
 del bert_config_dict["type_vocab_size"]
 _lowercase											=BertGenerationConfig(**__snake_case						)
 if is_encoder:
  _lowercase											=BertGenerationEncoder(__snake_case						)
 else:
  _lowercase											=BertGenerationDecoder(__snake_case						)
 print(F"Building PyTorch model from configuration: {config}"						)

 # Load weights from tf checkpoint
 load_tf_weights_in_bert_generation(
     __snake_case					,						__snake_case					,						model_class='''bert'''					,						is_encoder_named_decoder=__snake_case					,						is_encoder=__snake_case					,						)

 # Save pytorch-model
 print(F"Save PyTorch model and config to {pytorch_dump_path}"						)
 model.save_pretrained(__snake_case						)


if __name__ == "__main__":
  UpperCAmelCase__            =							argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
  )
  parser.add_argument(
      '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  )
  parser.add_argument(
      '''--is_encoder_named_decoder''',
      action='''store_true''',
      help='''If decoder has to be renamed to encoder in PyTorch model.''',
  )
  parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
  parser.add_argument('''--vocab_size''', default=5_0358, type=int, help='''Vocab size of model''')
  UpperCAmelCase__            =							parser.parse_args()
  convert_tf_checkpoint_to_pytorch(
      args.tf_hub_path,
      args.pytorch_dump_path,
      args.is_encoder_named_decoder,
      args.vocab_size,
      is_encoder=args.is_encoder,
  )

