



'''simple docstring'''







import argparse

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


logging.set_verbosity_info()





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    # Initialise PyTorch model
    UpperCamelCase__      =			BertConfig.from_pretrained(
        "bert-large-cased"					,  vocab_size=lowerCAmelCase_					,  max_position_embeddings=5_12					,  is_decoder=lowerCAmelCase_					,  add_cross_attention=lowerCAmelCase_					,  )
    UpperCamelCase__      =			bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    UpperCamelCase__      =			BertGenerationConfig(**lowerCAmelCase_	)
    if is_encoder:
        UpperCamelCase__      =			BertGenerationEncoder(lowerCAmelCase_	)
    else:
        UpperCamelCase__      =			BertGenerationDecoder(lowerCAmelCase_	)
    print(F'Building PyTorch model from configuration: {config}'	)

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        lowerCAmelCase_					,  lowerCAmelCase_					,  model_class="bert"					,  is_encoder_named_decoder=lowerCAmelCase_					,  is_encoder=lowerCAmelCase_					,  )

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


if __name__ == "__main__":
      lowercase__     :  Tuple    					=			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_03_58, type=int, help='Vocab size of model')
      lowercase__     :  Dict    					=			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,
      )









'''simple docstring'''







import heapq





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			[(0, start)]  # cost from start node,end node
    UpperCamelCase__      =			set()
    while heap:
        ((UpperCamelCase__)    ,  (UpperCamelCase__))      =			heapq.heappop(lowerCAmelCase_	)
        if u in visited:
            continue
        visited.add(lowerCAmelCase_	)
        if u == end:
            return cost
        for v, c in graph[u]:
            if v in visited:
                continue
            UpperCamelCase__      =			cost + c
            heapq.heappush(lowerCAmelCase_					,  (next_item, v)	)
    return -1


lowercase__     :  Any    					=			{
    'A': [['B', 2], ['C', 5]],
    'B': [['A', 2], ['D', 3], ['E', 1], ['F', 1]],
    'C': [['A', 5], ['F', 3]],
    'D': [['B', 3]],
    'E': [['B', 4], ['F', 3]],
    'F': [['C', 3], ['E', 3]],
}
lowercase__     :  Optional[int]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['F', 3]],
    'F': [],
}
lowercase__     :  Union[str, Any]    					=			{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}

lowercase__     :  Union[str, Any]    					=			dijkstra(G, 'E', 'C')
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

lowercase__     :  Optional[int]    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 3 --> F == 3

lowercase__     :  Any    					=			dijkstra(Ga, 'E', 'F')
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
      import doctest

      doctest.testmod()




