
'''simple docstring'''







from __future__ import annotations

from queue import Queue







def   lowerCAmelCase(  a__    :  str      , a__    :  List[Any]      ):







       '''simple docstring'''






       a ^= b
       b ^= a
       a ^= b
       return a, b







def   lowerCAmelCase(  a__    :  Tuple      , a__    :  str      ):







       '''simple docstring'''






       lowerCamelCase__				   =      1
       while (1 << j) < max_node:
              for i in range(1      , max_node + 1      ):
                     lowerCamelCase__				   =      parent[j - 1][parent[j - 1][i]]
              j += 1
       return parent







def   lowerCAmelCase(  a__    :  str      , a__    :  Tuple      , a__    :  str      , a__    :  List[Any]      ):







       '''simple docstring'''






       if level[u] < level[v]:
              lowerCamelCase__				   =      swap(__lowercase      , __lowercase      )
       # making depth of u same as depth of v
       for i in range(18      , -1      , -1      ):
              if level[u] - (1 << i) >= level[v]:
                     lowerCamelCase__				   =      parent[i][u]
    # at the same depth if u==v that mean lca is found
       if u == v:
              return u
       # moving both nodes upwards till lca in found
       for i in range(18      , -1      , -1      ):
              if parent[i][u] not in [0, parent[i][v]]:
                     lowerCamelCase__				   =      parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
       return parent[0][u]







def   lowerCAmelCase(  a__    :  int      , a__    :  Tuple      , a__    :  str      , a__    :  List[Any]      , a__    :  str = 1      , ):







       '''simple docstring'''






       lowerCamelCase__				   =      0
       lowerCamelCase__				   =      Queue(maxsize=__lowercase      )
       q.put(__lowercase      )
       while q.qsize() != 0:
              lowerCamelCase__				   =      q.get()
              for v in graph[u]:
                     if level[v] == -1:
                            lowerCamelCase__				   =      level[u] + 1
                            q.put(__lowercase      )
                            lowerCamelCase__				   =      u
       return level, parent







def   lowerCAmelCase(  ):







       '''simple docstring'''






       lowerCamelCase__				   =      13
       # initializing with 0
       lowerCamelCase__				   =      [[0 for _ in range(max_node + 10      )] for _ in range(20      )]
       # initializing with -1 which means every node is unvisited
       lowerCamelCase__				   =      [-1 for _ in range(max_node + 10      )]
       lowerCamelCase__				   =      {
           1: [2, 3, 4],
           2: [5],
           3: [6, 7],
           4: [8],
           5: [9, 10],
           6: [11],
           7: [],
           8: [12, 13],
           9: [],
           10: [],
           11: [],
           12: [],
           13: [],
       }
       lowerCamelCase__				   =      breadth_first_search(__lowercase      , __lowercase      , __lowercase      , __lowercase      , 1      )
       lowerCamelCase__				   =      create_sparse(__lowercase      , __lowercase      )
       print("LCA of node 1 and 3 is: "      , lowest_common_ancestor(1      , 3      , __lowercase      , __lowercase      )      )
       print("LCA of node 5 and 6 is: "      , lowest_common_ancestor(5      , 6      , __lowercase      , __lowercase      )      )
       print("LCA of node 7 and 11 is: "      , lowest_common_ancestor(7      , 11      , __lowercase      , __lowercase      )      )
       print("LCA of node 6 and 7 is: "      , lowest_common_ancestor(6      , 7      , __lowercase      , __lowercase      )      )
       print("LCA of node 4 and 12 is: "      , lowest_common_ancestor(4      , 12      , __lowercase      , __lowercase      )      )
       print("LCA of node 8 and 8 is: "      , lowest_common_ancestor(8      , 8      , __lowercase      , __lowercase      )      )


if __name__ == "__main__":
    main()






import argparse

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


logging.set_verbosity_info()
def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Dict:







						"""simple docstring"""

						A__						:				str         =			BertConfig.from_pretrained(
						    "bert-large-cased"				,  vocab_size=__lowercase				,  max_position_embeddings=5_1_2				,  is_decoder=__lowercase				,  add_cross_attention=__lowercase				,  )
						A__						:				List[str]         =			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						A__						:				Union[str, Any]         =			BertGenerationConfig(**__lowercase							)
						if is_encoder:
												A__						:				Optional[int]         =			BertGenerationEncoder(__lowercase							)
						else:
												A__						:				List[Any]         =			BertGenerationDecoder(__lowercase							)
						print(F'Building PyTorch model from configuration: {config}'							)

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

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


if __name__ == "__main__":
		snake_case     :    List[Any]						=    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_0_3_5_8, type=int, help='Vocab size of model')
		snake_case     :    List[Any]						=    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,
		)