


'''simple docstring'''






import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()






def 	_A				(lowerCAmelCase__  :Optional[Any]				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Tuple				,						lowerCAmelCase__  :List[str]		)   ->  List[Any]:




     '''simple docstring'''



     _a			      =							TapasConfig.from_json_file(__A		)
     # set absolute/relative position embeddings parameter
     _a			      =							reset_position_index_per_cell

     # set remaining parameters of TapasConfig as well as the model based on the task
     if task == "SQA":
          _a			      =							TapasForQuestionAnswering(config=__A		)
     elif task == "WTQ":
          # run_task_main.py hparams
          _a			      =							4
          _a			      =							True
          # hparam_utils.py hparams
          _a			      =							0.6_6_4_6_9_4
          _a			      =							0.2_0_7_9_5_1
          _a			      =							0.1_2_1_1_9_4
          _a			      =							True
          _a			      =							True
          _a			      =							False
          _a			      =							0.0_3_5_2_5_1_3

          _a			      =							TapasForQuestionAnswering(config=__A		)
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          _a			      =							4
          _a			      =							False
          # hparam_utils.py hparams
          _a			      =							3_6.4_5_1_9
          _a			      =							0.9_0_3_4_2_1
          _a			      =							2_2_2.0_8_8
          _a			      =							True
          _a			      =							True
          _a			      =							True
          _a			      =							0.7_6_3_1_4_1

          _a			      =							TapasForQuestionAnswering(config=__A		)
     elif task == "TABFACT":
          _a			      =							TapasForSequenceClassification(config=__A		)
     elif task == "MLM":
          _a			      =							TapasForMaskedLM(config=__A		)
     elif task == "INTERMEDIATE_PRETRAINING":
          _a			      =							TapasModel(config=__A		)
     else:
          raise ValueError(f'Task {task} not supported.'		)

     print(f'Building PyTorch model from configuration: {config}'		)
     # Load weights from tf checkpoint
     load_tf_weights_in_tapas(__A				,						__A				,						__A		)

     # Save pytorch-model (weights and configuration)
     print(f'Save PyTorch model to {pytorch_dump_path}'		)
     model.save_pretrained(__A		)

     # Save tokenizer files
     print(f'Save tokenizer files to {pytorch_dump_path}'		)
     _a			      =							TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt'				,						model_max_length=5_12		)
     tokenizer.save_pretrained(__A		)

     print('Used relative position embeddings:'				,						model.config.reset_position_index_per_cell		)


if __name__ == "__main__":
 a_					:    Dict	       =    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
 )
 parser.add_argument(
     "--reset_position_index_per_cell",
     default=False,
     action="store_true",
     help="Whether to use relative position embeddings or not. Defaults to True.",
 )
 parser.add_argument(
     "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
 )
 parser.add_argument(
     "--tapas_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained TAPAS model. \n"
         "This specifies the model architecture."
     ),
 )
 parser.add_argument(
     "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
 )
 a_					:    List[str]	       =    parser.parse_args()
 convert_tf_checkpoint_to_pytorch(
     args.task,
     args.reset_position_index_per_cell,
     args.tf_checkpoint_path,
     args.tapas_config_file,
     args.pytorch_dump_path,
 )





'''simple docstring'''






def 	_A				(lowerCAmelCase__  :List[str]				,						lowerCAmelCase__  :Optional[Any]		)   ->  Optional[int]:




     '''simple docstring'''



     print('\nThe shortest path matrix using Floyd Warshall algorithm\n'		)
     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               if dist[i][j] != float('inf'		):
                    print(int(dist[i][j]		)				,						end='\t'		)
               else:
                    print('INF'				,						end='\t'		)
          print()






def 	_A				(lowerCAmelCase__  :Dict				,						lowerCAmelCase__  :List[str]		)   ->  List[str]:




     '''simple docstring'''



     _a			      =							[[float('inf'		) for _ in range(lowerCAmelCase__		)] for _ in range(lowerCAmelCase__		)]

     for i in range(lowerCAmelCase__		):
          for j in range(lowerCAmelCase__		):
               _a			      =							graph[i][j]

               # check vertex k against all other vertices (i, j)
     for k in range(lowerCAmelCase__		):
          # looping through rows of graph array
          for i in range(lowerCAmelCase__		):
               # looping through columns of graph array
               for j in range(lowerCAmelCase__		):
                    if (
                        dist[i][k] != float('inf'		)
                        and dist[k][j] != float('inf'		)
                        and dist[i][k] + dist[k][j] < dist[i][j]
                    ):
                         _a			      =							dist[i][k] + dist[k][j]

     _print_dist(lowerCAmelCase__				,						lowerCAmelCase__		)
     return dist, v


if __name__ == "__main__":
 a_					:    Optional[Any]          =    int(input("Enter number of vertices: "))
 a_					:    List[Any]          =    int(input("Enter number of edges: "))

 a_					:    Dict          =    [[float("inf") for i in range(v)] for j in range(v)]

 for i in range(v):
  a_					:    Union[str, Any]          =    0.0

  # src and dst are indices that must be within the array size graph[e][v]
  # failure to follow this will result in an error
 for i in range(e):
  print("\nEdge ", i + 1)
  a_					:    Union[str, Any]          =    int(input("Enter source:"))
  a_					:    int          =    int(input("Enter destination:"))
  a_					:    Tuple          =    float(input("Enter weight:"))
  a_					:    Dict          =    weight

 floyd_warshall(graph, v)

 # Example Input
 # Enter number of vertices: 3
 # Enter number of edges: 2

 # # generated graph from vertex and edge inputs
 # [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
 # [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]

 # specify source, destination and weight for edge #1
 # Edge  1
 # Enter source:1
 # Enter destination:2
 # Enter weight:2

 # specify source, destination and weight for edge #2
 # Edge  2
 # Enter source:2
 # Enter destination:1
 # Enter weight:1

 # # Expected Output from the vertice, edge and src, dst, weight inputs!!
 # 0		INF	INF
 # INF	0	2
 # INF	1	0

