

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 						lowerCamelCase__      ( __A    :Tuple	,__A    :Optional[Any]	,__A    :str	,__A    :Optional[Any]	,__A    :Union[str, Any]		):


       """simple docstring"""




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

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              __snake_case       	=							TapasForQuestionAnswering(config=__A		)
       elif task == "WTQ":
              # run_task_main.py hparams
              __snake_case       	=							4
              __snake_case       	=							True
              # hparam_utils.py hparams
              __snake_case       	=							0.664_694
              __snake_case       	=							0.207_951
              __snake_case       	=							0.121_194
              __snake_case       	=							True
              __snake_case       	=							True
              __snake_case       	=							False
              __snake_case       	=							0.0_352_513

              __snake_case       	=							TapasForQuestionAnswering(config=__A		)
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              __snake_case       	=							4
              __snake_case       	=							False
              # hparam_utils.py hparams
              __snake_case       	=							36.4_519
              __snake_case       	=							0.903_421
              __snake_case       	=							222.088
              __snake_case       	=							True
              __snake_case       	=							True
              __snake_case       	=							True
              __snake_case       	=							0.763_141

              __snake_case       	=							TapasForQuestionAnswering(config=__A		)
       elif task == "TABFACT":
              __snake_case       	=							TapasForSequenceClassification(config=__A		)
       elif task == "MLM":
              __snake_case       	=							TapasForMaskedLM(config=__A		)
       elif task == "INTERMEDIATE_PRETRAINING":
              __snake_case       	=							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}'		)
       __snake_case       	=							TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + """vocab.txt"""	,model_max_length=5_1_2		)
       tokenizer.save_pretrained(__A		)

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


if __name__ == "__main__":
    UpperCamelCase__		 =     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.'''
    )
    UpperCamelCase__		 =     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,
    )




from __future__ import annotations







def 						lowerCamelCase__      ( __A    :list[float]	,__A    :Union[str, Any]		):


       """simple docstring"""




       print(F'Vertex\tShortest Distance from vertex {src}'		)
       for i, d in enumerate(__A		):
              print(F'{i}\t\t{d}'		)







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :list[float]	,__A    :int		):


       """simple docstring"""




       for j in range(__A		):
              __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])
              if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                     return True
       return False







def 						lowerCamelCase__      ( __A    :list[dict[str, int]]	,__A    :int	,__A    :int	,__A    :int		):


       """simple docstring"""




       __snake_case       	=							[float("""inf"""		)] * vertex_count
       __snake_case       	=							0.0

       for _ in range(vertex_count - 1		):
              for j in range(__A		):
                     __snake_case	,    __snake_case	,    __snake_case       	=							(graph[j][k] for k in ["""src""", """dst""", """weight"""])

                     if distance[u] != float("""inf"""		) and distance[u] + w < distance[v]:
                            __snake_case       	=							distance[u] + w

       __snake_case       	=							check_negative_cycle(__A	,__A	,__A		)
       if negative_cycle_exists:
              raise Exception("""Negative cycle found"""		)

       return distance


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    UpperCamelCase__		 =     int(input('''Enter number of vertices: ''').strip())
    UpperCamelCase__		 =     int(input('''Enter number of edges: ''').strip())

    UpperCamelCase__		 =     [{} for _ in range(E)]

    for i in range(E):
        print('''Edge ''', i + 1)
        UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__		 =     (
            int(x)
            for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
        )
        UpperCamelCase__		 =     {'''src''': src, '''dst''': dest, '''weight''': weight}

    UpperCamelCase__		 =     int(input('''\nEnter shortest path source:''').strip())
    UpperCamelCase__		 =     bellman_ford(graph, V, E, source)
    print_distance(shortest_distance, 0)

