





'''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 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]					,				__snake_case    :					Optional[int]					,				__snake_case    :					Any					,				__snake_case    :					int					,				__snake_case    :					List[Any]     )	->	str:
     # Initialise PyTorch model.
     # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
     # TapasConfig to False.

     # initialize configuration from json file
     __A		:					List[Any]						=			TapasConfig.from_json_file(__snake_case     )
     # set absolute/relative position embeddings parameter
     __A		:					List[str]						=			reset_position_index_per_cell

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

          __A		:					Union[str, Any]						=			TapasForQuestionAnswering(config=__snake_case     )
     elif task == "WIKISQL_SUPERVISED":
          # run_task_main.py hparams
          __A		:					Optional[Any]						=			4
          __A		:					List[str]						=			False
          # hparam_utils.py hparams
          __A		:					Union[str, Any]						=			36.4_519
          __A		:					Optional[int]						=			0.903_421
          __A		:					Optional[Any]						=			222.088
          __A		:					Union[str, Any]						=			True
          __A		:					List[Any]						=			True
          __A		:					str						=			True
          __A		:					List[Any]						=			0.763_141

          __A		:					Any						=			TapasForQuestionAnswering(config=__snake_case     )
     elif task == "TABFACT":
          __A		:					int						=			TapasForSequenceClassification(config=__snake_case     )
     elif task == "MLM":
          __A		:					int						=			TapasForMaskedLM(config=__snake_case     )
     elif task == "INTERMEDIATE_PRETRAINING":
          __A		:					Tuple						=			TapasModel(config=__snake_case     )
     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(__snake_case					,				__snake_case					,				__snake_case     )

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

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

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


if __name__ == "__main__":
  lowercase__       : Union[str, Any]         =       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.'''
  )
  lowercase__       : Union[str, Any]         =       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 			_lowerCAmelCase     (    __snake_case    :					Optional[Any]     )	->	Optional[Any]:  # noqa: E741
     __A		:					Tuple						=			len(__snake_case     )
     __A		:					Optional[int]						=			0
     __A		:					str						=			[0] * n
     __A		:					int						=			[False] * n
     __A		:					Tuple						=			[False] * n

     def dfs(__snake_case    :					List[str]					,				__snake_case    :					List[Any]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					int     ):
          if parent == root:
               out_edge_count += 1
          __A		:					str						=			True
          __A		:					Tuple						=			at

          for to in l[at]:
               if to == parent:
                    pass
               elif not visited[to]:
                    __A		:					Optional[int]						=			dfs(__snake_case					,				__snake_case					,				__snake_case					,				__snake_case     )
                    __A		:					int						=			min(low[at]					,				low[to]     )

                    # AP found via bridge
                    if at < low[to]:
                         __A		:					Tuple						=			True
                    # AP found via cycle
                    if at == low[to]:
                         __A		:					Optional[Any]						=			True
               else:
                    __A		:					Any						=			min(low[at]					,				__snake_case     )
          return out_edge_count

     for i in range(__snake_case     ):
          if not visited[i]:
               __A		:					Tuple						=			0
               __A		:					List[Any]						=			dfs(__snake_case					,				__snake_case					,				-1					,				__snake_case     )
               __A		:					Union[str, Any]						=			out_edge_count > 1

     for x in range(len(__snake_case     )     ):
          if is_art[x] is True:
               print(__snake_case     )


# Adjacency list of graph
lowercase__       : Tuple         =       {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}







compute_ap(data)