




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_		(       UpperCamelCase__				:      int ,     UpperCamelCase__				:      Optional[int] ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Tuple ,     UpperCamelCase__				:      Union[str, Any]      )     ->       Dict:





				"""simple docstring"""


				__lowerCamelCase		       =    TapasConfig.from_json_file(UpperCamelCase__      )
				# set absolute/relative position embeddings parameter
				__lowerCamelCase		       =    reset_position_index_per_cell

				# set remaining parameters of TapasConfig as well as the model based on the task
				if task == "SQA":
								__lowerCamelCase		       =    TapasForQuestionAnswering(config=UpperCamelCase__      )
				elif task == "WTQ":
								# run_task_main.py hparams
								__lowerCamelCase		       =    4
								__lowerCamelCase		       =    True
								# hparam_utils.py hparams
								__lowerCamelCase		       =    0.66_46_94
								__lowerCamelCase		       =    0.20_79_51
								__lowerCamelCase		       =    0.12_11_94
								__lowerCamelCase		       =    True
								__lowerCamelCase		       =    True
								__lowerCamelCase		       =    False
								__lowerCamelCase		       =    0.0_35_25_13

								__lowerCamelCase		       =    TapasForQuestionAnswering(config=UpperCamelCase__      )
				elif task == "WIKISQL_SUPERVISED":
								# run_task_main.py hparams
								__lowerCamelCase		       =    4
								__lowerCamelCase		       =    False
								# hparam_utils.py hparams
								__lowerCamelCase		       =    36.45_19
								__lowerCamelCase		       =    0.90_34_21
								__lowerCamelCase		       =    2_22.0_88
								__lowerCamelCase		       =    True
								__lowerCamelCase		       =    True
								__lowerCamelCase		       =    True
								__lowerCamelCase		       =    0.76_31_41

								__lowerCamelCase		       =    TapasForQuestionAnswering(config=UpperCamelCase__      )
				elif task == "TABFACT":
								__lowerCamelCase		       =    TapasForSequenceClassification(config=UpperCamelCase__      )
				elif task == "MLM":
								__lowerCamelCase		       =    TapasForMaskedLM(config=UpperCamelCase__      )
				elif task == "INTERMEDIATE_PRETRAINING":
								__lowerCamelCase		       =    TapasModel(config=UpperCamelCase__      )
				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(UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__      )

				# Save pytorch-model (weights and configuration)
				print(F"""Save PyTorch model to {pytorch_dump_path}"""      )
				model.save_pretrained(UpperCamelCase__      )

				# Save tokenizer files
				print(F"""Save tokenizer files to {pytorch_dump_path}"""      )
				__lowerCamelCase		       =    TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt' ,     model_max_length=512      )
				tokenizer.save_pretrained(UpperCamelCase__      )

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


if __name__ == "__main__":
					__A        =    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        =    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__ = 10						):




    '''simple docstring'''



    if not isinstance(snake_case__		,				snake_case__						) or n < 0:
        raise ValueError('''Invalid input'''						)
    A       :   List[str]					=						10**n
    A       :   Tuple					=						2_8433 * (pow(2		,				783_0457		,				snake_case__						)) + 1
    return str(number % modulus						)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      print(f'''{solution(10) = }''')




