


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__:     str							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     List[str]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple						) ->   List[str]:





	"""simple docstring"""







	A								=				TapasConfig.from_json_file(_lowerCamelCase						)
	# 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=_lowerCamelCase						)
	elif task == "WTQ":
		# run_task_main.py hparams
		A								=				4
		A								=				True
		# hparam_utils.py hparams
		A								=				0.66_46_94
		A								=				0.20_79_51
		A								=				0.12_11_94
		A								=				True
		A								=				True
		A								=				False
		A								=				0.0_35_25_13

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

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

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

	# 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(_lowerCamelCase						)

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


if __name__ == "__main__":
				_lowercase       :      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       :      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,
				)







def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   str:





	"""simple docstring"""







	return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



