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 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						):
					# 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
					_UpperCamelCase			     =     TapasConfig.from_json_file(UpperCAmelCase_						)
					# set absolute/relative position embeddings parameter
					_UpperCamelCase			     =     reset_position_index_per_cell

					# set remaining parameters of TapasConfig as well as the model based on the task
					if task == "SQA":
										_UpperCamelCase			     =     TapasForQuestionAnswering(config=UpperCAmelCase_						)
					elif task == "WTQ":
										# run_task_main.py hparams
										_UpperCamelCase			     =     4
										_UpperCamelCase			     =     True
										# hparam_utils.py hparams
										_UpperCamelCase			     =     0.664694
										_UpperCamelCase			     =     0.207951
										_UpperCamelCase			     =     0.121194
										_UpperCamelCase			     =     True
										_UpperCamelCase			     =     True
										_UpperCamelCase			     =     False
										_UpperCamelCase			     =     0.0352513

										_UpperCamelCase			     =     TapasForQuestionAnswering(config=UpperCAmelCase_						)
					elif task == "WIKISQL_SUPERVISED":
										# run_task_main.py hparams
										_UpperCamelCase			     =     4
										_UpperCamelCase			     =     False
										# hparam_utils.py hparams
										_UpperCamelCase			     =     36.4519
										_UpperCamelCase			     =     0.903421
										_UpperCamelCase			     =     222.088
										_UpperCamelCase			     =     True
										_UpperCamelCase			     =     True
										_UpperCamelCase			     =     True
										_UpperCamelCase			     =     0.763141

										_UpperCamelCase			     =     TapasForQuestionAnswering(config=UpperCAmelCase_						)
					elif task == "TABFACT":
										_UpperCamelCase			     =     TapasForSequenceClassification(config=UpperCAmelCase_						)
					elif task == "MLM":
										_UpperCamelCase			     =     TapasForMaskedLM(config=UpperCAmelCase_						)
					elif task == "INTERMEDIATE_PRETRAINING":
										_UpperCamelCase			     =     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}"""						)
					_UpperCamelCase			     =     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__":
					_lowerCAmelCase       =		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."
					)
					_lowerCAmelCase       =		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'''




from math import loga




def 						__lowerCamelCase			(	UpperCAmelCase_							)   ->int:
	if a < 0:
		raise ValueError('Input value must be a positive integer'							)
	elif isinstance(UpperCAmelCase_					,				UpperCAmelCase_							):
		raise TypeError('Input value must be a \'int\' type'							)
	return 0 if (a == 0) else int(loga(a & -a							)							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



