


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       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	int:


						'''simple docstring'''






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

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

												__snake_case				=		TapasForQuestionAnswering(config=_lowerCAmelCase )
						elif task == "TABFACT":
												__snake_case				=		TapasForSequenceClassification(config=_lowerCAmelCase )
						elif task == "MLM":
												__snake_case				=		TapasForMaskedLM(config=_lowerCAmelCase )
						elif task == "INTERMEDIATE_PRETRAINING":
												__snake_case				=		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}''' )
						__snake_case				=		TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"      ,	model_max_length=512 )
						tokenizer.save_pretrained(_lowerCAmelCase )

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


if __name__ == "__main__":
	A							:					int     						=     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							:					List[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,
	)









import os
from typing import Dict, List, Tuple, TypeVar, Union


A							:					List[Any]     						=     TypeVar('T')

A							:					Dict     						=     Union[List[T], Tuple[T, ...]]
A							:					Any     						=     Union[T, List[T], Dict[str, T]]
A							:					Optional[int]     						=     Union[str, bytes, os.PathLike]





