


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 					UpperCamelCase   (      __magic_name__	:				Any       ,       __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						TapasConfig.from_json_file(__magic_name__	)
		# set absolute/relative position embeddings parameter
		lowercase__	     =						reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				lowercase__	     =						TapasForQuestionAnswering(config=__magic_name__	)
		elif task == "WTQ":
				# run_task_main.py hparams
				lowercase__	     =						4
				lowercase__	     =						True
				# hparam_utils.py hparams
				lowercase__	     =						0.6_6_4_6_9_4
				lowercase__	     =						0.2_0_7_9_5_1
				lowercase__	     =						0.1_2_1_1_9_4
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						False
				lowercase__	     =						0.0_3_5_2_5_1_3

				lowercase__	     =						TapasForQuestionAnswering(config=__magic_name__	)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowercase__	     =						4
				lowercase__	     =						False
				# hparam_utils.py hparams
				lowercase__	     =						3_6.4_5_1_9
				lowercase__	     =						0.9_0_3_4_2_1
				lowercase__	     =						2_2_2.0_8_8
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						0.7_6_3_1_4_1

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

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

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

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


if __name__ == "__main__":
	A			:						Optional[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.'
	)
	A			:						Optional[int]        =  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 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__ = " "		):
      UpperCamelCase__     : Optional[int]											=					[]

      UpperCamelCase__     : int											=					0
      for index, char in enumerate(UpperCamelCase__		):
            if char == separator:
                  split_words.append(string[last_index:index]		)
                  UpperCamelCase__     : int											=					index + 1
            elif index + 1 == len(UpperCamelCase__		):
                  split_words.append(string[last_index : index + 1]		)
      return split_words


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

    testmod()


