




"""simple docstring"""







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__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


		__lowercase				:		Any												=				TapasConfig.from_json_file(_lowerCamelCase				)
		# set absolute/relative position embeddings parameter
		__lowercase				:		Optional[int]												=				reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				__lowercase				:		List[Any]												=				TapasForQuestionAnswering(config=_lowerCamelCase				)
		elif task == "WTQ":
				# run_task_main.py hparams
				__lowercase				:		str												=				4
				__lowercase				:		Optional[Any]												=				True
				# hparam_utils.py hparams
				__lowercase				:		Union[str, Any]												=				0.6_6_4_6_9_4
				__lowercase				:		Tuple												=				0.2_0_7_9_5_1
				__lowercase				:		Any												=				0.1_2_1_1_9_4
				__lowercase				:		List[str]												=				True
				__lowercase				:		List[str]												=				True
				__lowercase				:		List[Any]												=				False
				__lowercase				:		List[str]												=				0.0_3_5_2_5_1_3

				__lowercase				:		Any												=				TapasForQuestionAnswering(config=_lowerCamelCase				)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				__lowercase				:		List[str]												=				4
				__lowercase				:		Dict												=				False
				# hparam_utils.py hparams
				__lowercase				:		int												=				3_6.4_5_1_9
				__lowercase				:		Tuple												=				0.9_0_3_4_2_1
				__lowercase				:		Optional[Any]												=				2_2_2.0_8_8
				__lowercase				:		Dict												=				True
				__lowercase				:		Union[str, Any]												=				True
				__lowercase				:		Union[str, Any]												=				True
				__lowercase				:		List[Any]												=				0.7_6_3_1_4_1

				__lowercase				:		str												=				TapasForQuestionAnswering(config=_lowerCamelCase				)
		elif task == "TABFACT":
				__lowercase				:		Optional[Any]												=				TapasForSequenceClassification(config=_lowerCamelCase				)
		elif task == "MLM":
				__lowercase				:		List[str]												=				TapasForMaskedLM(config=_lowerCamelCase				)
		elif task == "INTERMEDIATE_PRETRAINING":
				__lowercase				:		List[str]												=				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}'				)
		__lowercase				:		Optional[int]												=				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__":
						__A   :  Tuple					=      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[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,
						)






"""simple docstring"""







def 						snake_case__       (					_lowerCamelCase				)			->int:






		"""simple docstring"""


		if a < 0:
				raise ValueError("Input value must be a positive integer"				)
		elif isinstance(_lowerCamelCase,  _lowerCamelCase				):
				raise TypeError("Input value must be a 'int' type"				)
		return bin(_lowerCamelCase				).count("1"				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
