
"""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 lowercase_						(     _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
							SCREAMING_SNAKE_CASE__		: Any     =     TapasConfig.from_json_file(_snake_case		)
							# set absolute/relative position embeddings parameter
							SCREAMING_SNAKE_CASE__		: List[Any]     =     reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														SCREAMING_SNAKE_CASE__		: str     =     TapasForQuestionAnswering(config=_snake_case		)
							elif task == "WTQ":
														# run_task_main.py hparams
														SCREAMING_SNAKE_CASE__		: Dict     =     4
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     True
														# hparam_utils.py hparams
														SCREAMING_SNAKE_CASE__		: List[Any]     =     0.664694
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     0.207951
														SCREAMING_SNAKE_CASE__		: List[Any]     =     0.121194
														SCREAMING_SNAKE_CASE__		: int     =     True
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     True
														SCREAMING_SNAKE_CASE__		: Tuple     =     False
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     0.0352513

														SCREAMING_SNAKE_CASE__		: Tuple     =     TapasForQuestionAnswering(config=_snake_case		)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     4
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     False
														# hparam_utils.py hparams
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     36.4519
														SCREAMING_SNAKE_CASE__		: List[str]     =     0.903421
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     222.088
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     True
														SCREAMING_SNAKE_CASE__		: str     =     True
														SCREAMING_SNAKE_CASE__		: int     =     True
														SCREAMING_SNAKE_CASE__		: Any     =     0.763141

														SCREAMING_SNAKE_CASE__		: Dict     =     TapasForQuestionAnswering(config=_snake_case		)
							elif task == "TABFACT":
														SCREAMING_SNAKE_CASE__		: int     =     TapasForSequenceClassification(config=_snake_case		)
							elif task == "MLM":
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     TapasForMaskedLM(config=_snake_case		)
							elif task == "INTERMEDIATE_PRETRAINING":
														SCREAMING_SNAKE_CASE__		: Dict     =     TapasModel(config=_snake_case		)
							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(_snake_case     ,_snake_case     ,_snake_case		)

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

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

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


if __name__ == "__main__":
					UpperCAmelCase__   :					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.'
					)
					UpperCAmelCase__   :					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,
					)





"""simple docstring"""




import torch

def lowercase_						(     ):
							if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.cuda.device_count()
							else:
														SCREAMING_SNAKE_CASE__		: int     =     0
							print(f'''Successfully ran on {num_gpus} GPUs'''		)


if __name__ == "__main__":
					main()




