



'''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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				TapasConfig.from_json_file(SCREAMING_SNAKE_CASE_  )
			# 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=SCREAMING_SNAKE_CASE_  )
			elif task == "WTQ":
						# run_task_main.py hparams
						lowercase__					  =				4
						lowercase__					  =				True
						# hparam_utils.py hparams
						lowercase__					  =				0.66_4694
						lowercase__					  =				0.20_7951
						lowercase__					  =				0.12_1194
						lowercase__					  =				True
						lowercase__					  =				True
						lowercase__					  =				False
						lowercase__					  =				0.035_2513

						lowercase__					  =				TapasForQuestionAnswering(config=SCREAMING_SNAKE_CASE_  )
			elif task == "WIKISQL_SUPERVISED":
						# run_task_main.py hparams
						lowercase__					  =				4
						lowercase__					  =				False
						# hparam_utils.py hparams
						lowercase__					  =				36.4519
						lowercase__					  =				0.90_3421
						lowercase__					  =				222.088
						lowercase__					  =				True
						lowercase__					  =				True
						lowercase__					  =				True
						lowercase__					  =				0.76_3141

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

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

			# 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(SCREAMING_SNAKE_CASE_  )

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


if __name__ == "__main__":
							lowercase_								=     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."""
							)
							lowercase_								=     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 __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				len(SCREAMING_SNAKE_CASE_  )
			for i in range(SCREAMING_SNAKE_CASE_  ):
						for j in range(i + 1						,  SCREAMING_SNAKE_CASE_  ):
									if numbers[j] < numbers[i]:
												lowercase__  ,       lowercase__					  =				numbers[j], numbers[i]
			return numbers


if __name__ == "__main__":
							lowercase_								=     input("""Enter numbers separated by a comma:\n""").strip()
							lowercase_								=     [int(item) for item in user_input.split(""",""")]
							print(exchange_sort(unsorted))




