




"""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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      List[str]:


			"""simple docstring"""
			# 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
			__UpperCAmelCase						:			Optional[Any]												=       TapasConfig.from_json_file(UpperCamelCase				)
			# set absolute/relative position embeddings parameter
			__UpperCAmelCase						:			Optional[Any]												=       reset_position_index_per_cell

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

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

						__UpperCAmelCase						:			Optional[Any]												=       TapasForQuestionAnswering(config=UpperCamelCase				)
			elif task == "TABFACT":
						__UpperCAmelCase						:			Union[str, Any]												=       TapasForSequenceClassification(config=UpperCamelCase				)
			elif task == "MLM":
						__UpperCAmelCase						:			Tuple												=       TapasForMaskedLM(config=UpperCamelCase				)
			elif task == "INTERMEDIATE_PRETRAINING":
						__UpperCAmelCase						:			List[str]												=       TapasModel(config=UpperCamelCase				)
			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(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

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

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

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


if __name__ == "__main__":
					A				=							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				=							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"""
from __future__ import annotations

def  lowercase     (_lowerCAmelCase						):
						__lowerCAmelCase     =  2
						__lowerCAmelCase     =  []
						while i * i <= n:
												if n % i:
																		i += 1
												else:
																		n //= i
																		factors.append(_lowerCAmelCase						)
						if n > 1:
												factors.append(_lowerCAmelCase						)
						return factors


if __name__ == "__main__":
			import doctest

			doctest.testmod()
