


"""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       a_ (	lowercase__ :Any,			lowercase__ :Any,			lowercase__ :Optional[int],			lowercase__ :Union[str, Any],			lowercase__ :Optional[int]     ):
			# 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
			__lowerCamelCase          =      TapasConfig.from_json_file(lowercase__     )
			# set absolute/relative position embeddings parameter
			__lowerCamelCase          =      reset_position_index_per_cell

			# set remaining parameters of TapasConfig as well as the model based on the task
			if task == "SQA":
						__lowerCamelCase          =      TapasForQuestionAnswering(config=lowercase__     )
			elif task == "WTQ":
						# run_task_main.py hparams
						__lowerCamelCase          =      4
						__lowerCamelCase          =      True
						# hparam_utils.py hparams
						__lowerCamelCase          =      0.664694
						__lowerCamelCase          =      0.207951
						__lowerCamelCase          =      0.121194
						__lowerCamelCase          =      True
						__lowerCamelCase          =      True
						__lowerCamelCase          =      False
						__lowerCamelCase          =      0.0352513

						__lowerCamelCase          =      TapasForQuestionAnswering(config=lowercase__     )
			elif task == "WIKISQL_SUPERVISED":
						# run_task_main.py hparams
						__lowerCamelCase          =      4
						__lowerCamelCase          =      False
						# hparam_utils.py hparams
						__lowerCamelCase          =      36.4519
						__lowerCamelCase          =      0.903421
						__lowerCamelCase          =      222.088
						__lowerCamelCase          =      True
						__lowerCamelCase          =      True
						__lowerCamelCase          =      True
						__lowerCamelCase          =      0.763141

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

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

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

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


if __name__ == "__main__":
					__magic_name__						: Optional[int]    =				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.'
					)
					__magic_name__						: Dict    =				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       a_ (	lowercase__ :list[float]     ):
			if len(lowercase__     ) < 2:
						raise ValueError("""Monogons and Digons are not polygons in the Euclidean space"""     )
			if any(i <= 0 for i in nums     ):
						raise ValueError("""All values must be greater than 0"""     )
			__lowerCamelCase          =      nums.copy()
			copy_nums.sort()
			return copy_nums[-1] < sum(copy_nums[:-1]     )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




