"""simple docstring"""





def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    list[list]:
							_SCREAMING_SNAKE_CASE		:						Any			  =    current_set.copy()
							for row_index, row in enumerate(__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    row[0]
														for column_index, column in enumerate(__SCREAMING_SNAKE_CASE  ):
																					if magnitude == 0:
																												_SCREAMING_SNAKE_CASE		:						List[Any]			  =    column
																												continue
																					_SCREAMING_SNAKE_CASE		:						List[str]			  =    column / magnitude
    # Subtract to cancel term
							_SCREAMING_SNAKE_CASE		:						int			  =    current_set[0]
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    [first_row]
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    current_set[1::]
							for row in current_set:
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    []
														# If first term is 0, it is already in form we want, so we preserve it
														if row[0] == 0:
																					final_set.append(__SCREAMING_SNAKE_CASE  )
																					continue
														for column_index in range(len(__SCREAMING_SNAKE_CASE  )  ):
																					temp_row.append(first_row[column_index] - row[column_index]  )
														final_set.append(__SCREAMING_SNAKE_CASE  )
							# Create next recursion iteration set
							if len(final_set[0]  ) != 3:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    final_set[0]
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
														_SCREAMING_SNAKE_CASE		:						int			  =    []
														for row in final_set[1::]:
																					current_first_column.append(row[0]  )
																					next_iteration.append(row[1::]  )
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    simplify(__SCREAMING_SNAKE_CASE  )
														for i in range(len(__SCREAMING_SNAKE_CASE  )  ):
																					resultant[i].insert(0     ,	current_first_column[i]  )
														resultant.insert(0     ,	__SCREAMING_SNAKE_CASE  )
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    resultant
							return final_set



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    list:
							if len(__SCREAMING_SNAKE_CASE  ) == 0:
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""  )
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    len(__SCREAMING_SNAKE_CASE  ) + 1
							if any(len(__SCREAMING_SNAKE_CASE  ) != _length for item in equations  ):
														raise IndexError("""solve_simultaneous() requires n lists of length n+1"""  )
							for row in equations:
														if any(not isinstance(__SCREAMING_SNAKE_CASE     ,	(int, float)  ) for column in row  ):
																					raise ValueError("""solve_simultaneous() requires lists of integers"""  )
							if len(__SCREAMING_SNAKE_CASE  ) == 1:
														return [equations[0][-1] / equations[0][0]]
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    equations.copy()
							if any(0 in row for row in data_set  ):
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    data_set.copy()
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
														for row_index, row in enumerate(__SCREAMING_SNAKE_CASE  ):
																					if 0 not in row:
																												_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    data_set.pop(__SCREAMING_SNAKE_CASE  )
																												break
														if not full_row:
																					raise ValueError("""solve_simultaneous() requires at least 1 full equation"""  )
														data_set.insert(0     ,	__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    data_set.copy()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    simplify(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    simplified[::-1]
							_SCREAMING_SNAKE_CASE		:						list			  =    []
							for row in simplified:
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    row[-1]
														if not solutions:
																					if row[-2] == 0:
																												solutions.append(0  )
																												continue
																					solutions.append(current_solution / row[-2]  )
																					continue
														_SCREAMING_SNAKE_CASE		:						List[str]			  =    row.copy()[: len(__SCREAMING_SNAKE_CASE  ) - 1 :]
														while temp_row[0] == 0:
																					temp_row.pop(0  )
														if len(__SCREAMING_SNAKE_CASE  ) == 0:
																					solutions.append(0  )
																					continue
														_SCREAMING_SNAKE_CASE		:						int			  =    temp_row[1::]
														_SCREAMING_SNAKE_CASE		:						str			  =    temp_row[::-1]
														for column_index, column in enumerate(__SCREAMING_SNAKE_CASE  ):
																					current_solution -= column * solutions[column_index]
														solutions.append(__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    []
							for item in solutions:
														final.append(float(round(__SCREAMING_SNAKE_CASE     ,	5  )  )  )
							return final[::-1]


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			lowerCAmelCase_     =	[
			    [2, 1, 1, 1, 1, 4],
			    [1, 2, 1, 1, 1, 5],
			    [1, 1, 2, 1, 1, 6],
			    [1, 1, 1, 2, 1, 7],
			    [1, 1, 1, 1, 2, 8],
			]
			print(solve_simultaneous(eq))
			print(solve_simultaneous([[4, 2]]))



"""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  )->    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
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    TapasConfig.from_json_file(__SCREAMING_SNAKE_CASE  )
							# set absolute/relative position embeddings parameter
							_SCREAMING_SNAKE_CASE		:						Dict			  =    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=__SCREAMING_SNAKE_CASE  )
							elif task == "WTQ":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    4
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    0.66_46_94
														_SCREAMING_SNAKE_CASE		:						str			  =    0.20_79_51
														_SCREAMING_SNAKE_CASE		:						str			  =    0.12_11_94
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    False
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    0.0_35_25_13

														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														_SCREAMING_SNAKE_CASE		:						int			  =    4
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    False
														# hparam_utils.py hparams
														_SCREAMING_SNAKE_CASE		:						Any			  =    36.45_19
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    0.90_34_21
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_22.0_88
														_SCREAMING_SNAKE_CASE		:						Any			  =    True
														_SCREAMING_SNAKE_CASE		:						str			  =    True
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    True
														_SCREAMING_SNAKE_CASE		:						Dict			  =    0.76_31_41

														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    TapasForQuestionAnswering(config=__SCREAMING_SNAKE_CASE  )
							elif task == "TABFACT":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForSequenceClassification(config=__SCREAMING_SNAKE_CASE  )
							elif task == "MLM":
														_SCREAMING_SNAKE_CASE		:						int			  =    TapasForMaskedLM(config=__SCREAMING_SNAKE_CASE  )
							elif task == "INTERMEDIATE_PRETRAINING":
														_SCREAMING_SNAKE_CASE		:						int			  =    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}"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    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__":
			lowerCAmelCase_     =	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.'''
			)
			lowerCAmelCase_     =	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,
			)


