


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   (      __magic_name__	:				Any       ,       __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				List[Any]	)  ->    Dict:
		"""simple docstring"""




		lowercase__	     =						TapasConfig.from_json_file(__magic_name__	)
		# 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=__magic_name__	)
		elif task == "WTQ":
				# run_task_main.py hparams
				lowercase__	     =						4
				lowercase__	     =						True
				# hparam_utils.py hparams
				lowercase__	     =						0.6_6_4_6_9_4
				lowercase__	     =						0.2_0_7_9_5_1
				lowercase__	     =						0.1_2_1_1_9_4
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						False
				lowercase__	     =						0.0_3_5_2_5_1_3

				lowercase__	     =						TapasForQuestionAnswering(config=__magic_name__	)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				lowercase__	     =						4
				lowercase__	     =						False
				# hparam_utils.py hparams
				lowercase__	     =						3_6.4_5_1_9
				lowercase__	     =						0.9_0_3_4_2_1
				lowercase__	     =						2_2_2.0_8_8
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						True
				lowercase__	     =						0.7_6_3_1_4_1

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

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

		# 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(__magic_name__	)

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


if __name__ == "__main__":
	A			:						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.'
	)
	A			:						Optional[int]        =  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  __magic_name__  (				lowercase				)				-> list[list]:

							"""simple docstring"""



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





def  __magic_name__  (				lowercase				)				-> list:

							"""simple docstring"""



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


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							UpperCAmelCase_							=				[
							    [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]]))