

from __future__ import annotations

from collections.abc import Callable

A											=     list[list[float | int]]





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= len(SCREAMING_SNAKE_CASE_	)
	snake_case_      		= [[0 for _ in range(size + 1	)] for _ in range(SCREAMING_SNAKE_CASE_	)]
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42

	for row in range(SCREAMING_SNAKE_CASE_	):
		for col in range(SCREAMING_SNAKE_CASE_	):
			snake_case_      		= matrix[row][col]

		snake_case_      		= vector[row][0]

	snake_case_      		= 0
	snake_case_      		= 0
	while row < size and col < size:
		# pivoting
		snake_case_      		= max((abs(augmented[rowa][col]	), rowa) for rowa in range(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_	)	)[
		    1
		]
		if augmented[pivot_row][col] == 0:
			col += 1
			continue
		else:
			snake_case_		,       snake_case_      		= augmented[pivot_row], augmented[row]

		for rowa in range(row + 1			, SCREAMING_SNAKE_CASE_	):
			snake_case_      		= augmented[rowa][col] / augmented[row][col]
			snake_case_      		= 0
			for cola in range(col + 1			, size + 1	):
				augmented[rowa][cola] -= augmented[row][cola] * ratio

		row += 1
		col += 1

	# back substitution
	for col in range(1			, SCREAMING_SNAKE_CASE_	):
		for row in range(SCREAMING_SNAKE_CASE_	):
			snake_case_      		= augmented[row][col] / augmented[col][col]
			for cola in range(SCREAMING_SNAKE_CASE_			, size + 1	):
				augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
	return [
	    [round(augmented[row][size] / augmented[row][row]			, 10	)] for row in range(SCREAMING_SNAKE_CASE_	)
	]





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= len(SCREAMING_SNAKE_CASE_	)
	snake_case_      		= [[0 for _ in range(SCREAMING_SNAKE_CASE_	)] for _ in range(SCREAMING_SNAKE_CASE_	)]
	snake_case_      		= [[0] for _ in range(SCREAMING_SNAKE_CASE_	)]
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42
	snake_case_      		= 42

	for x_val, y_val in enumerate(SCREAMING_SNAKE_CASE_	):
		for col in range(SCREAMING_SNAKE_CASE_	):
			snake_case_      		= (x_val + 1) ** (size - col - 1)
		snake_case_      		= y_val

	snake_case_      		= solve(SCREAMING_SNAKE_CASE_			, SCREAMING_SNAKE_CASE_	)

	def interpolated_func(lowercase__	) -> int:
		return sum(
		    round(coeffs[x_val][0]	) * (var ** (size - x_val - 1))
		    for x_val in range(SCREAMING_SNAKE_CASE_	)	)

	return interpolated_func





def a(lowercase__	):





	'''simple docstring'''
	return (
	    1
	    - variable
	    + variable**2
	    - variable**3
	    + variable**4
	    - variable**5
	    + variable**6
	    - variable**7
	    + variable**8
	    - variable**9
	    + variable**10
	)





def a(lowercase__ = question_function			, lowercase__ = 10	):





	'''simple docstring'''
	snake_case_      		= [func(SCREAMING_SNAKE_CASE_	) for x_val in range(1			, order + 1	)]

	snake_case_      		= [
	    interpolate(data_points[:max_coeff]	) for max_coeff in range(1			, order + 1	)
	]

	snake_case_      		= 0
	snake_case_      		= 42
	snake_case_      		= 42

	for poly in polynomials:
		snake_case_      		= 1
		while func(SCREAMING_SNAKE_CASE_	) == poly(SCREAMING_SNAKE_CASE_	):
			x_val += 1

		ret += poly(SCREAMING_SNAKE_CASE_	)

	return ret


if __name__ == "__main__":
		print(f"""{solution() = }""")



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__			, lowercase__			, lowercase__			, lowercase__			, lowercase__	):





	'''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
	snake_case_      		= TapasConfig.from_json_file(lowercase__	)
	# set absolute/relative position embeddings parameter
	snake_case_      		= reset_position_index_per_cell

	# set remaining parameters of TapasConfig as well as the model based on the task
	if task == "SQA":
		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "WTQ":
		# run_task_main.py hparams
		snake_case_      		= 4
		snake_case_      		= True
		# hparam_utils.py hparams
		snake_case_      		= 0.66_4694
		snake_case_      		= 0.20_7951
		snake_case_      		= 0.12_1194
		snake_case_      		= True
		snake_case_      		= True
		snake_case_      		= False
		snake_case_      		= 0.035_2513

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

		snake_case_      		= TapasForQuestionAnswering(config=lowercase__	)
	elif task == "TABFACT":
		snake_case_      		= TapasForSequenceClassification(config=lowercase__	)
	elif task == "MLM":
		snake_case_      		= TapasForMaskedLM(config=lowercase__	)
	elif task == "INTERMEDIATE_PRETRAINING":
		snake_case_      		= 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}"""	)
	snake_case_      		= 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__":
		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,
		)
