






from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def 					snake_case_						(   lowerCAmelCase_				:  NDArray[floataa]				,				lowerCAmelCase_				:  NDArray[floataa]				,				lowerCAmelCase_				:  list[int]				,				lowerCAmelCase_				:  int				,				):
							__lowercase	,	__lowercase					:							Dict			=   coefficient_matrix.shape
							__lowercase	,	__lowercase					:							Any			=   constant_matrix.shape

							if rowsa != colsa:
														__lowercase					:							Union[str, Any]			=   F"Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"
														raise ValueError(lowerCAmelCase_					)

							if colsa != 1:
														__lowercase					:							List[str]			=   F"Constant matrix must be nx1 but received {rowsa}x{colsa}"
														raise ValueError(lowerCAmelCase_					)

							if rowsa != rowsa:
														__lowercase					:							Optional[int]			=   (
														    """Coefficient and constant matrices dimensions must be nxn and nx1 but """
														    F"received {rowsa}x{colsa} and {rowsa}x{colsa}"
														)
														raise ValueError(lowerCAmelCase_					)

							if len(lowerCAmelCase_					) != rowsa:
														__lowercase					:							Dict			=   (
														    """Number of initial values must be equal to number of rows in coefficient """
														    F"matrix but received {len(lowerCAmelCase_					)} and {rowsa}"
														)
														raise ValueError(lowerCAmelCase_					)

							if iterations <= 0:
														raise ValueError("""Iterations must be at least 1"""					)

							__lowercase					:							NDArray[floataa]			=   np.concatenate(
							    (coefficient_matrix, constant_matrix)				,				axis=1					)

							__lowercase	,	__lowercase					:							str			=   table.shape

							strictly_diagonally_dominant(lowerCAmelCase_					)

							# Iterates the whole matrix for given number of times
							for _ in range(lowerCAmelCase_					):
														__lowercase					:							Optional[Any]			=   []
														for row in range(lowerCAmelCase_					):
																					__lowercase					:							Any			=   0
																					for col in range(lowerCAmelCase_					):
																												if col == row:
																																			__lowercase					:							str			=   table[row][col]
																												elif col == cols - 1:
																																			__lowercase					:							Any			=   table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					__lowercase					:							Optional[int]			=   (temp + val) / denom
																					new_val.append(lowerCAmelCase_					)
														__lowercase					:							Optional[Any]			=   new_val

							return [float(lowerCAmelCase_					) for i in new_val]
def 					snake_case_						(   lowerCAmelCase_				:  NDArray[floataa]					):
							__lowercase	,	__lowercase					:							int			=   table.shape

							__lowercase					:							List[str]			=   True

							for i in range(0				,				lowerCAmelCase_					):
														__lowercase					:							Union[str, Any]			=   0
														for j in range(0				,				cols - 1					):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""					)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
	import doctest

	doctest.testmod()







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 					snake_case_						(   lowerCAmelCase_				:  List[Any]				,				lowerCAmelCase_				:  Dict				,				lowerCAmelCase_				:  Any				,				lowerCAmelCase_				:  Tuple				,				lowerCAmelCase_				:  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
							__lowercase					:							Optional[int]			=   TapasConfig.from_json_file(lowerCAmelCase_					)
							# set absolute/relative position embeddings parameter
							__lowercase					:							Optional[Any]			=   reset_position_index_per_cell

							# set remaining parameters of TapasConfig as well as the model based on the task
							if task == "SQA":
														__lowercase					:							Union[str, Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WTQ":
														# run_task_main.py hparams
														__lowercase					:							List[Any]			=   4
														__lowercase					:							Union[str, Any]			=   True
														# hparam_utils.py hparams
														__lowercase					:							Any			=   0.664_694
														__lowercase					:							Tuple			=   0.207_951
														__lowercase					:							Dict			=   0.121_194
														__lowercase					:							List[str]			=   True
														__lowercase					:							str			=   True
														__lowercase					:							Dict			=   False
														__lowercase					:							Tuple			=   0.0_352_513

														__lowercase					:							List[Any]			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "WIKISQL_SUPERVISED":
														# run_task_main.py hparams
														__lowercase					:							Optional[int]			=   4
														__lowercase					:							int			=   False
														# hparam_utils.py hparams
														__lowercase					:							Tuple			=   36.4_519
														__lowercase					:							str			=   0.903_421
														__lowercase					:							List[Any]			=   222.088
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Tuple			=   True
														__lowercase					:							Union[str, Any]			=   True
														__lowercase					:							Optional[Any]			=   0.763_141

														__lowercase					:							str			=   TapasForQuestionAnswering(config=lowerCAmelCase_					)
							elif task == "TABFACT":
														__lowercase					:							List[Any]			=   TapasForSequenceClassification(config=lowerCAmelCase_					)
							elif task == "MLM":
														__lowercase					:							Optional[int]			=   TapasForMaskedLM(config=lowerCAmelCase_					)
							elif task == "INTERMEDIATE_PRETRAINING":
														__lowercase					:							Dict			=   TapasModel(config=lowerCAmelCase_					)
							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(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)

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

							# Save tokenizer files
							print(F"Save tokenizer files to {pytorch_dump_path}"					)
							__lowercase					:							Any			=   TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""				,				model_max_length=512					)
							tokenizer.save_pretrained(lowerCAmelCase_					)

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


if __name__ == "__main__":
	lowerCamelCase       :       List[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.'''
	)
	lowerCamelCase       :       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,
	)