




"""simple docstring"""







from __future__ import annotations

import numpy as np





def 							lowercase					(snake_case__   : np.ndarray )     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				np.shape(_lowerCamelCase )
				if rows != columns:
								lowerCAmelCase  =				(
								    "'table' has to be of square shaped array but got a "
								    f'''{rows}x{columns} array:\n{table}'''
								)
								raise ValueError(_lowerCamelCase )

				lowerCAmelCase  =				np.zeros((rows, columns) )
				lowerCAmelCase  =				np.zeros((rows, columns) )
				for i in range(_lowerCamelCase ):
								for j in range(_lowerCamelCase ):
												lowerCAmelCase  =				sum(lower[i][k] * upper[k][j] for k in range(_lowerCamelCase ) )
												if upper[j][j] == 0:
																raise ArithmeticError("""No LU decomposition exists""" )
												lowerCAmelCase  =				(table[i][j] - total) / upper[j][j]
								lowerCAmelCase  =				1
								for j in range(_lowerCamelCase      ,       _lowerCamelCase ):
												lowerCAmelCase  =				sum(lower[i][k] * upper[k][j] for k in range(_lowerCamelCase ) )
												lowerCAmelCase  =				table[i][j] - total
				return lower, upper


if __name__ == "__main__":
		import doctest

		doctest.testmod()











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a      =							{
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TimeSeriesTransformerForPrediction',
		    'TimeSeriesTransformerModel',
		    'TimeSeriesTransformerPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    TimeSeriesTransformerConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_time_series_transformer import (
				    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TimeSeriesTransformerForPrediction,
				    TimeSeriesTransformerModel,
				    TimeSeriesTransformerPreTrainedModel,
				)

else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





