



"""simple docstring"""
from __future__ import annotations

import numpy as np


def       _SCREAMING_SNAKE_CASE    (__lowerCAmelCase				)			->    List[str]:







  '''simple docstring'''


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

  lowercase_             =			np.zeros((rows, columns)				)
  lowercase_             =			np.zeros((rows, columns)				)
  for i in range(_UpperCamelCase				):
    for j in range(_UpperCamelCase				):
      lowercase_             =			sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase				)				)
      if upper[j][j] == 0:
        raise ArithmeticError("""No LU decomposition exists"""				)
      lowercase_             =			(table[i][j] - total) / upper[j][j]
    lowercase_             =			1
    for j in range(_UpperCamelCase			,	_UpperCamelCase				):
      lowercase_             =			sum(lower[i][k] * upper[k][j] for k in range(_UpperCamelCase				)				)
      lowercase_             =			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	:  Union[str, Any]            =     {
    "configuration_timesformer": ["TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "TimesformerConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A	:  Optional[int]            =     [
	    "TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TimesformerModel",
	    "TimesformerForVideoClassification",
	    "TimesformerPreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	A	:  List[str]            =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




