





from __future__ import annotations

import numpy as np


def       UpperCamelCase__(			UpperCamelCase__     :			np.ndarray    )->tuple[np.ndarray, np.ndarray]:
       A__   ,      A__		   =		np.shape(UpperCamelCase__    )
       if rows != columns:
              A__		   =		(
                  '''\'table\' has to be of square shaped array but got a '''
                  f"{rows}x{columns} array:\n{table}"
              )
              raise ValueError(UpperCamelCase__    )

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


if __name__ == "__main__":
       import doctest

       doctest.testmod()












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__: int							   =						{
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: List[Any]							   =						[
           'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
           'M2M100ForConditionalGeneration',
           'M2M100Model',
           'M2M100PreTrainedModel',
       ]


if TYPE_CHECKING:
       from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
       from .tokenization_mam_aaa import MaMaaaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mam_aaa import (
                  M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MaMaaaForConditionalGeneration,
                  MaMaaaModel,
                  MaMaaaPreTrainedModel,
              )


else:
       import sys

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





