

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__ :     Union[str, Any]				    =    {
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase__ :     Dict				    =    [
      """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """MegatronBertForCausalLM""",
      """MegatronBertForMaskedLM""",
      """MegatronBertForMultipleChoice""",
      """MegatronBertForNextSentencePrediction""",
      """MegatronBertForPreTraining""",
      """MegatronBertForQuestionAnswering""",
      """MegatronBertForSequenceClassification""",
      """MegatronBertForTokenClassification""",
      """MegatronBertModel""",
      """MegatronBertPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_megatron_bert import (
        MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegatronBertForCausalLM,
        MegatronBertForMaskedLM,
        MegatronBertForMultipleChoice,
        MegatronBertForNextSentencePrediction,
        MegatronBertForPreTraining,
        MegatronBertForQuestionAnswering,
        MegatronBertForSequenceClassification,
        MegatronBertForTokenClassification,
        MegatronBertModel,
        MegatronBertPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase__ :     int				    =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







from __future__ import annotations

import numpy as np






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

      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   np.zeros((rows, columns)			)
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   np.zeros((rows, columns)			)
      for i in range(_A			):
            for j in range(_A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  if upper[j][j] == 0:
                        raise ArithmeticError("""No LU decomposition exists"""			)
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   (table[i][j] - total) / upper[j][j]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   1
            for j in range(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      List[str]       =   sum(lower[i][k] * upper[k][j] for k in range(_A			)			)
                  SCREAMING_SNAKE_CASE :      Optional[Any]       =   table[i][j] - total
      return lower, upper


if __name__ == "__main__":
  import doctest

  doctest.testmod()




