





from __future__ import annotations

import numpy as np




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

  lowercase__			    =						np.zeros((rows, columns)	)
  lowercase__			    =						np.zeros((rows, columns)	)
  for i in range(_SCREAMING_SNAKE_CASE	):
    for j in range(_SCREAMING_SNAKE_CASE	):
      lowercase__			    =						sum(lower[i][k] * upper[k][j] for k in range(_SCREAMING_SNAKE_CASE	)	)
      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(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	):
      lowercase__			    =						sum(lower[i][k] * upper[k][j] for k in range(_SCREAMING_SNAKE_CASE	)	)
      lowercase__			    =						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


lowercase_					     =	{
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	["""SqueezeBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	[
          """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SqueezeBertForMaskedLM""",
          """SqueezeBertForMultipleChoice""",
          """SqueezeBertForQuestionAnswering""",
          """SqueezeBertForSequenceClassification""",
          """SqueezeBertForTokenClassification""",
          """SqueezeBertModel""",
          """SqueezeBertModule""",
          """SqueezeBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

      lowercase_					     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


