





from __future__ import annotations

import numpy as np



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Optional[int]:


      '''simple docstring'''


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

      SCREAMING_SNAKE_CASE		=						np.zeros((rows, columns)     )
      SCREAMING_SNAKE_CASE		=						np.zeros((rows, columns)     )
      for i in range(_SCREAMING_SNAKE_CASE     ):
            for j in range(_SCREAMING_SNAKE_CASE     ):
                  SCREAMING_SNAKE_CASE		=						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"""     )
                  SCREAMING_SNAKE_CASE		=						(table[i][j] - total) / upper[j][j]
            SCREAMING_SNAKE_CASE		=						1
            for j in range(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     ):
                  SCREAMING_SNAKE_CASE		=						sum(lower[i][k] * upper[k][j] for k in range(_SCREAMING_SNAKE_CASE     )     )
                  SCREAMING_SNAKE_CASE		=						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


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
    """tokenization_mvp""": ["""MvpTokenizer"""],
}

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MVP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MvpForCausalLM""",
          """MvpForConditionalGeneration""",
          """MvpForQuestionAnswering""",
          """MvpForSequenceClassification""",
          """MvpModel""",
          """MvpPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
      from .tokenization_mvp import MvpTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_mvp_fast import MvpTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_mvp import (
                MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
                MvpForCausalLM,
                MvpForConditionalGeneration,
                MvpForQuestionAnswering,
                MvpForSequenceClassification,
                MvpModel,
                MvpPreTrainedModel,
            )

else:
      import sys

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


