





'''simple docstring'''







from __future__ import annotations

import numpy as np






def 					_lowerCamelCase							(					lowercase		: np.ndarray		)      ->      List[Any]:
				_a				      =					np.shape(A__		)
				if rows != columns:
								_a				      =					(
								    """'table' has to be of square shaped array but got a """
								    F'{rows}x{columns} array:\n{table}'
								)
								raise ValueError(A__		)

				_a				      =					np.zeros((rows, columns)		)
				_a				      =					np.zeros((rows, columns)		)
				for i in range(A__		):
								for j in range(A__		):
												_a				      =					sum(lower[i][k] * upper[k][j] for k in range(A__		)		)
												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(A__       ,				A__		):
												_a				      =					sum(lower[i][k] * upper[k][j] for k in range(A__		)		)
												_a				      =					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_speech_available, is_torch_available


__A   :    str											= {
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    List[str]											= [
      "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "ASTForAudioClassification",
      "ASTModel",
      "ASTPreTrainedModel",
  ]

try:
  if not is_speech_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __A   :    Union[str, Any]											= ["ASTFeatureExtractor"]

if TYPE_CHECKING:
  from .configuration_audio_spectrogram_transformer import (
      AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      ASTConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_audio_spectrogram_transformer import (
        AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        ASTForAudioClassification,
        ASTModel,
        ASTPreTrainedModel,
    )

  try:
    if not is_speech_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
  import sys

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



