




'''simple docstring'''





from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "configuration_audio_spectrogram_transformer": [
        "AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ASTConfig",
    ]
}

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

try:
		if not is_speech_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_SCREAMING_SNAKE_CASE							:       Optional[int]						=			["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

		_SCREAMING_SNAKE_CASE							:       Dict						=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''





from __future__ import annotations

import numpy as np


def 					UpperCamelCase_(     snake_case							:	np.ndarray					):

							'''simple docstring'''






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

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


if __name__ == "__main__":
		import doctest

		doctest.testmod()




