


import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def 							_UpperCAmelCase       (UpperCamelCase__  :						NDArray[floataa]       ,							UpperCamelCase__  :						NDArray[floataa]		):
       _A   : Any       					=				np.shape(UpperCamelCase__		)

       _A   : NDArray[floataa]       					=				np.zeros((rows, 1)       ,							dtype=UpperCamelCase__		)
       for row in reversed(range(UpperCamelCase__		)		):
              _A   : int       					=				0
              for col in range(row + 1       ,							UpperCamelCase__		):
                     total += coefficients[row, col] * x[col]

              _A   : Optional[Any]       					=				(vector[row] - total) / coefficients[row, row]

       return x
def 							_UpperCAmelCase       (UpperCamelCase__  :						NDArray[floataa]       ,							UpperCamelCase__  :						NDArray[floataa]		):
       _A   : Optional[Any]       					=				np.shape(UpperCamelCase__		)
       if rows != columns:
              return np.array(()       ,							dtype=UpperCamelCase__		)

       # augmented matrix
       _A   : NDArray[floataa]       					=				np.concatenate((coefficients, vector)       ,							axis=1		)
       _A   : List[str]       					=				augmented_mat.astype("float64"		)

       # scale the matrix leaving it triangular
       for row in range(rows - 1		):
              _A   : str       					=				augmented_mat[row, row]
              for col in range(row + 1       ,							UpperCamelCase__		):
                     _A   : Dict       					=				augmented_mat[col, row] / pivot
                     augmented_mat[col, :] -= factor * augmented_mat[row, :]

       _A   : Any       					=				retroactive_resolution(
           augmented_mat[:, 0:columns]       ,							augmented_mat[:, columns : columns + 1]		)

       return x


if __name__ == "__main__":
    import doctest

    doctest.testmod()




from typing import TYPE_CHECKING

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


lowerCAmelCase__   	=				{
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				['DetaImageProcessor']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase__   	=				[
        'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'DetaForObjectDetection',
        'DetaModel',
        'DetaPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .image_processing_deta import DetaImageProcessor

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_deta import (
            DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
            DetaForObjectDetection,
            DetaModel,
            DetaPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
