




import numpy as np
from numpy import floataa
from numpy.typing import NDArray






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_    , a_		    =  np.shape(lowercase__			)

  a_		    =  np.zeros((rows, 1)      ,      dtype=lowercase__			)
  for row in reversed(range(lowercase__			)			):
    a_		    =  0
    for col in range(row + 1      ,      lowercase__			):
      total += coefficients[row, col] * x[col]

    a_		    =  (vector[row] - total) / coefficients[row, row]

  return x






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_    , a_		    =  np.shape(lowercase__			)
  if rows != columns:
    return np.array(()      ,      dtype=lowercase__			)

  # augmented matrix
  a_		    =  np.concatenate((coefficients, vector)      ,      axis=1			)
  a_		    =  augmented_mat.astype('''float64'''			)

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

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

  return x


if __name__ == "__main__":
  import doctest

  doctest.testmod()



"""simple docstring"""




from typing import TYPE_CHECKING

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


__A	:       Dict        =						{'configuration_cvt': ['CVT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'CvtConfig']}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       Any        =						[
	    'CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'CvtForImageClassification',
	    'CvtModel',
	    'CvtPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__A	:       List[str]        =						[
	    'TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFCvtForImageClassification',
	    'TFCvtModel',
	    'TFCvtPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_cvt import CVT_PRETRAINED_CONFIG_ARCHIVE_MAP, CvtConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cvt import (
		    CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CvtForImageClassification,
		    CvtModel,
		    CvtPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_cvt import (
		    TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFCvtForImageClassification,
		    TFCvtModel,
		    TFCvtPreTrainedModel,
		)


else:
	import sys

	__A	:       str        =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





