
'''simple docstring'''





import numpy as np





def        snake_case							(		UpperCAmelCase	)->			Optional[int]:







						"""simple docstring"""
						__A	,      __A       	=			a.shape
						__A       	=			min(UpperCAmelCase      ,  UpperCAmelCase	)
						__A       	=			np.eye(UpperCAmelCase	)
						__A       	=			a.copy()

						for k in range(t - 1	):
												# select a column of modified matrix A':
												__A       	=			r[k:, [k]]
												# construct first basis vector
												__A       	=			np.zeros_like(UpperCAmelCase	)
												__A       	=			1.0
												# determine scaling factor
												__A       	=			np.linalg.norm(UpperCAmelCase	)
												# construct vector v for Householder reflection
												__A       	=			x + np.sign(x[0]	) * alpha * ea
												v /= np.linalg.norm(UpperCAmelCase	)

												# construct the Householder matrix
												__A       	=			np.eye(m - k	) - 2.0 * v @ v.T
												# pad with ones and zeros as necessary
												__A       	=			np.block([[np.eye(UpperCAmelCase	), np.zeros((k, m - k)	)], [np.zeros((m - k, k)	), q_k]]	)

												__A       	=			q @ q_k.T
												__A       	=			q_k @ r

						return q, r


if __name__ == "__main__":
	import doctest

	doctest.testmod()








'''simple docstring'''





from typing import TYPE_CHECKING

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


a__		:   Optional[Any]            =       {
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a__		:   Optional[int]            =       [
     "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
     "MobileViTV2ForImageClassification",
     "MobileViTV2ForSemanticSegmentation",
     "MobileViTV2Model",
     "MobileViTV2PreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_mobilevitva import (
     MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     MobileViTVaConfig,
     MobileViTVaOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mobilevitva import (
      MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
      MobileViTVaForImageClassification,
      MobileViTVaForSemanticSegmentation,
      MobileViTVaModel,
      MobileViTVaPreTrainedModel,
  )

else:
 import sys

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






