






"""simple docstring"""


import unittest

import numpy as np
import PIL
import torch

from diffusers.image_processor import VaeImageProcessor


class    snake_case						(				unittest.TestCase		):


   '''simple docstring'''

   @property
   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					1
     __A		     =					3
     __A		     =					8
     __A		     =					8

     __A		     =					torch.rand((batch_size, num_channels, height, width)    )

     return sample


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict,			_lowerCamelCase					:	Union[str, Any]    ):

     '''simple docstring'''
     if isinstance(image[0],			PIL.Image.Image    ):
       return np.stack([np.array(_lowerCamelCase    ) for i in image],			axis=0    )
     elif isinstance(_lowerCamelCase,			torch.Tensor    ):
       return image.cpu().numpy().transpose(0,			2,			3,			1    )
     return image


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample
     __A		     =					self.to_np(_lowerCamelCase    )

     for output_type in ["pt", "np", "pil"]:
       __A		     =					image_processor.postprocess(
           image_processor.preprocess(_lowerCamelCase    ),			output_type=_lowerCamelCase,			)
       __A		     =					self.to_np(_lowerCamelCase    )
       __A		     =					(input_np * 2_55).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np    ).max() < 1e-6
       ), f'decoded output does not match input for output_type {output_type}'


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )
     __A		     =					self.dummy_sample.cpu().numpy().transpose(0,			2,			3,			1    )

     for output_type in ["pt", "np", "pil"]:
       __A		     =					image_processor.postprocess(image_processor.preprocess(_lowerCamelCase    ),			output_type=_lowerCamelCase    )

       __A		     =					self.to_np(_lowerCamelCase    )
       __A		     =					(input_np * 2_55).round() if output_type == '''pil''' else input_np
       assert (
           np.abs(in_np - out_np    ).max() < 1e-6
       ), f'decoded output does not match input for output_type {output_type}'


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample.cpu().numpy().transpose(0,			2,			3,			1    )
     __A		     =					image_processor.numpy_to_pil(_lowerCamelCase    )

     for output_type in ["pt", "np", "pil"]:
       __A		     =					image_processor.postprocess(image_processor.preprocess(_lowerCamelCase    ),			output_type=_lowerCamelCase    )
       for i, o in zip(_lowerCamelCase,			_lowerCamelCase    ):
         __A		     =					np.array(_lowerCamelCase    )
         __A		     =					self.to_np(_lowerCamelCase    ) if output_type == '''pil''' else (self.to_np(_lowerCamelCase    ) * 2_55).round()
         assert (
             np.abs(in_np - out_np    ).max() < 1e-6
         ), f'decoded output does not match input for output_type {output_type}'


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample
     __A		     =					input_pt_ad.squeeze(0    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)
     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					self.to_np(self.dummy_sample    )
     __A		     =					input_np_ad.squeeze(0    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)
     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     assert np.abs(out_pt_ad - out_pt_ad    ).max() < 1e-6
     assert np.abs(out_np_ad - out_np_ad    ).max() < 1e-6






   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					VaeImageProcessor(do_resize=_lowerCamelCase,			do_normalize=_lowerCamelCase    )

     __A		     =					self.dummy_sample
     __A		     =					list(_lowerCamelCase    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					self.to_np(self.dummy_sample    )
     list(_lowerCamelCase    )

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     __A		     =					image_processor.postprocess(
         image_processor.preprocess(_lowerCamelCase    ),			output_type='''np''',			)

     assert np.abs(out_pt_ad - out_pt_list    ).max() < 1e-6
     assert np.abs(out_np_ad - out_np_list    ).max() < 1e-6










'''simple docstring'''
import numpy as np
def 							snake_case_    (  a__						:      Tuple     ):



    """simple docstring"""




    __lowercase							,__lowercase     							=		a.shape
    __lowercase     							=		min(a__       ,a__     )
    __lowercase     							=		np.eye(a__     )
    __lowercase     							=		a.copy()

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

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

        __lowercase     							=		q @ q_k.T
        __lowercase     							=		q_k @ r

    return q, r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




