

import numpy as np
from numpy import floataa
from numpy.typing import NDArray
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      NDArray[floataa]       ,     SCREAMING_SNAKE_CASE__     :      NDArray[floataa] )				-> NDArray[floataa]:





    '''simple docstring'''






    A__ ,		A__        =  np.shape(SCREAMING_SNAKE_CASE__ )

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

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

    return x
def 		_snake_case(       SCREAMING_SNAKE_CASE__     :      NDArray[floataa]       ,     SCREAMING_SNAKE_CASE__     :      NDArray[floataa] )				-> NDArray[floataa]:





    '''simple docstring'''






    A__ ,		A__        =  np.shape(SCREAMING_SNAKE_CASE__ )
    if rows != columns:
        return np.array(()       ,     dtype=SCREAMING_SNAKE_CASE__ )

    # 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       ,     SCREAMING_SNAKE_CASE__ ):
            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"""

import random
import unittest

import torch

from diffusers import IFSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin

@skip_mps
class __lowerCAmelCase						( __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ):
     lowercase		  =		IFSuperResolutionPipeline
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"width", "height"}
     lowercase		  =		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowercase		  =		PipelineTesterMixin.required_optional_params - {"latents"}





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      return self._get_superresolution_dummy_components()





     def 		UpperCAmelCase   (      self      ,	__UpperCAmelCase      ,	__UpperCAmelCase=0 ):

      '''simple docstring'''




      if str(__UpperCAmelCase ).startswith('mps' ):
       __UpperCamelCase							    =			torch.manual_seed(__UpperCAmelCase )
      else:
       __UpperCamelCase							    =			torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )

      __UpperCamelCase							    =			floats_tensor((1, 3, 32, 32)      ,	rng=random.Random(__UpperCAmelCase ) ).to(__UpperCAmelCase )

      __UpperCamelCase							    =			{
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 2,
          'output_type': 'numpy',
      }

      return inputs





     @unittest.skipIf(
         torch_device != 'cuda' or not is_xformers_available()      ,	reason='XFormers attention is only available with CUDA and `xformers` installed'      ,	)
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_optional_components()





     @unittest.skipIf(torch_device != 'cuda'      ,	reason='float16 requires CUDA' )
     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      super().test_save_load_floataa(expected_max_diff=1E-1 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )





     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_save_load_local()







     def 		UpperCAmelCase   (      self ):

      '''simple docstring'''




      self._test_inference_batch_single_identical(
          expected_max_diff=1E-2      ,	)



