



'''simple docstring'''


import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







@slow
@require_torch_gpu
class 	UpperCAmelCase	(		unittest.TestCase):


 '''simple docstring'''




 def       lowercase_  (   self) ->      str:



      """simple docstring"""







      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
      a_       		=sd_pipe.to(lowerCAmelCase_)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      sd_pipe.set_scheduler("sample_euler")

      a_       		="A painting of a squirrel eating a burger"
      a_       		=torch.manual_seed(0)
      a_       		=sd_pipe([prompt]							,  generator=lowerCAmelCase_							,  guidance_scale=9.0							,  num_inference_steps=2_0							,  output_type="np")

      a_       		=output.images

      a_       		=image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a_       		=np.array([0.0_4_4_7, 0.0_4_9_2, 0.0_4_6_8, 0.0_4_0_8, 0.0_3_8_3, 0.0_4_0_8, 0.0_3_5_4, 0.0_3_8_0, 0.0_3_3_9])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2




 def       lowercase_  (   self) ->      int:



      """simple docstring"""







      a_       		=StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
      a_       		=sd_pipe.to(lowerCAmelCase_)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      sd_pipe.set_scheduler("sample_euler")

      a_       		="A painting of a squirrel eating a burger"
      a_       		=torch.manual_seed(0)
      a_       		=sd_pipe([prompt]							,  generator=lowerCAmelCase_							,  guidance_scale=9.0							,  num_inference_steps=2_0							,  output_type="np")

      a_       		=output.images

      a_       		=image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a_       		=np.array([0.1_2_3_7, 0.1_3_2_0, 0.1_4_3_8, 0.1_3_5_9, 0.1_3_9_0, 0.1_1_3_2, 0.1_2_7_7, 0.1_1_7_5, 0.1_1_1_2])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-1




 def       lowercase_  (   self) ->      Any:



      """simple docstring"""







      a_       		=StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base")
      a_       		=sd_pipe.to(lowerCAmelCase_)
      sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_)

      sd_pipe.set_scheduler("sample_dpmpp_2m")

      a_       		="A painting of a squirrel eating a burger"
      a_       		=torch.manual_seed(0)
      a_       		=sd_pipe(
          [prompt]							,  generator=lowerCAmelCase_							,  guidance_scale=7.5							,  num_inference_steps=1_5							,  output_type="np"							,  use_karras_sigmas=lowerCAmelCase_							,  )

      a_       		=output.images

      a_       		=image[0, -3:, -3:, -1]

      assert image.shape == (1, 5_1_2, 5_1_2, 3)
      a_       		=np.array(
          [0.1_1_3_8_1_6_8_9, 0.1_2_1_1_2_9_2_1, 0.1_3_8_9_4_5_7, 0.1_2_5_4_9_6_0_6, 0.1_2_4_4_9_6_4, 0.1_0_8_3_1_5_1_7, 0.1_1_5_6_2_8_6_6, 0.1_0_8_6_7_8_1_6, 0.1_0_4_9_9_0_4_8])

      assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2







'''simple docstring'''


from typing import Any

import numpy as np






def  UpperCAmelCase_       (		lowercase__       ):


     '''simple docstring'''







     return np.array_equal(lowercase__       ,	matrix.conjugate().T       )






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=v.conjugate().T
     a_       		=v_star.dot(lowercase__       )
     assert isinstance(lowercase__       ,	np.ndarray       )
     return (v_star_dot.dot(lowercase__       )) / (v_star.dot(lowercase__       ))






def  UpperCAmelCase_       (		):


     '''simple docstring'''







     a_       		=np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
     a_       		=np.array([[1], [2], [3]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     print(rayleigh_quotient(lowercase__       ,	lowercase__       )       )

     a_       		=np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
     assert is_hermitian(lowercase__       ), F"""{a} is not hermitian."""
     assert rayleigh_quotient(lowercase__       ,	lowercase__       ) == float(3       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  tests()


