





'''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				):
 def      UpperCAmelCase_							(				self      :List[str]							)->							Optional[Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()
 def      UpperCAmelCase_							(				self      :List[str]							)->							List[Any]:
     A__				=			StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"							)
     A__				=			sd_pipe.to(lowercase_							)
     sd_pipe.set_progress_bar_config(disable=lowercase_							)

     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=lowercase_						,       guidance_scale=9.0						,       num_inference_steps=20						,       output_type="np"							)

     A__				=			output.images

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

     assert image.shape == (1, 5_12, 5_12, 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      UpperCAmelCase_							(				self      :Tuple							)->							Optional[Any]:
     A__				=			StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
     A__				=			sd_pipe.to(lowercase_							)
     sd_pipe.set_progress_bar_config(disable=lowercase_							)

     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=lowercase_						,       guidance_scale=9.0						,       num_inference_steps=20						,       output_type="np"							)

     A__				=			output.images

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

     assert image.shape == (1, 5_12, 5_12, 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      UpperCAmelCase_							(				self      :Tuple							)->							List[Any]:
     A__				=			StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"							)
     A__				=			sd_pipe.to(lowercase_							)
     sd_pipe.set_progress_bar_config(disable=lowercase_							)

     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=lowercase_						,       guidance_scale=7.5						,       num_inference_steps=15						,       output_type="np"						,       use_karras_sigmas=lowercase_						,       )

     A__				=			output.images

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

     assert image.shape == (1, 5_12, 5_12, 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'''

import functools





def 						UpperCamelCase				(							_lowerCamelCase					:					str						,       _lowerCamelCase					:					str       ):
    A__				=			len(_lowerCamelCase       )
    A__				=			len(_lowerCamelCase       )

    @functools.cache
    def min_distance(_lowerCamelCase					:					int						,       _lowerCamelCase					:					int       ) -> int:
        # if first word index is overflow - delete all from the second word
        if indexa >= len_worda:
            return len_worda - indexa
        # if second word index is overflow - delete all from the first word
        if indexa >= len_worda:
            return len_worda - indexa
        A__				=			int(worda[indexa] != worda[indexa]       )  # current letters not identical
        return min(
            1 + min_distance(indexa + 1						,       _lowerCamelCase       )						,       1 + min_distance(_lowerCamelCase						,       indexa + 1       )						,       diff + min_distance(indexa + 1						,       indexa + 1       )						,       )

    return min_distance(0						,       0       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()



