






'''simple docstring'''
def 	_a(					UpperCamelCase__			:  str							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=0

      # if input_string is "aba" than new_input_string become "a|b|a"
      SCREAMING_SNAKE_CASE__       :			Tuple													=''''''
      SCREAMING_SNAKE_CASE__       :			Tuple													=''''''

      # append each character + "|" in new_string for range(0, length-1)
      for i in input_string[: len(UpperCamelCase__							) - 1]:
            new_input_string += i + "|"
      # append last character
      new_input_string += input_string[-1]

      # we will store the starting and ending of previous furthest ending palindromic
      # substring
      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Tuple													=0, 0

      # length[i] shows the length of palindromic substring with center i
      SCREAMING_SNAKE_CASE__       :			Tuple													=[1 for i in range(len(UpperCamelCase__							)							)]

      # for each character in new_string find corresponding palindromic string
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=0
      for j in range(len(UpperCamelCase__							)							):
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=1 if j > r else min(length[l + r - j] // 2,		r - j + 1							)
            while (
                j - k >= 0
                and j + k < len(UpperCamelCase__							)
                and new_input_string[k + j] == new_input_string[j - k]
            ):
                  k += 1

            SCREAMING_SNAKE_CASE__       :			Tuple													=2 * k - 1

            # does this string is ending after the previously explored end (that is r) ?
            # if yes the update the new r to the last index of this
            if j + k - 1 > r:
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=j - k + 1  # noqa: E741
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=j + k - 1

            # update max_length and start position
            if max_length < length[j]:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=length[j]
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=j

    # create that string
      SCREAMING_SNAKE_CASE__       :			Dict													=new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
      for i in s:
            if i != "|":
                  output_string += i

      return output_string


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''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        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 def        __magic_name__		( self		:							List[str] )		->  Optional[Any]:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        __magic_name__		( self		:							Union[str, Any] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
       SCREAMING_SNAKE_CASE__       :			str													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			List[Any]													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			List[str]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=output.images

       SCREAMING_SNAKE_CASE__       :			int													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Any													=np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )

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




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			int													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_euler''' )

       SCREAMING_SNAKE_CASE__       :			Any													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Dict													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe([prompt]   , generator=__lowercase   , guidance_scale=9.0   , num_inference_steps=20   , output_type='''np''' )

       SCREAMING_SNAKE_CASE__       :			Dict													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			List[str]													=np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )

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




 def        __magic_name__		( self		:							List[str] )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			Any													=StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
       SCREAMING_SNAKE_CASE__       :			Tuple													=sd_pipe.to(__lowercase )
       sd_pipe.set_progress_bar_config(disable=__lowercase )

       sd_pipe.set_scheduler('''sample_dpmpp_2m''' )

       SCREAMING_SNAKE_CASE__       :			Tuple													='''A painting of a squirrel eating a burger'''
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=torch.manual_seed(0 )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=sd_pipe(
           [prompt]   , generator=__lowercase   , guidance_scale=7.5   , num_inference_steps=15   , output_type='''np'''   , use_karras_sigmas=__lowercase   , )

       SCREAMING_SNAKE_CASE__       :			Optional[int]													=output.images

       SCREAMING_SNAKE_CASE__       :			Dict													=image[0, -3:, -3:, -1]

       assert image.shape == (1, 5_12, 5_12, 3)
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=np.array(
           [0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )

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