






import itertools
import string
from collections.abc import Generator, Iterable






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  iter(__lowercase)
   while True:
      UpperCamelCase_						=  tuple(itertools.islice(__lowercase     ,   __lowercase))
      if not chunk:
         return
      yield chunk






def      _snake_case     (__lowercase):
   UpperCamelCase_						=  ''.join([c.upper() for c in dirty if c in string.ascii_letters])
   UpperCamelCase_						=  ''

   if len(__lowercase) < 2:
      return dirty

   for i in range(len(__lowercase) - 1):
      clean += dirty[i]

      if dirty[i] == dirty[i + 1]:
         clean += "X"

   clean += dirty[-1]

   if len(__lowercase) & 1:
      clean += "X"

   return clean






def      _snake_case     (__lowercase):
   # I and J are used interchangeably to allow
   # us to use a 5x5 table (25 letters)
   UpperCamelCase_						=  'ABCDEFGHIKLMNOPQRSTUVWXYZ'
   # we're using a list instead of a '2d' array because it makes the math
   # for setting up the table and doing the actual encoding/decoding simpler
   UpperCamelCase_						=  []

   # copy key chars into the table if they are in `alphabet` ignoring duplicates
   for char in key.upper():
      if char not in table and char in alphabet:
         table.append(__lowercase)

    # fill the rest of the table in with the remaining alphabet chars
   for char in alphabet:
      if char not in table:
         table.append(__lowercase)

   return table






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  generate_table(__lowercase)
   UpperCamelCase_						=  prepare_input(__lowercase)
   UpperCamelCase_						=  ''

   # https://en.wikipedia.org/wiki/Playfair_cipher#Description
   for chara, chara in chunker(__lowercase     ,   2):
      UpperCamelCase_		,   UpperCamelCase_						=  divmod(table.index(__lowercase)     ,   5)
      UpperCamelCase_		,   UpperCamelCase_						=  divmod(table.index(__lowercase)     ,   5)

      if rowa == rowa:
         ciphertext += table[rowa * 5 + (cola + 1) % 5]
         ciphertext += table[rowa * 5 + (cola + 1) % 5]
      elif cola == cola:
         ciphertext += table[((rowa + 1) % 5) * 5 + cola]
         ciphertext += table[((rowa + 1) % 5) * 5 + cola]
      else:  # rectangle
         ciphertext += table[rowa * 5 + cola]
         ciphertext += table[rowa * 5 + cola]

   return ciphertext






def      _snake_case     (__lowercase     ,   __lowercase):
   UpperCamelCase_						=  generate_table(__lowercase)
   UpperCamelCase_						=  ''

   # https://en.wikipedia.org/wiki/Playfair_cipher#Description
   for chara, chara in chunker(__lowercase     ,   2):
      UpperCamelCase_		,   UpperCamelCase_						=  divmod(table.index(__lowercase)     ,   5)
      UpperCamelCase_		,   UpperCamelCase_						=  divmod(table.index(__lowercase)     ,   5)

      if rowa == rowa:
         plaintext += table[rowa * 5 + (cola - 1) % 5]
         plaintext += table[rowa * 5 + (cola - 1) % 5]
      elif cola == cola:
         plaintext += table[((rowa - 1) % 5) * 5 + cola]
         plaintext += table[((rowa - 1) % 5) * 5 + cola]
      else:  # rectangle
         plaintext += table[rowa * 5 + cola]
         plaintext += table[rowa * 5 + cola]

   return plaintext








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 				__UpperCamelCase			(      self				:			Dict						)				->							Tuple:

				"""simple docstring"""

				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							sd_pipe([prompt]   ,   generator=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							output.images

				SCREAMING_SNAKE_CASE :   Any					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   Union[str, 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 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_euler"						)

				SCREAMING_SNAKE_CASE :   List[str]					  =							"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=a   ,   guidance_scale=9.0   ,   num_inference_steps=20   ,   output_type="np"						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							output.images

				SCREAMING_SNAKE_CASE :   Tuple					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							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 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							sd_pipe.to(a						)
				sd_pipe.set_progress_bar_config(disable=a						)

				sd_pipe.set_scheduler("sample_dpmpp_2m"						)

				SCREAMING_SNAKE_CASE :   str					  =							"A painting of a squirrel eating a burger"
				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   str					  =							sd_pipe(
				    [prompt]   ,   generator=a   ,   guidance_scale=7.5   ,   num_inference_steps=15   ,   output_type="np"   ,   use_karras_sigmas=a   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							output.images

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				SCREAMING_SNAKE_CASE :   int					  =							np.array(
				    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]						)

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