



"""simple docstring"""

def  __snake_case							(	__A      :						str							,		__A      :						str     )     ->       str:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							int							=     len(__A     )
				SCREAMING_SNAKE_CASE						:							int							=     len(__A     )
				SCREAMING_SNAKE_CASE						:							int							=     (
				    first_str_length if first_str_length > second_str_length else second_str_length
				)
				SCREAMING_SNAKE_CASE						:							list							=     []
				for char_count in range(__A     ):
								if char_count < first_str_length:
												output_list.append(first_str[char_count]     )
								if char_count < second_str_length:
												output_list.append(second_str[char_count]     )
				return "".join(__A     )


if __name__ == "__main__":
							print(alternative_string_arrange('AB', 'XYZ'), end=' ')










"""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     lowerCAmelCase__						(	unittest.TestCase ):


	'''simple docstring'''







	def 			_lowerCAmelCase     (      self				:       List[Any]    )						->	str:





					"""simple docstring"""


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





	def 			_lowerCAmelCase     (      self				:       Any    )						->	str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							int							=     StableDiffusionKDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'    )
					SCREAMING_SNAKE_CASE						:							Dict							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

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

					SCREAMING_SNAKE_CASE						:							str							=     output.images

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

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





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_euler'    )

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

					SCREAMING_SNAKE_CASE						:							Optional[int]							=     output.images

					SCREAMING_SNAKE_CASE						:							List[str]							=     image[0, -3:, -3:, -1]

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							List[Any]							=     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 			_lowerCAmelCase     (      self				:       Dict    )						->	Optional[Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Optional[int]							=     StableDiffusionKDiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-1-base'    )
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     sd_pipe.to(_SCREAMING_SNAKE_CASE    )
					sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE    )

					sd_pipe.set_scheduler('sample_dpmpp_2m'    )

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

					SCREAMING_SNAKE_CASE						:							int							=     output.images

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

					assert image.shape == (1, 512, 512, 3)
					SCREAMING_SNAKE_CASE						:							int							=     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





