


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        a	(unittest.TestCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Any       )			->					Any:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()

							def 					__snake_case      (							self :						Optional[Any]       )			->					List[str]:
														__snake_case	:    Union[str, Any]											=     StableDiffusionKDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4"       )
														__snake_case	:    int											=     sd_pipe.to(lowerCamelCase       )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														sd_pipe.set_scheduler("sample_euler"       )

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

														__snake_case	:    str											=     output.images

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

														assert image.shape == (1, 512, 512, 3)
														__snake_case	:    Dict											=     np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39]       )

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

							def 					__snake_case      (							self :						str       )			->					Optional[Any]:
														__snake_case	:    int											=     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"       )
														__snake_case	:    Optional[Any]											=     sd_pipe.to(lowerCamelCase       )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														sd_pipe.set_scheduler("sample_euler"       )

														__snake_case	:    Tuple											=     "A painting of a squirrel eating a burger"
														__snake_case	:    Tuple											=     torch.manual_seed(0       )
														__snake_case	:    Tuple											=     sd_pipe([prompt]					,			generator=lowerCamelCase					,			guidance_scale=9.0					,			num_inference_steps=20					,			output_type="np"       )

														__snake_case	:    Dict											=     output.images

														__snake_case	:    Optional[Any]											=     image[0, -3:, -3:, -1]

														assert image.shape == (1, 512, 512, 3)
														__snake_case	:    List[str]											=     np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12]       )

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






							def 					__snake_case      (							self :						Dict       )			->					str:
														__snake_case	:    List[Any]											=     StableDiffusionKDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base"       )
														__snake_case	:    Union[str, Any]											=     sd_pipe.to(lowerCamelCase       )
														sd_pipe.set_progress_bar_config(disable=lowerCamelCase       )

														sd_pipe.set_scheduler("sample_dpmpp_2m"       )

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

														__snake_case	:    List[Any]											=     output.images

														__snake_case	:    str											=     image[0, -3:, -3:, -1]

														assert image.shape == (1, 512, 512, 3)
														__snake_case	:    Optional[Any]											=     np.array(
														    [0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48]       )

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





import warnings

from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor


_snake_case  :	List[str]			      =       logging.get_logger(__name__)

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Any					,			**lowerCamelCase :						Union[str, Any]       )			->					None:
														warnings.warn(
														    "The class PoolFormerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
														    " Please use PoolFormerImageProcessor instead."					,			lowerCamelCase					,			)
														super().__init__(*lowerCamelCase					,			**lowerCamelCase       )

