

'''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      lowercase_      (unittest.TestCase ):




  """simple docstring"""




  def    SCREAMING_SNAKE_CASE ( self  :	str     ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()






  def    SCREAMING_SNAKE_CASE ( self  :	Optional[int]     ):
   __lowercase				    =					StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''     )
   __lowercase				    =					sd_pipe.to(lowercase__     )
   sd_pipe.set_progress_bar_config(disable=lowercase__     )

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

   __lowercase				    =					'''A painting of a squirrel eating a burger'''
   __lowercase				    =					torch.manual_seed(0     )
   __lowercase				    =					sd_pipe([prompt]   ,generator=lowercase__   ,guidance_scale=9.0   ,num_inference_steps=2_0   ,output_type='''np'''     )

   __lowercase				    =					output.images

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

   assert image.shape == (1, 5_1_2, 5_1_2, 3)
   __lowercase				    =					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    SCREAMING_SNAKE_CASE ( self  :	Union[str, Any]     ):
   __lowercase				    =					StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
   __lowercase				    =					sd_pipe.to(lowercase__     )
   sd_pipe.set_progress_bar_config(disable=lowercase__     )

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

   __lowercase				    =					'''A painting of a squirrel eating a burger'''
   __lowercase				    =					torch.manual_seed(0     )
   __lowercase				    =					sd_pipe([prompt]   ,generator=lowercase__   ,guidance_scale=9.0   ,num_inference_steps=2_0   ,output_type='''np'''     )

   __lowercase				    =					output.images

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

   assert image.shape == (1, 5_1_2, 5_1_2, 3)
   __lowercase				    =					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    SCREAMING_SNAKE_CASE ( self  :	List[Any]     ):
   __lowercase				    =					StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''     )
   __lowercase				    =					sd_pipe.to(lowercase__     )
   sd_pipe.set_progress_bar_config(disable=lowercase__     )

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

   __lowercase				    =					'''A painting of a squirrel eating a burger'''
   __lowercase				    =					torch.manual_seed(0     )
   __lowercase				    =					sd_pipe(
       [prompt]   ,generator=lowercase__   ,guidance_scale=7.5   ,num_inference_steps=1_5   ,output_type='''np'''   ,use_karras_sigmas=lowercase__   ,)

   __lowercase				    =					output.images

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

   assert image.shape == (1, 5_1_2, 5_1_2, 3)
   __lowercase				    =					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



from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_						:     Optional[int]	       =							logging.get_logger(__name__)




class   __lowercase						(						__snake_case       ):
	_A       =   "timm_backbone"






	def __init__(self				: Any      ,	snake_case				: List[Any]=None      ,	snake_case				: int=3      ,	snake_case				: Dict=True      ,	snake_case				: Union[str, Any]=True      ,	snake_case				: Any=None      ,	**snake_case				: int      ,	) ->			Optional[int]:
							super().__init__(**snake_case    )
							_lowercase       :	Dict           =   backbone
							_lowercase       :	Optional[Any]           =   num_channels
							_lowercase       :	Union[str, Any]           =   features_only
							_lowercase       :	Tuple           =   use_pretrained_backbone
							_lowercase       :	List[str]           =   True
							_lowercase       :	Tuple           =   out_indices if out_indices is not None else (-1,)




