

"""simple docstring"""





import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class      __a (   snake_case__,     unittest.TestCase     ):

			"""simple docstring"""

			SCREAMING_SNAKE_CASE_      =    RePaintPipeline
			SCREAMING_SNAKE_CASE_      =    IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
			SCREAMING_SNAKE_CASE_      =    PipelineTesterMixin.required_optional_params - {
			    'latents',
			    'num_images_per_prompt',
			    'callback',
			    'callback_steps',
			}
			SCREAMING_SNAKE_CASE_      =    IMAGE_INPAINTING_BATCH_PARAMS
			SCREAMING_SNAKE_CASE_      =    False






			def  _lowerCAmelCase      (				self  :       Optional[int]    ):
								torch.manual_seed(0    )
								torch.manual_seed(0    )
								UpperCamelCase__		:     Tuple								=UNetaDModel(
								    block_out_channels=(32, 64)    ,	layers_per_block=2    ,	sample_size=32    ,	in_channels=3    ,	out_channels=3    ,	down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')    ,	up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')    ,	)
								UpperCamelCase__		:     str								=RePaintScheduler()
								UpperCamelCase__		:     Optional[Any]								={'''unet''': unet, '''scheduler''': scheduler}
								return components






			def  _lowerCAmelCase      (				self  :       str    ,	lowercase_  :       Tuple    ,	lowercase_  :       Union[str, Any]=0    ):
								if str(lowercase_    ).startswith('''mps'''    ):
													UpperCamelCase__		:     Optional[int]								=torch.manual_seed(lowercase_    )
								else:
													UpperCamelCase__		:     Tuple								=torch.Generator(device=lowercase_    ).manual_seed(lowercase_    )
								UpperCamelCase__		:     int								=np.random.RandomState(lowercase_    ).standard_normal((1, 3, 32, 32)    )
								UpperCamelCase__		:     str								=torch.from_numpy(lowercase_    ).to(device=lowercase_    ,	dtype=torch.floataa    )
								UpperCamelCase__		:     str								=(image > 0).to(device=lowercase_    ,	dtype=torch.floataa    )
								UpperCamelCase__		:     List[str]								={
								    '''image''': image,
								    '''mask_image''': mask,
								    '''generator''': generator,
								    '''num_inference_steps''': 5,
								    '''eta''': 0.0,
								    '''jump_length''': 2,
								    '''jump_n_sample''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs






			def  _lowerCAmelCase      (				self  :       List[Any]    ):
								UpperCamelCase__		:     str								='''cpu'''  # ensure determinism for the device-dependent torch.Generator
								UpperCamelCase__		:     Optional[Any]								=self.get_dummy_components()
								UpperCamelCase__		:     Tuple								=RePaintPipeline(**lowercase_    )
								UpperCamelCase__		:     Optional[Any]								=sd_pipe.to(lowercase_    )
								sd_pipe.set_progress_bar_config(disable=lowercase_    )

								UpperCamelCase__		:     Union[str, Any]								=self.get_dummy_inputs(lowercase_    )
								UpperCamelCase__		:     List[str]								=sd_pipe(**lowercase_    ).images
								UpperCamelCase__		:     Optional[int]								=image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								UpperCamelCase__		:     List[str]								=np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]    )

								assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-3






			@skip_mps
			def  _lowerCAmelCase      (				self  :       Any    ):
								return super().test_save_load_local()






			@unittest.skip('''non-deterministic pipeline'''    )
			def  _lowerCAmelCase      (				self  :       str    ):
								return super().test_inference_batch_single_identical()






			@skip_mps
			def  _lowerCAmelCase      (				self  :       Dict    ):
								return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def  _lowerCAmelCase      (				self  :       List[str]    ):
								return super().test_save_load_optional_components()






			@skip_mps
			def  _lowerCAmelCase      (				self  :       Optional[int]    ):
								return super().test_attention_slicing_forward_pass()



@nightly
@require_torch_gpu
class      __a (   unittest.TestCase     ):

			"""simple docstring"""

			def  _lowerCAmelCase      (				self  :       List[Any]    ):
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






			def  _lowerCAmelCase      (				self  :       Union[str, Any]    ):
								UpperCamelCase__		:     Optional[int]								=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
								    '''repaint/celeba_hq_256.png'''    )
								UpperCamelCase__		:     List[str]								=load_image(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''    )
								UpperCamelCase__		:     Optional[int]								=load_numpy(
								    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
								    '''repaint/celeba_hq_256_result.npy'''    )

								UpperCamelCase__		:     Union[str, Any]								='''google/ddpm-ema-celebahq-256'''
								UpperCamelCase__		:     Any								=UNetaDModel.from_pretrained(lowercase_    )
								UpperCamelCase__		:     List[Any]								=RePaintScheduler.from_pretrained(lowercase_    )

								UpperCamelCase__		:     Any								=RePaintPipeline(unet=lowercase_    ,	scheduler=lowercase_    ).to(lowercase_    )
								repaint.set_progress_bar_config(disable=lowercase_    )
								repaint.enable_attention_slicing()

								UpperCamelCase__		:     Optional[Any]								=torch.manual_seed(0    )
								UpperCamelCase__		:     Optional[int]								=repaint(
								    lowercase_    ,	lowercase_    ,	num_inference_steps=250    ,	eta=0.0    ,	jump_length=10    ,	jump_n_sample=10    ,	generator=lowercase_    ,	output_type='''np'''    ,	)
								UpperCamelCase__		:     Any								=output.images[0]

								assert image.shape == (256, 256, 3)
								assert np.abs(expected_image - image    ).mean() < 1e-2





"""simple docstring"""





def 							_lowerCAmelCase   (    UpperCAmelCase  :				int					):


					'''simple docstring'''






					if not isinstance(UpperCAmelCase   ,				UpperCAmelCase					):
										raise ValueError('''double_factorial() only accepts integral values'''					)
					if n < 0:
										raise ValueError('''double_factorial() not defined for negative values'''					)
					return 1 if n <= 1 else n * double_factorial(n - 2					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()


