




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  lowerCamelCase     (		lowerCamelCase__					,unittest.TestCase    ):






			'''simple docstring'''





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

			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> int:
								torch.manual_seed(0	)
								torch.manual_seed(0	)
								__magic_name__				:						Optional[int]     				=				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''')							,    )
								__magic_name__				:						int     				=				RePaintScheduler()
								__magic_name__				:						Optional[int]     				=				{'''unet''': unet, '''scheduler''': scheduler}
								return components

			def 				UpperCAmelCase__							(			self      : Dict							,    lowerCamelCase_      : List[str]							,    lowerCamelCase_      : int=0	)    -> Tuple:
								if str(__UpperCamelCase	).startswith('''mps'''	):
													__magic_name__				:						Optional[Any]     				=				torch.manual_seed(__UpperCamelCase	)
								else:
													__magic_name__				:						int     				=				torch.Generator(device=__UpperCamelCase	).manual_seed(__UpperCamelCase	)
								__magic_name__				:						Any     				=				np.random.RandomState(__UpperCamelCase	).standard_normal((1, 3, 32, 32)	)
								__magic_name__				:						List[str]     				=				torch.from_numpy(__UpperCamelCase	).to(device=__UpperCamelCase							,    dtype=torch.floataa	)
								__magic_name__				:						Tuple     				=				(image > 0).to(device=__UpperCamelCase							,    dtype=torch.floataa	)
								__magic_name__				:						Union[str, Any]     				=				{
								    '''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 				UpperCAmelCase__							(			self      : str	)    -> Optional[Any]:
								__magic_name__				:						Dict     				=				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								__magic_name__				:						Optional[int]     				=				self.get_dummy_components()
								__magic_name__				:						int     				=				RePaintPipeline(**__UpperCamelCase	)
								__magic_name__				:						Union[str, Any]     				=				sd_pipe.to(__UpperCamelCase	)
								sd_pipe.set_progress_bar_config(disable=__UpperCamelCase	)

								__magic_name__				:						Tuple     				=				self.get_dummy_inputs(__UpperCamelCase	)
								__magic_name__				:						List[str]     				=				sd_pipe(**__UpperCamelCase	).images
								__magic_name__				:						Dict     				=				image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								__magic_name__				:						Optional[int]     				=				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 				UpperCAmelCase__							(			self      : Any	)    -> Union[str, Any]:
								return super().test_save_load_local()

			@unittest.skip('''non-deterministic pipeline'''	)
			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> int:
								return super().test_inference_batch_single_identical()

			@skip_mps
			def 				UpperCAmelCase__							(			self      : Dict	)    -> str:
								return super().test_dict_tuple_outputs_equivalent()

			@skip_mps
			def 				UpperCAmelCase__							(			self      : Any	)    -> int:
								return super().test_save_load_optional_components()


			@skip_mps
			def 				UpperCAmelCase__							(			self      : Tuple	)    -> Optional[int]:
								return super().test_attention_slicing_forward_pass()






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






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : str	)    -> List[str]:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()


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

								__magic_name__				:						Tuple     				=				'''google/ddpm-ema-celebahq-256'''
								__magic_name__				:						Any     				=				UNetaDModel.from_pretrained(__UpperCamelCase	)
								__magic_name__				:						str     				=				RePaintScheduler.from_pretrained(__UpperCamelCase	)

								__magic_name__				:						Union[str, Any]     				=				RePaintPipeline(unet=__UpperCamelCase							,    scheduler=__UpperCamelCase	).to(__UpperCamelCase	)
								repaint.set_progress_bar_config(disable=__UpperCamelCase	)
								repaint.enable_attention_slicing()

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

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







'''simple docstring'''

def __UpperCamelCase (      __lowerCamelCase		:			int					)   ->  int:







     '''simple docstring'''






     if not isinstance(__lowerCamelCase   ,	__lowerCamelCase					) or number < 0:
          raise ValueError("Input must be a non-negative integer"					)

     _a            =		number & ~(number - 1)
     _a            =		0
     while intermediate:
          intermediate >>= 1
          index += 1
     return index - 1


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)

