
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 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






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



							def      A__		(	self      )-> str:





													'''simple docstring'''






													torch.manual_seed(0      )
													torch.manual_seed(0      )
													__UpperCamelCase  	=      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  	=      RePaintScheduler()
													__UpperCamelCase  	=      {'''unet''': unet, '''scheduler''': scheduler}
													return components



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=0      )-> Dict:





													'''simple docstring'''






													if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																			__UpperCamelCase  	=      torch.manual_seed(SCREAMING_SNAKE_CASE_      )
													else:
																			__UpperCamelCase  	=      torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np.random.RandomState(SCREAMING_SNAKE_CASE_      ).standard_normal((1, 3, 32, 32)      )
													__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													__UpperCamelCase  	=      (image > 0).to(device=SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													__UpperCamelCase  	=      {
													    '''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      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase  	=      self.get_dummy_components()
													__UpperCamelCase  	=      RePaintPipeline(**SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      sd_pipe.to(SCREAMING_SNAKE_CASE_      )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      sd_pipe(**SCREAMING_SNAKE_CASE_      ).images
													__UpperCamelCase  	=      image[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													__UpperCamelCase  	=      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      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													return super().test_save_load_local()



							@unittest.skip('''non-deterministic pipeline'''      )
							def      A__		(	self      )-> int:





													'''simple docstring'''






													return super().test_inference_batch_single_identical()



							@skip_mps
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													return super().test_dict_tuple_outputs_equivalent()



							@skip_mps
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													return super().test_save_load_optional_components()



							@skip_mps
							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													return super().test_attention_slicing_forward_pass()






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

							"""simple docstring"""






							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






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



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
													    '''repaint/celeba_hq_256.png'''      )
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''      )
													__UpperCamelCase  	=      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
													    '''repaint/celeba_hq_256_result.npy'''      )

													__UpperCamelCase  	=      '''google/ddpm-ema-celebahq-256'''
													__UpperCamelCase  	=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      RePaintPipeline(unet=SCREAMING_SNAKE_CASE_  ,						scheduler=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
													repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
													repaint.enable_attention_slicing()

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

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












"""simple docstring"""







from itertools import combinations, product





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					if xa == ya == 0 or xa == ya == 0:
										return False
					a__ 							=	xa * xa + ya * ya
					a__ 							=	xa * xa + ya * ya
					a__ 							=	(xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
					return (
					    a_square + b_square == c_square
					    or a_square + c_square == b_square
					    or b_square + c_square == a_square
					)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_ = 50):
					return sum(
					    1
					    for pta, pta in combinations(product(range(limit + 1)			,  repeat=2)			,  2)
					    if is_right(*lowerCamelCase_			,  *lowerCamelCase_))


if __name__ == "__main__":
	print(F'''{solution() = }''')


