


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				(							_lowercase	,		unittest.TestCase):
	_a    :    str        =				RePaintPipeline
	_a    :    int        =				IMAGE_INPAINTING_PARAMS - {'''width''', '''height''', '''guidance_scale'''}
	_a    :    int        =				PipelineTesterMixin.required_optional_params - {
	    '''latents''',
	    '''num_images_per_prompt''',
	    '''callback''',
	    '''callback_steps''',
	}
	_a    :    Optional[int]        =				IMAGE_INPAINTING_BATCH_PARAMS
	_a    :    List[str]        =				False




	def 				UpperCAmelCase__(   self		:						str						)->			Dict:
			torch.manual_seed(0						)
			torch.manual_seed(0						)
			lowerCAmelCase__  :     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''')						,   )
			lowerCAmelCase__  :     str       	=   RePaintScheduler()
			lowerCAmelCase__  :     Union[str, Any]       	=   {'''unet''': unet, '''scheduler''': scheduler}
			return components




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

			lowerCAmelCase__  :     Union[str, Any]       	=   self.get_dummy_inputs(_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     str       	=   sd_pipe(**_SCREAMING_SNAKE_CASE						).images
			lowerCAmelCase__  :     List[str]       	=   image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__  :     Optional[Any]       	=   np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]						)

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




	@skip_mps
	def 				UpperCAmelCase__(   self		:						Any						)->			Tuple:
			return super().test_save_load_local()




	@unittest.skip('''non-deterministic pipeline'''						)
	def 				UpperCAmelCase__(   self		:						List[Any]						)->			Dict:
			return super().test_inference_batch_single_identical()




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




	@skip_mps
	def 				UpperCAmelCase__(   self		:						Union[str, Any]						)->			str:
			return super().test_save_load_optional_components()




	@skip_mps
	def 				UpperCAmelCase__(   self		:						Optional[Any]						)->			Union[str, Any]:
			return super().test_attention_slicing_forward_pass()





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




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




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

			lowerCAmelCase__  :     List[Any]       	=   '''google/ddpm-ema-celebahq-256'''
			lowerCAmelCase__  :     Dict       	=   UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
			lowerCAmelCase__  :     Optional[int]       	=   RePaintScheduler.from_pretrained(_SCREAMING_SNAKE_CASE						)

			lowerCAmelCase__  :     Union[str, Any]       	=   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()

			lowerCAmelCase__  :     Union[str, Any]       	=   torch.manual_seed(0						)
			lowerCAmelCase__  :     List[Any]       	=   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'''						,   )
			lowerCAmelCase__  :     Dict       	=   output.images[0]

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








from itertools import combinations_with_replacement






def 	lowerCamelCase_       (			_a = 100			):




		"""simple docstring"""






		lowerCAmelCase__  :     list[int]       	=   [*list(range(1      ,  21			)			), 25]
		lowerCAmelCase__  :     list[int]       	=   [2 * x for x in range(1      ,  21			)] + [50]
		lowerCAmelCase__  :     list[int]       	=   [3 * x for x in range(1      ,  21			)]
		lowerCAmelCase__  :     list[int]       	=   singles + doubles + triples + [0]

		lowerCAmelCase__  :     int       	=   0
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int
		lowerCAmelCase__  :     int

		for double in doubles:
				for throwa, throwa in combinations_with_replacement(_a      ,  2			):
						lowerCAmelCase__  :     Dict       	=   double + throwa + throwa
						if checkout_total < limit:
								num_checkouts += 1

		return num_checkouts


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




