
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 							lowercase   (						SCREAMING_SNAKE_CASE_    ,  unittest.TestCase  ):
		__lowercase			:			Optional[int]      	=							RePaintPipeline
		__lowercase			:			List[Any]      	=							IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
		__lowercase			:			List[Any]      	=							PipelineTesterMixin.required_optional_params - {
		    "latents",
		    "num_images_per_prompt",
		    "callback",
		    "callback_steps",
		}
		__lowercase			:			Optional[Any]      	=							IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase			:			Dict      	=							False



		def __UpperCamelCase					(					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 __UpperCamelCase					(					self							, A_							, A_=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 __UpperCamelCase					(					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([1.0000, 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			)    ->   Optional[Any]:
				"""simple docstring"""
				return super().test_save_load_local()



		@unittest.skip('non-deterministic pipeline'			)
		def __UpperCamelCase					(					self			)    ->   int:
				"""simple docstring"""
				return super().test_inference_batch_single_identical()



		@skip_mps
		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				return super().test_dict_tuple_outputs_equivalent()



		@skip_mps
		def __UpperCamelCase					(					self			)    ->   List[str]:
				"""simple docstring"""
				return super().test_save_load_optional_components()



		@skip_mps
		def __UpperCamelCase					(					self			)    ->   Tuple:
				"""simple docstring"""
				return super().test_attention_slicing_forward_pass()






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



		def __UpperCamelCase					(					self			)    ->   Optional[Any]:
				"""simple docstring"""
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase					(					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


def 						A_			(				snake_case				: Union[str, Any]			,   snake_case				: Dict			)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      (boundary[1] - boundary[0]) / steps
						__UpperCamelCase  	=      boundary[0]
						__UpperCamelCase  	=      boundary[1]
						__UpperCamelCase  	=      make_points(snake_case			,   snake_case			,   snake_case			)
						__UpperCamelCase  	=      0.0
						y += (h / 3.0) * f(snake_case			)
						__UpperCamelCase  	=      2
						for i in x_i:
												y += (h / 3) * (4 - 2 * (cnt % 2)) * f(snake_case			)
												cnt += 1
						y += (h / 3.0) * f(snake_case			)
						return y




def 						A_			(				snake_case				: str			,   snake_case				: Optional[int]			,   snake_case				: Any			)   ->     Optional[int]:



						'''simple docstring'''

						__UpperCamelCase  	=      a + h
						while x < (b - h):
												yield x
												__UpperCamelCase  	=      x + h




def 						A_			(				snake_case				: Union[str, Any]			)   ->     str:  # enter your function here



						'''simple docstring'''

						__UpperCamelCase  	=      (x - 0) * (x - 0)
						return y




def 						A_			(				)   ->     Dict:



						'''simple docstring'''

						__UpperCamelCase  	=      0.0  # Lower bound of integration
						__UpperCamelCase  	=      1.0  # Upper bound of integration
						__UpperCamelCase  	=      10.0  # define number of steps or resolution
						__UpperCamelCase  	=      [a, b]  # define boundary of integration
						__UpperCamelCase  	=      method_a(snake_case			,   snake_case			)
						print(f"y = {y}"			)


if __name__ == "__main__":
				main()




