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



				def    _lowercase	(self							: Optional[int])       ->      Tuple:
					torch.manual_seed(0)
					torch.manual_seed(0)
					__snake_case     :				Union[str, Any] 						=					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')      ,    )
					__snake_case     :				List[str] 						=					RePaintScheduler()
					__snake_case     :				str 						=					{'unet': unet, 'scheduler': scheduler}
					return components



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

					__snake_case     :				Dict 						=					self.get_dummy_inputs(_A)
					__snake_case     :				Optional[Any] 						=					sd_pipe(**_A).images
					__snake_case     :				List[Any] 						=					image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)
					__snake_case     :				Optional[int] 						=					np.array([1.0_000, 0.5_426, 0.5_497, 0.2_200, 1.0_000, 1.0_000, 0.5_623, 1.0_000, 0.6_274])

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



				@skip_mps
				def    _lowercase	(self							: Dict)       ->      str:
					return super().test_save_load_local()



				@unittest.skip('non-deterministic pipeline')
				def    _lowercase	(self							: Optional[Any])       ->      str:
					return super().test_inference_batch_single_identical()



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



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






				@skip_mps
				def    _lowercase	(self							: str)       ->      List[Any]:
					return super().test_attention_slicing_forward_pass()







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



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






				def    _lowercase	(self							: Optional[int])       ->      Any:
					__snake_case     :				Any 						=					load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
					    'repaint/celeba_hq_256.png')
					__snake_case     :				str 						=					load_image(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png')
					__snake_case     :				Dict 						=					load_numpy(
					    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
					    'repaint/celeba_hq_256_result.npy')

					__snake_case     :				int 						=					'google/ddpm-ema-celebahq-256'
					__snake_case     :				Optional[int] 						=					UNetaDModel.from_pretrained(_A)
					__snake_case     :				int 						=					RePaintScheduler.from_pretrained(_A)

					__snake_case     :				List[str] 						=					RePaintPipeline(unet=_A      ,    scheduler=_A).to(_A)
					repaint.set_progress_bar_config(disable=_A)
					repaint.enable_attention_slicing()

					__snake_case     :				Optional[Any] 						=					torch.manual_seed(0)
					__snake_case     :				List[str] 						=					repaint(
					    _A      ,    _A      ,    num_inference_steps=2_50      ,    eta=0.0      ,    jump_length=10      ,    jump_n_sample=10      ,    generator=_A      ,    output_type='np'      ,    )
					__snake_case     :				str 						=					output.images[0]

					assert image.shape == (2_56, 2_56, 3)
					assert np.abs(expected_image - image).mean() < 1E-2






"""simple docstring"""







import math
def     __UpperCAmelCase							(       UpperCAmelCase_       :							int    )				->     str:

	'''simple docstring'''





	if number == 0:
		__snake_case     :				Any 						=					'Zero'
		return words
	else:
		__snake_case     :				Optional[Any] 						=					math.logaa(UpperCAmelCase_    )
		__snake_case     :				Optional[Any] 						=					digits + 1
		__snake_case     :				List[str] 						=					{}
		__snake_case     :				List[str] 						=					''
		__snake_case     :				Any 						=					'One'
		__snake_case     :				List[str] 						=					'Two'
		__snake_case     :				Tuple 						=					'Three'
		__snake_case     :				str 						=					'Four'
		__snake_case     :				int 						=					'Five'
		__snake_case     :				Optional[Any] 						=					'Six'
		__snake_case     :				str 						=					'Seven'
		__snake_case     :				Optional[Any] 						=					'Eight'
		__snake_case     :				Tuple 						=					'Nine'

		__snake_case     :				Tuple 						=					{}
		__snake_case     :				Optional[int] 						=					''
		__snake_case     :				str 						=					'Twenty'
		__snake_case     :				List[str] 						=					'Thirty'
		__snake_case     :				str 						=					'Forty'
		__snake_case     :				Union[str, Any] 						=					'Fifty'
		__snake_case     :				str 						=					'Sixty'
		__snake_case     :				Tuple 						=					'Seventy'
		__snake_case     :				Dict 						=					'Eighty'
		__snake_case     :				Optional[Any] 						=					'Ninety'

		__snake_case     :				List[Any] 						=					{}
		__snake_case     :				Optional[Any] 						=					'Ten'
		__snake_case     :				List[str] 						=					'Eleven'
		__snake_case     :				Any 						=					'Twelve'
		__snake_case     :				Tuple 						=					'Thirteen'
		__snake_case     :				Tuple 						=					'Fourteen'
		__snake_case     :				Optional[int] 						=					'Fifteen'
		__snake_case     :				Any 						=					'Sixteen'
		__snake_case     :				int 						=					'Seventeen'
		__snake_case     :				Dict 						=					'Eighteen'
		__snake_case     :				List[Any] 						=					'Nineteen'

		__snake_case     :				List[Any] 						=					{}
		__snake_case     :				List[Any] 						=					'Hundred,'
		__snake_case     :				Union[str, Any] 						=					'Thousand,'
		__snake_case     :				List[Any] 						=					'Lakh,'
		__snake_case     :				List[Any] 						=					'Crore,'

		__snake_case     :				List[Any] 						=					number
		__snake_case     :				List[Any] 						=					''
		__snake_case     :				List[Any] 						=					0
		__snake_case     :				Union[str, Any] 						=					int(UpperCAmelCase_    )
		while counter < digits:
			__snake_case     :				List[str] 						=					temp_num % 10
			if counter % 2 == 0:
				__snake_case     :				Tuple 						=					''
				if counter in placevalue and current != 0:
					__snake_case     :				List[str] 						=					placevalue[counter]
				if counter == 2:
					__snake_case     :				Dict 						=					singles[current] + addition + words
				elif counter == 0:
					if ((temp_num % 1_00) // 10) == 1:
						__snake_case     :				Dict 						=					teens[current] + addition + words
						__snake_case     :				Dict 						=					temp_num // 10
						counter += 1
					else:
						__snake_case     :				Optional[Any] 						=					singles[current] + addition + words

				else:
					__snake_case     :				Any 						=					doubles[current] + addition + words

			else:
				if counter == 1:
					if current == 1:
						__snake_case     :				Dict 						=					teens[number % 10] + words
					else:
						__snake_case     :				Optional[int] 						=					''
						if counter in placevalue:
							__snake_case     :				Dict 						=					placevalue[counter]
						__snake_case     :				Union[str, Any] 						=					doubles[current] + addition + words
				else:
					__snake_case     :				List[Any] 						=					''
					if counter in placevalue:
						if current == 0 and ((temp_num % 1_00) // 10) == 0:
							__snake_case     :				Optional[Any] 						=					''
						else:
							__snake_case     :				Any 						=					placevalue[counter]
					if ((temp_num % 1_00) // 10) == 1:
						__snake_case     :				int 						=					teens[current] + addition + words
						__snake_case     :				Any 						=					temp_num // 10
						counter += 1
					else:
						__snake_case     :				List[str] 						=					singles[current] + addition + words
			counter += 1
			__snake_case     :				str 						=					temp_num // 10
	return words


if __name__ == "__main__":
	import doctest

	doctest.testmod()





