


"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()


class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							@property
							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								torch.manual_seed(0)
								snake_case__  :						Tuple		=      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")	,					)
								return model




							def        UpperCAmelCase (						self)   ->	int:







								'''simple docstring'''




								snake_case__  :						int		=      "cpu"
								snake_case__  :						Tuple		=      self.dummy_uncond_unet
								snake_case__  :						str		=      DDPMScheduler()

								snake_case__  :						List[Any]		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Optional[int]		=      torch.Generator(device=lowerCamelCase__).manual_seed(0)
								snake_case__  :						Any		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy").images

								snake_case__  :						Dict		=      torch.Generator(device=lowerCamelCase__).manual_seed(0)
								snake_case__  :						Tuple		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy"	,					return_dict=lowerCamelCase__)[0]

								snake_case__  :						Optional[Any]		=      image[0, -3:, -3:, -1]
								snake_case__  :						int		=      image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						Union[str, Any]		=      np.array(
								    [9.9_56E-01, 5.7_85E-01, 4.6_75E-01, 9.9_30E-01, 0.0, 1.0_00, 1.1_99E-03, 2.6_48E-04, 5.1_01E-04])

								assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2







							def        UpperCAmelCase (						self)   ->	List[Any]:







								'''simple docstring'''




								snake_case__  :						List[str]		=      self.dummy_uncond_unet
								snake_case__  :						str		=      DDPMScheduler(prediction_type="sample")

								snake_case__  :						Any		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      torch.manual_seed(0)
								snake_case__  :						Tuple		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy").images

								snake_case__  :						Dict		=      torch.manual_seed(0)
								snake_case__  :						str		=      ddpm(generator=lowerCamelCase__	,					num_inference_steps=2	,					output_type="numpy")[0]

								snake_case__  :						Any		=      image[0, -3:, -3:, -1]
								snake_case__  :						Dict		=      image_eps[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						str		=      1E-2 if torch_device != "mps" else 3E-2
								assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance


@slow
@require_torch_gpu
class 			SCREAMING_SNAKE_CASE_  (   unittest.TestCase):




							'''simple docstring'''





							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Any		=      "google/ddpm-cifar10-32"

								snake_case__  :						Any		=      UNetaDModel.from_pretrained(lowerCamelCase__)
								snake_case__  :						Union[str, Any]		=      DDPMScheduler.from_pretrained(lowerCamelCase__)

								snake_case__  :						Union[str, Any]		=      DDPMPipeline(unet=lowerCamelCase__	,					scheduler=lowerCamelCase__)
								ddpm.to(lowerCamelCase__)
								ddpm.set_progress_bar_config(disable=lowerCamelCase__)

								snake_case__  :						Tuple		=      torch.manual_seed(0)
								snake_case__  :						Union[str, Any]		=      ddpm(generator=lowerCamelCase__	,					output_type="numpy").images

								snake_case__  :						int		=      image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								snake_case__  :						Union[str, Any]		=      np.array([0.42_00, 0.35_88, 0.19_39, 0.38_47, 0.33_82, 0.26_47, 0.41_55, 0.35_82, 0.33_85])
								assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2









"""simple docstring"""






def 			A__		(					_UpperCAmelCase    :      str	)							->	list:

	'''simple docstring'''


	snake_case__  :						Any		=      []
	snake_case__  :						List[Any]		=      1

	for i in range(len(_UpperCAmelCase	)	):
		if i + 1 < len(_UpperCAmelCase	) and text[i] == text[i + 1]:
			count += 1
		else:
			encoded.append((text[i], count)	)
			snake_case__  :						Dict		=      1

	return encoded



def 			A__		(					_UpperCAmelCase    :      list	)							->	str:

	'''simple docstring'''


	return "".join(char * length for char, length in encoded	)


if __name__ == "__main__":
					from doctest import testmod

					testmod(name="""run_length_encode""", verbose=True)
					testmod(name="""run_length_decode""", verbose=True)





