"""simple docstring"""







import unittest

import numpy as np
import torch

from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class 			A__	(     _lowerCamelCase	,					unittest.TestCase):
		A_						:	Union[str, Any]           =     DDIMPipeline
		A_						:	Dict           =     UNCONDITIONAL_IMAGE_GENERATION_PARAMS
		A_						:	Dict           =     PipelineTesterMixin.required_optional_params - {
		    'num_images_per_prompt',
		    'latents',
		    'callback',
		    'callback_steps',
		}
		A_						:	Dict           =     UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
		A_						:	int           =     False

		def 							__lowerCamelCase							(				self			):
			torch.manual_seed(0			)
			__lowerCAmelCase      :						List[str]						      =				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      :						List[Any]						      =				DDIMScheduler()
			__lowerCAmelCase      :						int						      =				{'unet': unet, 'scheduler': scheduler}
			return components

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=0			):
			if str(_SCREAMING_SNAKE_CASE			).startswith('mps'			):
				__lowerCAmelCase      :						int						      =				torch.manual_seed(_SCREAMING_SNAKE_CASE			)
			else:
				__lowerCAmelCase      :						Tuple						      =				torch.Generator(device=_SCREAMING_SNAKE_CASE			).manual_seed(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Any						      =				{
			    'batch_size': 1,
			    'generator': generator,
			    'num_inference_steps': 2,
			    'output_type': 'numpy',
			}
			return inputs

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						List[Any]						      =				'cpu'

			__lowerCAmelCase      :						str						      =				self.get_dummy_components()
			__lowerCAmelCase      :						Any						      =				self.pipeline_class(**_SCREAMING_SNAKE_CASE			)
			pipe.to(_SCREAMING_SNAKE_CASE			)
			pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Any						      =				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						int						      =				pipe(**_SCREAMING_SNAKE_CASE			).images
			__lowerCAmelCase      :						Dict						      =				image[0, -3:, -3:, -1]

			self.assertEqual(image.shape	,		(1, 32, 32, 3)			)
			__lowerCAmelCase      :						Optional[Any]						      =				np.array(
			    [1.0_00E00, 5.7_17E-01, 4.7_17E-01, 1.0_00E00, 0.0_00E00, 1.0_00E00, 3.0_00E-04, 0.0_00E00, 9.0_00E-04]			)
			__lowerCAmelCase      :						List[Any]						      =				np.abs(image_slice.flatten() - expected_slice			).max()
			self.assertLessEqual(_SCREAMING_SNAKE_CASE	,		1E-3			)

		def 							__lowerCamelCase							(				self			):
			super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3			)

		def 							__lowerCamelCase							(				self			):
			super().test_save_load_local(expected_max_difference=3E-3			)

		def 							__lowerCamelCase							(				self			):
			super().test_save_load_optional_components(expected_max_difference=3E-3			)







		def 							__lowerCamelCase							(				self			):
			super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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

		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Union[str, Any]						      =				'google/ddpm-cifar10-32'

			__lowerCAmelCase      :						Union[str, Any]						      =				UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						str						      =				DDIMScheduler()

			__lowerCAmelCase      :						Dict						      =				DDIMPipeline(unet=_SCREAMING_SNAKE_CASE	,		scheduler=_SCREAMING_SNAKE_CASE			)
			ddim.to(_SCREAMING_SNAKE_CASE			)
			ddim.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Optional[int]						      =				torch.manual_seed(0			)
			__lowerCAmelCase      :						Any						      =				ddim(generator=_SCREAMING_SNAKE_CASE	,		eta=0.0	,		output_type='numpy'			).images

			__lowerCAmelCase      :						List[str]						      =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			__lowerCAmelCase      :						List[Any]						      =				np.array([0.1723, 0.1617, 0.1600, 0.1626, 0.1497, 0.1513, 0.1505, 0.1442, 0.1453]			)

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







		def 							__lowerCamelCase							(				self			):
			__lowerCAmelCase      :						Optional[int]						      =				'google/ddpm-ema-bedroom-256'

			__lowerCAmelCase      :						Optional[Any]						      =				UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE			)
			__lowerCAmelCase      :						Optional[Any]						      =				DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						Tuple						      =				DDIMPipeline(unet=_SCREAMING_SNAKE_CASE	,		scheduler=_SCREAMING_SNAKE_CASE			)
			ddpm.to(_SCREAMING_SNAKE_CASE			)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[str]						      =				torch.manual_seed(0			)
			__lowerCAmelCase      :						List[Any]						      =				ddpm(generator=_SCREAMING_SNAKE_CASE	,		output_type='numpy'			).images

			__lowerCAmelCase      :						Optional[int]						      =				image[0, -3:, -3:, -1]

			assert image.shape == (1, 2_56, 2_56, 3)
			__lowerCAmelCase      :						str						      =				np.array([0.0060, 0.0201, 0.0344, 0.0024, 0.0018, 0.0002, 0.0022, 0.0000, 0.0069]			)

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

"""simple docstring"""







class 			A__	:

		def __init__(				self	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						List[Any]						      =				size
			__lowerCAmelCase      :						str						      =				[0] * size
			__lowerCAmelCase      :						Any						      =				[0] * size

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return index | (index + 1)

		@staticmethod
		def 							__lowerCamelCase							(				_SCREAMING_SNAKE_CASE			):
			return (index & (index + 1)) - 1

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			__lowerCAmelCase      :						Any						      =				value
			while index < self.size:
				__lowerCAmelCase      :						Dict						      =				self.get_prev(_SCREAMING_SNAKE_CASE			) + 1
				if current_left_border == index:
					__lowerCAmelCase      :						Any						      =				value
				else:
					__lowerCAmelCase      :						Any						      =				max(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[Any]						      =				self.get_next(_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			):
			right -= 1  # Because of right is exclusive
			__lowerCAmelCase      :						Optional[int]						      =				0
			while left <= right:
				__lowerCAmelCase      :						Optional[int]						      =				self.get_prev(_SCREAMING_SNAKE_CASE			)
				if left <= current_left:
					__lowerCAmelCase      :						Optional[Any]						      =				max(_SCREAMING_SNAKE_CASE	,		self.tree[right]			)
					__lowerCAmelCase      :						Optional[Any]						      =				current_left
				else:
					__lowerCAmelCase      :						List[str]						      =				max(_SCREAMING_SNAKE_CASE	,		self.arr[right]			)
					right -= 1
			return result


if __name__ == "__main__":
							import doctest

							doctest.testmod()