






def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	str:
     if any(not isinstance(__lowerCamelCase					,    __lowerCamelCase			) or x < 0 for x in sequence			):
          raise TypeError("Sequence must be list of non-negative integers"			)
     for _ in range(len(__lowerCamelCase			)			):
          for i, (rod_upper, rod_lower) in enumerate(zip(__lowerCamelCase					,    sequence[1:]			)			):
               if rod_upper > rod_lower:
                    sequence[i] -= rod_upper - rod_lower
                    sequence[i + 1] += rod_upper - rod_lower
     return sequence


if __name__ == "__main__":
 assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
 assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]




"""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        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_		,   unittest.TestCase   ):


					'''simple docstring'''





					_a   			=							DDIMPipeline
					_a   			=							UNCONDITIONAL_IMAGE_GENERATION_PARAMS
					_a   			=							PipelineTesterMixin.required_optional_params - {
					    'num_images_per_prompt',
					    'latents',
					    'callback',
					    'callback_steps',
					}
					_a   			=							UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
					_a   			=							False



					def 		snake_case       (       self							:  str     )->					Optional[Any]:
								torch.manual_seed(0     )
								lowerCamelCase__		:		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'''),  )
								lowerCamelCase__		:		Optional[Any]			=DDIMScheduler()
								lowerCamelCase__		:		List[Any]			={'''unet''': unet, '''scheduler''': scheduler}
								return components



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Optional[Any]=0     )->					Optional[int]:
								if str(lowerCamelCase     ).startswith('''mps'''     ):
											lowerCamelCase__		:		Dict			=torch.manual_seed(lowerCamelCase     )
								else:
											lowerCamelCase__		:		Optional[int]			=torch.Generator(device=lowerCamelCase     ).manual_seed(lowerCamelCase     )
								lowerCamelCase__		:		Tuple			={
								    '''batch_size''': 1,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''output_type''': '''numpy''',
								}
								return inputs



					def 		snake_case       (       self							:  Dict     )->					str:
								lowerCamelCase__		:		Optional[Any]			='''cpu'''

								lowerCamelCase__		:		int			=self.get_dummy_components()
								lowerCamelCase__		:		Optional[int]			=self.pipeline_class(**lowerCamelCase     )
								pipe.to(lowerCamelCase     )
								pipe.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		List[str]			=self.get_dummy_inputs(lowerCamelCase     )
								lowerCamelCase__		:		Any			=pipe(**lowerCamelCase     ).images
								lowerCamelCase__		:		Optional[Any]			=image[0, -3:, -3:, -1]

								self.assertEqual(image.shape,  (1, 32, 32, 3)     )
								lowerCamelCase__		:		Tuple			=np.array(
								    [1.000E00, 5.717E-01, 4.717E-01, 1.000E00, 0.000E00, 1.000E00, 3.000E-04, 0.000E00, 9.000E-04]     )
								lowerCamelCase__		:		str			=np.abs(image_slice.flatten() - expected_slice     ).max()
								self.assertLessEqual(lowerCamelCase,  1E-3     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					List[Any]:
								super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3     )



					def 		snake_case       (       self							:  Union[str, Any]     )->					int:
								super().test_save_load_local(expected_max_difference=3E-3     )



					def 		snake_case       (       self							:  List[Any]     )->					List[Any]:
								super().test_save_load_optional_components(expected_max_difference=3E-3     )




					def 		snake_case       (       self							:  Optional[Any]     )->					Optional[int]:
								super().test_inference_batch_single_identical(expected_max_diff=3E-3     )





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


					'''simple docstring'''





					def 		snake_case       (       self							:  Optional[Any]     )->					List[str]:
								lowerCamelCase__		:		Optional[Any]			='''google/ddpm-cifar10-32'''

								lowerCamelCase__		:		Union[str, Any]			=UNetaDModel.from_pretrained(lowerCamelCase     )
								lowerCamelCase__		:		Optional[int]			=DDIMScheduler()

								lowerCamelCase__		:		int			=DDIMPipeline(unet=lowerCamelCase,  scheduler=lowerCamelCase     )
								ddim.to(lowerCamelCase     )
								ddim.set_progress_bar_config(disable=lowerCamelCase     )

								lowerCamelCase__		:		Tuple			=torch.manual_seed(0     )
								lowerCamelCase__		:		int			=ddim(generator=lowerCamelCase,  eta=0.0,  output_type='''numpy'''     ).images

								lowerCamelCase__		:		Tuple			=image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowerCamelCase__		:		Any			=np.array([0.1_723, 0.1_617, 0.1_600, 0.1_626, 0.1_497, 0.1_513, 0.1_505, 0.1_442, 0.1_453]     )

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




					def 		snake_case       (       self							:  Optional[int]     )->					Any:
								lowerCamelCase__		:		str			='''google/ddpm-ema-bedroom-256'''

								lowerCamelCase__		:		Optional[int]			=UNetaDModel.from_pretrained(lowerCamelCase     )
								lowerCamelCase__		:		Any			=DDIMScheduler.from_pretrained(lowerCamelCase     )

								lowerCamelCase__		:		Optional[Any]			=DDIMPipeline(unet=lowerCamelCase,  scheduler=lowerCamelCase     )
								ddpm.to(lowerCamelCase     )
								ddpm.set_progress_bar_config(disable=lowerCamelCase     )

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

								lowerCamelCase__		:		Any			=image[0, -3:, -3:, -1]

								assert image.shape == (1, 256, 256, 3)
								lowerCamelCase__		:		Any			=np.array([0.0_060, 0.0_201, 0.0_344, 0.0_024, 0.0_018, 0.0_002, 0.0_022, 0.0_000, 0.0_069]     )

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




