



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 				_snake_case			(					SCREAMING_SNAKE_CASE__    ,	unittest.TestCase):
				UpperCamelCase__				:					Optional[int]     		=DDIMPipeline
				UpperCamelCase__				:					Dict     		=UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				UpperCamelCase__				:					int     		=PipelineTesterMixin.required_optional_params - {
				    """num_images_per_prompt""",
				    """latents""",
				    """callback""",
				    """callback_steps""",
				}
				UpperCamelCase__				:					Optional[Any]     		=UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
				UpperCamelCase__				:					Tuple     		=False




				def 							A__      (						self					:						Optional[int]						):
							torch.manual_seed(0						)
							lowercase__					  =				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"),					)
							lowercase__					  =				DDIMScheduler()
							lowercase__					  =				{"""unet""": unet, """scheduler""": scheduler}
							return components




				def 							A__      (						self					:						str,					__lowercase					:						Any,					__lowercase					:						List[str]=0						):

							if str(_lowercase						).startswith("mps"						):
										lowercase__					  =				torch.manual_seed(_lowercase						)
							else:
										lowercase__					  =				torch.Generator(device=_lowercase						).manual_seed(_lowercase						)
							lowercase__					  =				{
							    """batch_size""": 1,
							    """generator""": generator,
							    """num_inference_steps""": 2,
							    """output_type""": """numpy""",
							}
							return inputs




				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				"""cpu"""

							lowercase__					  =				self.get_dummy_components()
							lowercase__					  =				self.pipeline_class(**_lowercase						)
							pipe.to(_lowercase						)
							pipe.set_progress_bar_config(disable=_lowercase						)

							lowercase__					  =				self.get_dummy_inputs(_lowercase						)
							lowercase__					  =				pipe(**_lowercase						).images
							lowercase__					  =				image[0, -3:, -3:, -1]

							self.assertEqual(image.shape,					(1, 32, 32, 3)						)
							lowercase__					  =				np.array(
							    [1.0_0_0e0_0, 5.7_1_7e-0_1, 4.7_1_7e-0_1, 1.0_0_0e0_0, 0.0_0_0e0_0, 1.0_0_0e0_0, 3.0_0_0e-0_4, 0.0_0_0e0_0, 9.0_0_0e-0_4]						)
							lowercase__					  =				np.abs(image_slice.flatten() - expected_slice						).max()
							self.assertLessEqual(_lowercase,					1e-3						)




				def 							A__      (						self					:						Optional[int]						):
							super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3						)




				def 							A__      (						self					:						Dict						):
							super().test_save_load_local(expected_max_difference=3e-3						)




				def 							A__      (						self					:						Union[str, Any]						):
							super().test_save_load_optional_components(expected_max_difference=3e-3						)




				def 							A__      (						self					:						Optional[int]						):
							super().test_inference_batch_single_identical(expected_max_diff=3e-3						)
@slow
@require_torch_gpu
class 				_snake_case			(					unittest.TestCase):




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				"""google/ddpm-cifar10-32"""

							lowercase__					  =				UNetaDModel.from_pretrained(_lowercase						)
							lowercase__					  =				DDIMScheduler()

							lowercase__					  =				DDIMPipeline(unet=_lowercase,					scheduler=_lowercase						)
							ddim.to(_lowercase						)
							ddim.set_progress_bar_config(disable=_lowercase						)

							lowercase__					  =				torch.manual_seed(0						)
							lowercase__					  =				ddim(generator=_lowercase,					eta=0.0,					output_type="numpy"						).images

							lowercase__					  =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							lowercase__					  =				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 							A__      (						self					:						Tuple						):
							lowercase__					  =				"""google/ddpm-ema-bedroom-256"""

							lowercase__					  =				UNetaDModel.from_pretrained(_lowercase						)
							lowercase__					  =				DDIMScheduler.from_pretrained(_lowercase						)

							lowercase__					  =				DDIMPipeline(unet=_lowercase,					scheduler=_lowercase						)
							ddpm.to(_lowercase						)
							ddpm.set_progress_bar_config(disable=_lowercase						)

							lowercase__					  =				torch.manual_seed(0						)
							lowercase__					  =				ddpm(generator=_lowercase,					output_type="numpy"						).images

							lowercase__					  =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 256, 256, 3)
							lowercase__					  =				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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				0
			for ch in input_str:
						lowercase__					  =				ord(SCREAMING_SNAKE_CASE_  )
						lowercase__					  =				pow(2						,  SCREAMING_SNAKE_CASE_  )

						# If we already turned on bit for current character's unicode
						if bitmap >> ch_unicode & 1 == 1:
									return False
						bitmap |= ch_bit_index_on
			return True


if __name__ == "__main__":
							import doctest

							doctest.testmod()




