






"""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       snake_case_       (    _UpperCamelCase    ,							unittest.TestCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					DDIMPipeline
	SCREAMING_SNAKE_CASE_:							Optional[int]   			=					UNCONDITIONAL_IMAGE_GENERATION_PARAMS
	SCREAMING_SNAKE_CASE_:							List[str]   			=					PipelineTesterMixin.required_optional_params - {
	    "num_images_per_prompt",
	    "latents",
	    "callback",
	    "callback_steps",
	}
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					False


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					torch.manual_seed(0						)
					A__							 =       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')  ,					)
					A__							 =       DDIMScheduler()
					A__							 =       {"unet": unet, "scheduler": scheduler}
					return components


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a=0						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       "cpu"

					A__							 =       self.get_dummy_components()
					A__							 =       self.pipeline_class(**__a						)
					pipe.to(__a						)
					pipe.set_progress_bar_config(disable=__a						)

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

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


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().test_save_load_local(expected_max_difference=3E-3						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().test_save_load_optional_components(expected_max_difference=3E-3						)






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					super().test_inference_batch_single_identical(expected_max_diff=3E-3						)





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



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       "google/ddpm-cifar10-32"

					A__							 =       UNetaDModel.from_pretrained(__a						)
					A__							 =       DDIMScheduler()

					A__							 =       DDIMPipeline(unet=__a  ,					scheduler=__a						)
					ddim.to(__a						)
					ddim.set_progress_bar_config(disable=__a						)

					A__							 =       torch.manual_seed(0						)
					A__							 =       ddim(generator=__a  ,					eta=0.0  ,					output_type='numpy'						).images

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

					assert image.shape == (1, 32, 32, 3)
					A__							 =       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       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       "google/ddpm-ema-bedroom-256"

					A__							 =       UNetaDModel.from_pretrained(__a						)
					A__							 =       DDIMScheduler.from_pretrained(__a						)

					A__							 =       DDIMPipeline(unet=__a  ,					scheduler=__a						)
					ddpm.to(__a						)
					ddpm.set_progress_bar_config(disable=__a						)

					A__							 =       torch.manual_seed(0						)
					A__							 =       ddpm(generator=__a  ,					output_type='numpy'						).images

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

					assert image.shape == (1, 256, 256, 3)
					A__							 =       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"""






from math import ceil



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(0       ,lowerCAmelCase__				)				)

				A__							 =       [item for sublist in list(device_map.values()				) for item in sublist]

				# Duplicate check
				A__							 =       []
				for i in device_map_blocks:
								if device_map_blocks.count(lowerCAmelCase__				) > 1 and i not in duplicate_blocks:
												duplicate_blocks.append(lowerCAmelCase__				)
    # Missing blocks
				A__							 =       [i for i in blocks if i not in device_map_blocks]
				A__							 =       [i for i in device_map_blocks if i not in blocks]

				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device.'
								    ' These attention blocks were specified more than once: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'There are attention blocks for this model that are not specified in the device_map. Add these attention '
								    'blocks to a device on the device_map: ' + str(lowerCAmelCase__				)				)
				if len(lowerCAmelCase__				) != 0:
								raise ValueError(
								    'The device_map contains more attention blocks than this model has. Remove these from the device_map:'
								    + str(lowerCAmelCase__				)				)







def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       list(range(lowerCAmelCase__				)				)
				A__							 =       int(ceil(n_layers / len(lowerCAmelCase__				)				)				)
				A__							 =       [layers[i : i + n_blocks] for i in range(0       ,lowerCAmelCase__       ,lowerCAmelCase__				)]

				return dict(zip(lowerCAmelCase__       ,lowerCAmelCase__				)				)


