

import unittest

import numpy as np
import torch

from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()






class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







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

				"""simple docstring"""

				torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   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")   ,   )
				return model






			def 				__UpperCamelCase			(      self				:			Union[str, Any]						)				->							Dict:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							self.dummy_uncond_unet
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Any					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   List[Any]					  =							pipe(num_inference_steps=2   ,   generator=a   ,   output_type="numpy"   ,   return_dict=a						)[0]

				SCREAMING_SNAKE_CASE :   List[Any]					  =							image[0, -3:, -3:, -1]
				SCREAMING_SNAKE_CASE :   Any					  =							image_from_tuple[0, -3:, -3:, -1]

				assert image.shape == (1, 32, 32, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]						)

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






@slow
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			def 				__UpperCamelCase			(      self				:			int						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"google/ncsnpp-celebahq-256"
				SCREAMING_SNAKE_CASE :   List[Any]					  =							UNetaDModel.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							KarrasVeScheduler()

				SCREAMING_SNAKE_CASE :   Optional[Any]					  =							KarrasVePipeline(unet=a   ,   scheduler=a						)
				pipe.to(a						)
				pipe.set_progress_bar_config(disable=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							torch.manual_seed(0						)
				SCREAMING_SNAKE_CASE :   Any					  =							pipe(num_inference_steps=20   ,   generator=a   ,   output_type="numpy"						).images

				SCREAMING_SNAKE_CASE :   List[str]					  =							image[0, -3:, -3:, -1]
				assert image.shape == (1, 256, 256, 3)
				SCREAMING_SNAKE_CASE :   str					  =							np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586]						)

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


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a_      =     {
    'configuration_swinv2': ['SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Swinv2Config'],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_      =     [
			    'SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'Swinv2ForImageClassification',
			    'Swinv2ForMaskedImageModeling',
			    'Swinv2Model',
			    'Swinv2PreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

			a_      =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)