





"""simple docstring"""




import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      List[str]         =VQModel
							lowerCamelCase     :      List[str]         ="""sample"""






							@property
							def 					__a     (     self   ,      lowerCAmelCase__=(32, 32)   )			->				Any:
								a   :	Dict			=       4
								a   :	str			=       3

								a   :	str			=       floats_tensor((batch_size, num_channels) + sizes   ).to(lowerCAmelCase__   )

								return {"sample": image}






							@property
							def 					__a     (     self   )			->				Union[str, Any]:
								return (3, 32, 32)






							@property
							def 					__a     (     self   )			->				int:
								return (3, 32, 32)






							def 					__a     (     self   )			->				int:
								a   :	str			=       {
								    "block_out_channels": [32, 64],
								    "in_channels": 3,
								    "out_channels": 3,
								    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
								    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "latent_channels": 3,
								}
								a   :	Union[str, Any]			=       self.dummy_input
								return init_dict, inputs_dict






							def 					__a     (     self   )			->				List[Any]:
								pass






							def 					__a     (     self   )			->				Union[str, Any]:
								pass






							def 					__a     (     self   )			->				int:
								a   :	Optional[int]			=       VQModel.from_pretrained("fusing/vqgan-dummy"   ,      output_loading_info=lowerCAmelCase__   )
								self.assertIsNotNone(lowerCAmelCase__   )
								self.assertEqual(len(loading_info["missing_keys"]   )   ,      0   )

								model.to(lowerCAmelCase__   )
								a   :	Optional[int]			=       model(**self.dummy_input   )

								assert image is not None, "Make sure output is not None"






							def 					__a     (     self   )			->				int:
								a   :	List[str]			=       VQModel.from_pretrained("fusing/vqgan-dummy"   )
								model.to(lowerCAmelCase__   ).eval()

								torch.manual_seed(0   )
								if torch.cuda.is_available():
									torch.cuda.manual_seed_all(0   )

								a   :	int			=       torch.randn(1   ,      model.config.in_channels   ,      model.config.sample_size   ,      model.config.sample_size   )
								a   :	Union[str, Any]			=       image.to(lowerCAmelCase__   )
								with torch.no_grad():
									a   :	Dict			=       model(lowerCAmelCase__   ).sample

								a   :	Tuple			=       output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								a   :	Tuple			=       torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]   )
								# fmt: on
								self.assertTrue(torch.allclose(lowerCAmelCase__   ,      lowerCAmelCase__   ,      atol=1E-3   )   )









"""simple docstring"""




import baseaa






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					str      )				->bytes:






	'''simple docstring'''






	return baseaa.aaaencode(string.encode("utf-8"      )      )






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					bytes      )				->str:






	'''simple docstring'''






	return baseaa.aaadecode(_lowercase      ).decode("utf-8"      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()


