
'''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 			lowerCAmelCase (				__UpperCAmelCase		,     __UpperCAmelCase		,     unittest.TestCase			):
		a							:					Optional[Any]       =					VQModel
		a							:					Tuple       =					"""sample"""




		@property
		def     lowercase       (				self					,						UpperCamelCase=(32, 32)      ):
								_SCREAMING_SNAKE_CASE				= 4
								_SCREAMING_SNAKE_CASE				= 3

								_SCREAMING_SNAKE_CASE				= floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCamelCase      )

								return {"sample": image}




		@property
		def     lowercase       (				self      ):
								return (3, 32, 32)




		@property
		def     lowercase       (				self      ):
								return (3, 32, 32)




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= {
								    "block_out_channels": [32, 64],
								    "in_channels": 3,
								    "out_channels": 3,
								    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
								    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
								    "latent_channels": 3,
								}
								_SCREAMING_SNAKE_CASE				= self.dummy_input
								return init_dict, inputs_dict




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE,							_SCREAMING_SNAKE_CASE				= VQModel.from_pretrained("fusing/vqgan-dummy"					,						output_loading_info=UpperCamelCase      )
								self.assertIsNotNone(UpperCamelCase      )
								self.assertEqual(len(loading_info["missing_keys"]      )					,						0      )

								model.to(UpperCamelCase      )
								_SCREAMING_SNAKE_CASE				= model(**self.dummy_input      )

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




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= VQModel.from_pretrained("fusing/vqgan-dummy"      )
								model.to(UpperCamelCase      ).eval()

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

								_SCREAMING_SNAKE_CASE				= torch.randn(1					,						model.config.in_channels					,						model.config.sample_size					,						model.config.sample_size      )
								_SCREAMING_SNAKE_CASE				= image.to(UpperCamelCase      )
								with torch.no_grad():
														_SCREAMING_SNAKE_CASE				= model(UpperCamelCase      ).sample

								_SCREAMING_SNAKE_CASE				= output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								_SCREAMING_SNAKE_CASE				= torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]      )
								# fmt: on
								self.assertTrue(torch.allclose(UpperCamelCase					,						UpperCamelCase					,						atol=1e-3      )      )

'''simple docstring'''







import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


_snake_case				:   List[Any]     =				logging.get_logger(__name__)


class 			lowerCAmelCase (				__UpperCAmelCase			):




		def __init__(				self					,						*UpperCamelCase					,						**UpperCamelCase      ):
								warnings.warn(
								    "The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
								    " use DPTImageProcessor instead."					,						UpperCamelCase					,						)
								super().__init__(*UpperCamelCase					,						**UpperCamelCase      )