





'''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 			SCREAMING_SNAKE_CASE   (a__ ,		a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	VQModel
     lowerCAmelCase		      =	'''sample'''


     @property
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=(32, 32)):

          '''simple docstring'''






          __A						:			Optional[Any]								=	4
          __A						:			Dict								=	3

          __A						:			List[Any]								=	floats_tensor((batch_size, num_channels) + sizes).to(_UpperCAmelCase)

          return {"sample": image}


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return (3, 32, 32)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return (3, 32, 32)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	{
              '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						:			Optional[int]								=	self.dummy_input
          return init_dict, inputs_dict


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	VQModel.from_pretrained('fusing/vqgan-dummy'						,     output_loading_info=_UpperCAmelCase)
          self.assertIsNotNone(_UpperCAmelCase)
          self.assertEqual(len(loading_info['missing_keys'])						,     0)

          model.to(_UpperCAmelCase)
          __A						:			Any								=	model(**self.dummy_input)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	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)

          __A						:			List[str]								=	torch.randn(1						,     model.config.in_channels						,     model.config.sample_size						,     model.config.sample_size)
          __A						:			Optional[Any]								=	image.to(_UpperCAmelCase)
          with torch.no_grad():
               __A						:			Tuple								=	model(_UpperCAmelCase).sample

          __A						:			List[Any]								=	output[0, -1, -3:, -3:].flatten().cpu()
          # fmt: off
          __A						:			List[str]								=	torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
          # fmt: on
          self.assertTrue(torch.allclose(_UpperCAmelCase						,     _UpperCAmelCase						,     atol=1e-3))






'''simple docstring'''




import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
  lowercase__   :				Dict      =   {
      '''linear''': PIL.Image.Resampling.BILINEAR,
      '''bilinear''': PIL.Image.Resampling.BILINEAR,
      '''bicubic''': PIL.Image.Resampling.BICUBIC,
      '''lanczos''': PIL.Image.Resampling.LANCZOS,
      '''nearest''': PIL.Image.Resampling.NEAREST,
  }
else:
  lowercase__   :				Any      =   {
      '''linear''': PIL.Image.LINEAR,
      '''bilinear''': PIL.Image.BILINEAR,
      '''bicubic''': PIL.Image.BICUBIC,
      '''lanczos''': PIL.Image.LANCZOS,
      '''nearest''': PIL.Image.NEAREST,
  }






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Dict								=	(images / 2 + 0.5).clamp(0			,							1      )
     __A						:			str								=	images.cpu().permute(0			,							2			,							3			,							1      ).float().numpy()
     __A						:			Dict								=	numpy_to_pil(__snake_case      )
     return images







def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     if images.ndim == 3:
          __A						:			List[Any]								=	images[None, ...]
     __A						:			List[str]								=	(images * 2_55).round().astype('uint8'      )
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          __A						:			str								=	[Image.fromarray(image.squeeze()			,							mode='L'      ) for image in images]
     else:
          __A						:			str								=	[Image.fromarray(__snake_case      ) for image in images]

     return pil_images