






'''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 a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							VQModel
   _snake_case        =							"""sample"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self					,   __a=(3_2, 3_2))			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= 4
      __snake_case				:      Optional[int]     							= 3

      __snake_case				:      str     							= floats_tensor((batch_size, num_channels) + sizes).to(__a)

      return {"sample": image}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= {
          'block_out_channels': [3_2, 6_4],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 3,
      }
      __snake_case				:      List[Any]     							= self.dummy_input
      return init_dict, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






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

      model.to(__a)
      __snake_case				:      Any     							= model(**self.dummy_input)

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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy')
      model.to(__a).eval()

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

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

      __snake_case				:      int     							= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      __snake_case				:      int     							= 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(__a					,   __a					,   atol=1E-3))







'''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 a_    (    UpperCamelCase_		,      UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							VQModel
   _snake_case        =							"""sample"""







   @property
   def     SCREAMING_SNAKE_CASE__					(self					,   __a=(3_2, 3_2))			->      str:
      """simple docstring"""






      __snake_case				:      Dict     							= 4
      __snake_case				:      Optional[int]     							= 3

      __snake_case				:      str     							= floats_tensor((batch_size, num_channels) + sizes).to(__a)

      return {"sample": image}







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return (3, 3_2, 3_2)







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= {
          'block_out_channels': [3_2, 6_4],
          'in_channels': 3,
          'out_channels': 3,
          'down_block_types': ['DownEncoderBlock2D', 'DownEncoderBlock2D'],
          'up_block_types': ['UpDecoderBlock2D', 'UpDecoderBlock2D'],
          'latent_channels': 3,
      }
      __snake_case				:      List[Any]     							= self.dummy_input
      return init_dict, inputs_dict







   def     SCREAMING_SNAKE_CASE__					(self)			->      Any:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






      pass







   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






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

      model.to(__a)
      __snake_case				:      Any     							= model(**self.dummy_input)

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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      Union[str, Any]     							= VQModel.from_pretrained('fusing/vqgan-dummy')
      model.to(__a).eval()

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

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

      __snake_case				:      int     							= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      __snake_case				:      int     							= 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(__a					,   __a					,   atol=1E-3))