


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 			_lowercase (							lowercase__							,  lowercase__							,  unittest.TestCase):


       """simple docstring"""




       A__											=			VQModel
       A__											=			"sample"





       @property
       def 						lowerCAmelCase     (   self							:			List[Any]					,      __lowerCamelCase							:			Tuple=(32, 32)       ):


             '''simple docstring'''



             lowerCamelCase__		:				Any    =  4
             lowerCamelCase__		:				Tuple    =  3

             lowerCamelCase__		:				Any    =  floats_tensor((batch_size, num_channels) + sizes       ).to(__lowerCamelCase       )

             return {"sample": image}





       @property
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



             return (3, 32, 32)





       @property
       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             return (3, 32, 32)





       def 						lowerCAmelCase     (   self							:			str       ):


             '''simple docstring'''



             lowerCamelCase__		:				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,
             }
             lowerCamelCase__		:				Optional[Any]    =  self.dummy_input
             return init_dict, inputs_dict





       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			Optional[Any]       ):


             '''simple docstring'''



             pass





       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__    ,   lowerCamelCase__		:				str    =  VQModel.from_pretrained("fusing/vqgan-dummy"					,      output_loading_info=__lowerCamelCase       )
             self.assertIsNotNone(__lowerCamelCase       )
             self.assertEqual(len(loading_info["missing_keys"]       )					,      0       )

             model.to(__lowerCamelCase       )
             lowerCamelCase__		:				Optional[int]    =  model(**self.dummy_input       )

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





       def 						lowerCAmelCase     (   self							:			Optional[int]       ):


             '''simple docstring'''



             lowerCamelCase__		:				List[Any]    =  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       )

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

             lowerCamelCase__		:				Dict    =  output[0, -1, -3:, -3:].flatten().cpu()
             # fmt: off
             lowerCamelCase__		:				int    =  torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]       )
             # fmt: on
             self.assertTrue(torch.allclose(__lowerCamelCase					,      __lowerCamelCase					,      atol=1E-3       )       )






from PIL import Image

def       lowercase_		(		_A			:    Image						):

      """simple docstring"""




      lowerCamelCase__    ,   lowerCamelCase__		:				List[str]    =  image.size
      lowerCamelCase__		:				Dict    =  0
      lowerCamelCase__		:				Dict    =  image.load()
      for i in range(_A						):
            for j in range(_A						):
                  lowerCamelCase__		:				List[str]    =  pixels[j, i]
                  mean += pixel
      mean //= width * height

      for j in range(_A						):
            for i in range(_A						):
                  lowerCamelCase__		:				Union[str, Any]    =  255 if pixels[i, j] > mean else 0
      return image


if __name__ == "__main__":
    A      :    int		=					mean_threshold(Image.open("path_to_image").convert("L"))
    image.save("output_image_path")


