

import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case	,    __snake_case       	=							(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
       __snake_case       	=							image.resize((w, h)	,resample=PIL_INTERPOLATION["""lanczos"""]		)
       __snake_case       	=							np.array(__A		).astype(np.floataa		) / 255.0
       __snake_case       	=							image[None].transpose(0	,3	,1	,2		)
       __snake_case       	=							torch.from_numpy(__A		)
       return 2.0 * image - 1.0




class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , )      ->							Optional[int]:






              """simple docstring"""


              super().__init__()
              self.register_modules(vqvae=_UpperCamelCase      , unet=_UpperCamelCase      , scheduler=_UpperCamelCase   )






       @torch.no_grad()
       def __call__(		self      , _UpperCamelCase = None      , _UpperCamelCase = 1      , _UpperCamelCase = 1_00      , _UpperCamelCase = 0.0      , _UpperCamelCase = None      , _UpperCamelCase = "pil"      , _UpperCamelCase = True      , )      ->							Union[Tuple, ImagePipelineOutput]:






              """simple docstring"""


              if isinstance(_UpperCamelCase      , PIL.Image.Image   ):
                     __snake_case       	=							1
              elif isinstance(_UpperCamelCase      , torch.Tensor   ):
                     __snake_case       	=							image.shape[0]
              else:
                     raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(_UpperCamelCase   )}'   )

              if isinstance(_UpperCamelCase      , PIL.Image.Image   ):
                     __snake_case       	=							preprocess(_UpperCamelCase   )

              __snake_case	,    __snake_case       	=							image.shape[-2:]

              # in_channels should be 6: 3 for latents, 3 for low resolution image
              __snake_case       	=							(batch_size, self.unet.config.in_channels // 2, height, width)
              __snake_case       	=							next(self.unet.parameters()   ).dtype

              __snake_case       	=							randn_tensor(_UpperCamelCase      , generator=_UpperCamelCase      , device=self.device      , dtype=_UpperCamelCase   )

              __snake_case       	=							image.to(device=self.device      , dtype=_UpperCamelCase   )

              # set timesteps and move to the correct device
              self.scheduler.set_timesteps(_UpperCamelCase      , device=self.device   )
              __snake_case       	=							self.scheduler.timesteps

              # scale the initial noise by the standard deviation required by the scheduler
              __snake_case       	=							latents * self.scheduler.init_noise_sigma

              # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
              # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
              # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
              # and should be between [0, 1]
              __snake_case       	=							"""eta""" in set(inspect.signature(self.scheduler.step   ).parameters.keys()   )
              __snake_case       	=							{}
              if accepts_eta:
                     __snake_case       	=							eta

              for t in self.progress_bar(_UpperCamelCase   ):
                     # concat latents and low resolution image in the channel dimension.
                     __snake_case       	=							torch.cat([latents, image]      , dim=1   )
                     __snake_case       	=							self.scheduler.scale_model_input(_UpperCamelCase      , _UpperCamelCase   )
                     # predict the noise residual
                     __snake_case       	=							self.unet(_UpperCamelCase      , _UpperCamelCase   ).sample
                     # compute the previous noisy sample x_t -> x_t-1
                     __snake_case       	=							self.scheduler.step(_UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase      , **_UpperCamelCase   ).prev_sample

              # decode the image latents with the VQVAE
              __snake_case       	=							self.vqvae.decode(_UpperCamelCase   ).sample
              __snake_case       	=							torch.clamp(_UpperCamelCase      , -1.0      , 1.0   )
              __snake_case       	=							image / 2 + 0.5
              __snake_case       	=							image.cpu().permute(0      , 2      , 3      , 1   ).numpy()

              if output_type == "pil":
                     __snake_case       	=							self.numpy_to_pil(_UpperCamelCase   )

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=_UpperCamelCase   )




from PIL import Image







def 						lowerCamelCase__      ( __A    :Image		):


       """simple docstring"""




       __snake_case	,    __snake_case       	=							image.size
       __snake_case       	=							0
       __snake_case       	=							image.load()
       for i in range(__A		):
              for j in range(__A		):
                     __snake_case       	=							pixels[j, i]
                     mean += pixel
       mean //= width * height

       for j in range(__A		):
              for i in range(__A		):
                     __snake_case       	=							2_5_5 if pixels[i, j] > mean else 0
       return image


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

