



'''simple docstring'''





from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_lowerCAmelCase										=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowerCAmelCase										=     '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=8      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    lowerCAmelCase__			:  Any											= width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  def __init__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,)   ->		List[str]:
      super().__init__()

      self.register_modules(
          unet=__UpperCAmelCase  ,scheduler=__UpperCAmelCase  ,movq=__UpperCAmelCase  ,)
      lowerCAmelCase__			:  str											= 2 ** (len(self.movq.config.block_out_channels				) - 1)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase				)   ->		int:
      if latents is None:
          lowerCAmelCase__			:  Dict											= randn_tensor(__UpperCAmelCase  ,generator=__UpperCAmelCase  ,device=__UpperCAmelCase  ,dtype=__UpperCAmelCase				)
      else:
          if latents.shape != shape:
              raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""				)
          lowerCAmelCase__			:  Optional[int]											= latents.to(__UpperCAmelCase				)

      lowerCAmelCase__			:  List[Any]											= latents * scheduler.init_noise_sigma
      return latents




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=0				)   ->		Any:
      if is_accelerate_available():
          from accelerate import cpu_offload
      else:
          raise ImportError("""Please install accelerate via `pip install accelerate`"""				)

      lowerCAmelCase__			:  str											= torch.device(F"""cuda:{gpu_id}"""				)

      lowerCAmelCase__			:  Tuple											= [
          self.unet,
          self.movq,
      ]
      for cpu_offloaded_model in models:
          if cpu_offloaded_model is not None:
              cpu_offload(__UpperCAmelCase  ,__UpperCAmelCase				)




  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=0				)   ->		Union[str, Any]:
      if is_accelerate_available() and is_accelerate_version(""">="""  ,"""0.17.0.dev0"""				):
          from accelerate import cpu_offload_with_hook
      else:
          raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""				)

      lowerCAmelCase__			:  str											= torch.device(F"""cuda:{gpu_id}"""				)

      if self.device.type != "cpu":
          self.to("""cpu"""  ,silence_dtype_warnings=__UpperCAmelCase				)
          torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

      lowerCAmelCase__			:  Optional[int]											= None
      for cpu_offloaded_model in [self.unet, self.movq]:
          lowerCAmelCase__				,       lowerCAmelCase__			:  Tuple											= cpu_offload_with_hook(__UpperCAmelCase  ,__UpperCAmelCase  ,prev_module_hook=__UpperCAmelCase				)

      # We'll offload the last model manually.
      lowerCAmelCase__			:  str											= hook




  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      if not hasattr(self.unet  ,"""_hf_hook"""				):
          return self.device
      for module in self.unet.modules():
          if (
              hasattr(__UpperCAmelCase  ,"""_hf_hook"""				)
              and hasattr(module._hf_hook  ,"""execution_device"""				)
              and module._hf_hook.execution_device is not None
          ):
              return torch.device(module._hf_hook.execution_device				)
      return self.device






  @torch.no_grad()
  @replace_example_docstring(__UpperCAmelCase				)
  def __call__(   self  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase = 512  ,__UpperCAmelCase = 512  ,__UpperCAmelCase = 100  ,__UpperCAmelCase = 4.0  ,__UpperCAmelCase = 1  ,__UpperCAmelCase = None  ,__UpperCAmelCase = None  ,__UpperCAmelCase = "pil"  ,__UpperCAmelCase = True  ,)   ->		List[Any]:
      lowerCAmelCase__			:  Any											= self._execution_device

      lowerCAmelCase__			:  List[str]											= guidance_scale > 1.0

      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase				):
          lowerCAmelCase__			:  str											= torch.cat(__UpperCAmelCase  ,dim=0				)
      lowerCAmelCase__			:  List[str]											= image_embeds.shape[0] * num_images_per_prompt
      if isinstance(__UpperCAmelCase  ,__UpperCAmelCase				):
          lowerCAmelCase__			:  Any											= torch.cat(__UpperCAmelCase  ,dim=0				)

      if do_classifier_free_guidance:
          lowerCAmelCase__			:  List[str]											= image_embeds.repeat_interleave(__UpperCAmelCase  ,dim=0				)
          lowerCAmelCase__			:  List[Any]											= negative_image_embeds.repeat_interleave(__UpperCAmelCase  ,dim=0				)

      lowerCAmelCase__			:  Optional[Any]											= torch.cat([negative_image_embeds, image_embeds]  ,dim=0				).to(dtype=self.unet.dtype  ,device=__UpperCAmelCase				)

      self.scheduler.set_timesteps(__UpperCAmelCase  ,device=__UpperCAmelCase				)
      lowerCAmelCase__			:  Tuple											= self.scheduler.timesteps

      lowerCAmelCase__			:  str											= self.unet.config.in_channels

      lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[Any]											= downscale_height_and_width(__UpperCAmelCase  ,__UpperCAmelCase  ,self.movq_scale_factor				)

      # create initial latent
      lowerCAmelCase__			:  List[str]											= self.prepare_latents(
          (batch_size, num_channels_latents, height, width)  ,image_embeds.dtype  ,__UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,self.scheduler  ,)

      for i, t in enumerate(self.progress_bar(__UpperCAmelCase				)				):
          # expand the latents if we are doing classifier free guidance
          lowerCAmelCase__			:  int											= torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

          lowerCAmelCase__			:  Dict											= {"""image_embeds""": image_embeds}
          lowerCAmelCase__			:  Optional[int]											= self.unet(
              sample=__UpperCAmelCase  ,timestep=__UpperCAmelCase  ,encoder_hidden_states=__UpperCAmelCase  ,added_cond_kwargs=__UpperCAmelCase  ,return_dict=__UpperCAmelCase  ,)[0]

          if do_classifier_free_guidance:
              lowerCAmelCase__				,       lowerCAmelCase__			:  Any											= noise_pred.split(latents.shape[1]  ,dim=1				)
              lowerCAmelCase__				,       lowerCAmelCase__			:  List[Any]											= noise_pred.chunk(2				)
              lowerCAmelCase__				,       lowerCAmelCase__			:  List[str]											= variance_pred.chunk(2				)
              lowerCAmelCase__			:  List[str]											= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
              lowerCAmelCase__			:  Optional[Any]											= torch.cat([noise_pred, variance_pred_text]  ,dim=1				)

          if not (
              hasattr(self.scheduler.config  ,"""variance_type"""				)
              and self.scheduler.config.variance_type in ["learned", "learned_range"]
          ):
              lowerCAmelCase__				,       lowerCAmelCase__			:  str											= noise_pred.split(latents.shape[1]  ,dim=1				)

          # compute the previous noisy sample x_t -> x_t-1
          lowerCAmelCase__			:  Any											= self.scheduler.step(
              __UpperCAmelCase  ,__UpperCAmelCase  ,__UpperCAmelCase  ,generator=__UpperCAmelCase  ,)[0]
      # post-processing
      lowerCAmelCase__			:  Any											= self.movq.decode(__UpperCAmelCase  ,force_not_quantize=__UpperCAmelCase				)["""sample"""]

      if output_type not in ["pt", "np", "pil"]:
          raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""				)

      if output_type in ["np", "pil"]:
          lowerCAmelCase__			:  Union[str, Any]											= image * 0.5 + 0.5
          lowerCAmelCase__			:  List[Any]											= image.clamp(0  ,1				)
          lowerCAmelCase__			:  Dict											= image.cpu().permute(0  ,2  ,3  ,1				).float().numpy()

      if output_type == "pil":
          lowerCAmelCase__			:  str											= self.numpy_to_pil(__UpperCAmelCase				)

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=__UpperCAmelCase				)







'''simple docstring'''





from PIL import Image
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase      ):



    """simple docstring"""





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

    for j in range(UpperCamelCase      ):
        for i in range(UpperCamelCase      ):
            lowerCAmelCase__			:  Dict											= 255 if pixels[i, j] > mean else 0
    return image


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


