




'''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  		=      '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'






def 		_lowerCAmelCase							(   lowercase							: Optional[int]					,  lowercase							: int					,  lowercase							: Tuple=8    )			->Optional[int]:
   """simple docstring"""




   lowercase__       							=							height // scale_factor**2
   if height % scale_factor**2 != 0:
      new_height += 1
   lowercase__       							=							width // scale_factor**2
   if width % scale_factor**2 != 0:
      new_width += 1
   return new_height * scale_factor, new_width * scale_factor





class   __A (							snake_case_  ):



   """simple docstring"""







   def __init__(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, )->       int:
      super().__init__()

      self.register_modules(
          unet=__lowercase				, scheduler=__lowercase				, movq=__lowercase				, )
      lowercase__       							=							2 ** (len(self.movq.config.block_out_channels						) - 1)

   def snake_case_(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase						)->       Dict:







      if latents is None:
         lowercase__       							=							randn_tensor(__lowercase				, generator=__lowercase				, device=__lowercase				, dtype=__lowercase						)
      else:
         if latents.shape != shape:
            raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''						)
         lowercase__       							=							latents.to(__lowercase						)

      lowercase__       							=							latents * scheduler.init_noise_sigma
      return latents

   def snake_case_(      self				, _lowerCamelCase=0						)->       List[Any]:







      if is_accelerate_available():
         from accelerate import cpu_offload
      else:
         raise ImportError('''Please install accelerate via `pip install accelerate`'''						)

      lowercase__       							=							torch.device(f'''cuda:{gpu_id}'''						)

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

   def snake_case_(      self				, _lowerCamelCase=0						)->       int:







      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.'''						)

      lowercase__       							=							torch.device(f'''cuda:{gpu_id}'''						)

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

      lowercase__       							=							None
      for cpu_offloaded_model in [self.unet, self.movq]:
         lowercase__       							=							cpu_offload_with_hook(__lowercase				, __lowercase				, prev_module_hook=__lowercase						)

      # We'll offload the last model manually.
      lowercase__       							=							hook

   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def snake_case_(      self						)->       int:







      if not hasattr(self.unet				, '''_hf_hook'''						):
         return self.device
      for module in self.unet.modules():
         if (
             hasattr(__lowercase				, '''_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(__lowercase						)
   def __call__(      self				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase = 5_1_2				, _lowerCamelCase = 5_1_2				, _lowerCamelCase = 1_0_0				, _lowerCamelCase = 4.0				, _lowerCamelCase = 1				, _lowerCamelCase = None				, _lowerCamelCase = None				, _lowerCamelCase = "pil"				, _lowerCamelCase = True				, )->       List[Any]:
      lowercase__       							=							self._execution_device

      lowercase__       							=							guidance_scale > 1.0

      if isinstance(__lowercase				, __lowercase						):
         lowercase__       							=							torch.cat(__lowercase				, dim=0						)
      if isinstance(__lowercase				, __lowercase						):
         lowercase__       							=							torch.cat(__lowercase				, dim=0						)
      if isinstance(__lowercase				, __lowercase						):
         lowercase__       							=							torch.cat(__lowercase				, dim=0						)

      lowercase__       							=							image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
         lowercase__       							=							image_embeds.repeat_interleave(__lowercase				, dim=0						)
         lowercase__       							=							negative_image_embeds.repeat_interleave(__lowercase				, dim=0						)
         lowercase__       							=							hint.repeat_interleave(__lowercase				, dim=0						)

      lowercase__       							=							torch.cat([negative_image_embeds, image_embeds]				, dim=0						).to(dtype=self.unet.dtype				, device=__lowercase						)
      lowercase__       							=							torch.cat([hint, hint]				, dim=0						).to(dtype=self.unet.dtype				, device=__lowercase						)
      self.scheduler.set_timesteps(__lowercase				, device=__lowercase						)
      lowercase__       							=							self.scheduler.timesteps

      lowercase__       							=							self.movq.config.latent_channels

      lowercase__       							=							downscale_height_and_width(__lowercase				, __lowercase				, self.movq_scale_factor						)

      # create initial latent
      lowercase__       							=							self.prepare_latents(
          (batch_size, num_channels_latents, height, width)				, image_embeds.dtype				, __lowercase				, __lowercase				, __lowercase				, self.scheduler				, )

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

         lowercase__       							=							{'image_embeds': image_embeds, 'hint': hint}
         lowercase__       							=							self.unet(
             sample=__lowercase				, timestep=__lowercase				, encoder_hidden_states=__lowercase				, added_cond_kwargs=__lowercase				, return_dict=__lowercase				, )[0]

         if do_classifier_free_guidance:
            lowercase__       							=							noise_pred.split(latents.shape[1]				, dim=1						)
            lowercase__       							=							noise_pred.chunk(2						)
            lowercase__       							=							variance_pred.chunk(2						)
            lowercase__       							=							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
            lowercase__       							=							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"]
         ):
            lowercase__       							=							noise_pred.split(latents.shape[1]				, dim=1						)

         # compute the previous noisy sample x_t -> x_t-1
         lowercase__       							=							self.scheduler.step(
             __lowercase				, __lowercase				, __lowercase				, generator=__lowercase				, )[0]
      # post-processing
      lowercase__       							=							self.movq.decode(__lowercase				, force_not_quantize=__lowercase						)['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"]:
         lowercase__       							=							image * 0.5 + 0.5
         lowercase__       							=							image.clamp(0				, 1						)
         lowercase__       							=							image.cpu().permute(0				, 2				, 3				, 1						).float().numpy()

      if output_type == "pil":
         lowercase__       							=							self.numpy_to_pil(__lowercase						)

      if not return_dict:
         return (image,)

      return ImagePipelineOutput(images=__lowercase						)








'''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  if dst_width < 0 or dst_height < 0:
   raise ValueError('Destination width/height should be > 0'    )

  __A	:		Dict	  =      img
  __A	:		Dict	  =      img.shape[1]
  __A	:		Tuple	  =      img.shape[0]
  __A	:		List[str]	  =      dst_width
  __A	:		Tuple	  =      dst_height

  __A	:		Optional[Any]	  =      self.src_w / self.dst_w
  __A	:		List[str]	  =      self.src_h / self.dst_h

  __A	:		Dict	  =      (
      np.ones((self.dst_h, self.dst_w, 3)   ,   np.uinta    ) * 255
  )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  for i in range(self.dst_h    ):
   for j in range(self.dst_w    ):
    __A	:		List[str]	  =      self.img[self.get_y(__lowercase    )][self.get_x(__lowercase    )]


 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_x * x    )







 def 			snake_case__ (   self   ,   __lowercase    ):

  """simple docstring"""







  return int(self.ratio_y * y    )


if __name__ == "__main__":
 UpperCAmelCase_				,      UpperCAmelCase_			:  str										=   8_0_0, 6_0_0
 UpperCAmelCase_			:  str										=   imread('image_data/lena.jpg', 1)
 UpperCAmelCase_			:  List[str]										=   NearestNeighbour(im, dst_w, dst_h)
 n.process()

 imshow(
     f'''Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}''', n.output
 )
 waitKey(0)
 destroyAllWindows()






