






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	:							List[str]				=logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase	:							str				='''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> 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"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase					,    __lowerCAmelCase=8			) ->	Any:
     UpperCamelCase__			:   Optional[int]       				=  height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCamelCase__			:   Union[str, Any]       				=  width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor





class 			__a  (    A__       ):







 def __init__(		self					:   int							,   SCREAMING_SNAKE_CASE					:   UNetaDConditionModel							,   SCREAMING_SNAKE_CASE					:   DDPMScheduler							,   SCREAMING_SNAKE_CASE					:   VQModel							,   ):


      '''simple docstring'''






      super().__init__()

      self.register_modules(
          unet=SCREAMING_SNAKE_CASE							,   scheduler=SCREAMING_SNAKE_CASE							,   movq=SCREAMING_SNAKE_CASE							,   )
      UpperCamelCase__			:   List[str]       				=  2 ** (len(self.movq.config.block_out_channels						) - 1)







 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Any							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Tuple						):


      '''simple docstring'''






      if latents is None:
           UpperCamelCase__			:   List[Any]       				=  randn_tensor(SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   device=SCREAMING_SNAKE_CASE							,   dtype=SCREAMING_SNAKE_CASE						)
      else:
           if latents.shape != shape:
                raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
           UpperCamelCase__			:   str       				=  latents.to(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   List[str]       				=  latents * scheduler.init_noise_sigma
      return latents







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=0						):


      '''simple docstring'''






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

      UpperCamelCase__			:   str       				=  torch.device(F'cuda:{gpu_id}'						)

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







 def 					__lowercase					(		self					:   str							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=0						):


      '''simple docstring'''






      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."						)

      UpperCamelCase__			:   List[Any]       				=  torch.device(F'cuda:{gpu_id}'						)

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

      UpperCamelCase__			:   Dict       				=  None
      for cpu_offloaded_model in [self.unet, self.movq]:
           UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  cpu_offload_with_hook(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   prev_module_hook=SCREAMING_SNAKE_CASE						)

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







 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      if not hasattr(self.unet							,   "_hf_hook"						):
           return self.device
      for module in self.unet.modules():
           if (
               hasattr(SCREAMING_SNAKE_CASE							,   "_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(SCREAMING_SNAKE_CASE						)
 def __call__(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   Union[torch.FloatTensor, List[torch.FloatTensor]]							,   SCREAMING_SNAKE_CASE					:   Union[torch.FloatTensor, List[torch.FloatTensor]]							,   SCREAMING_SNAKE_CASE					:   torch.FloatTensor							,   SCREAMING_SNAKE_CASE					:   int = 5_12							,   SCREAMING_SNAKE_CASE					:   int = 5_12							,   SCREAMING_SNAKE_CASE					:   int = 1_00							,   SCREAMING_SNAKE_CASE					:   float = 4.0							,   SCREAMING_SNAKE_CASE					:   int = 1							,   SCREAMING_SNAKE_CASE					:   Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[torch.FloatTensor] = None							,   SCREAMING_SNAKE_CASE					:   Optional[str] = "pil"							,   SCREAMING_SNAKE_CASE					:   bool = True							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self._execution_device

      UpperCamelCase__			:   Dict       				=  guidance_scale > 1.0

      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   Dict       				=  torch.cat(SCREAMING_SNAKE_CASE							,   dim=0						)
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   str       				=  torch.cat(SCREAMING_SNAKE_CASE							,   dim=0						)
      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
           UpperCamelCase__			:   List[str]       				=  torch.cat(SCREAMING_SNAKE_CASE							,   dim=0						)

      UpperCamelCase__			:   int       				=  image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
           UpperCamelCase__			:   Tuple       				=  image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE							,   dim=0						)
           UpperCamelCase__			:   Union[str, Any]       				=  negative_image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE							,   dim=0						)
           UpperCamelCase__			:   int       				=  hint.repeat_interleave(SCREAMING_SNAKE_CASE							,   dim=0						)

      UpperCamelCase__			:   Union[str, Any]       				=  torch.cat([negative_image_embeds, image_embeds]							,   dim=0						).to(dtype=self.unet.dtype							,   device=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  torch.cat([hint, hint]							,   dim=0						).to(dtype=self.unet.dtype							,   device=SCREAMING_SNAKE_CASE						)
      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							,   device=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  self.scheduler.timesteps

      UpperCamelCase__			:   Dict       				=  self.movq.config.latent_channels

      UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  downscale_height_and_width(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   self.movq_scale_factor						)

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

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

           UpperCamelCase__			:   Union[str, Any]       				=  {"image_embeds": image_embeds, "hint": hint}
           UpperCamelCase__			:   Dict       				=  self.unet(
               sample=SCREAMING_SNAKE_CASE							,   timestep=SCREAMING_SNAKE_CASE							,   encoder_hidden_states=SCREAMING_SNAKE_CASE							,   added_cond_kwargs=SCREAMING_SNAKE_CASE							,   return_dict=SCREAMING_SNAKE_CASE							,   )[0]

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

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

      if output_type == "pil":
           UpperCamelCase__			:   int       				=  self.numpy_to_pil(SCREAMING_SNAKE_CASE						)

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=SCREAMING_SNAKE_CASE						)







import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


lowerCamelCase	:							Tuple				=False





class 			__a  (    unittest.TestCase       ):
 pass







@slow
@require_torch_gpu
class 			__a  (    unittest.TestCase       ):







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"						)
      pipe.to(SCREAMING_SNAKE_CASE						)
      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"						)
      UpperCamelCase__			:   List[Any]       				=  torch.manual_seed(0						)
      UpperCamelCase__			:   List[Any]       				=  pipe(
          image=SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   guidance_scale=7.5							,   num_inference_steps=50							,   output_type="numpy"							,   ).images

      UpperCamelCase__			:   Union[str, Any]       				=  image[0, 2_53:2_56, 2_53:2_56, -1]

      assert image.shape == (1, 5_12, 5_12, 3)
      UpperCamelCase__			:   Optional[Any]       				=  np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]						)

      assert np.abs(image_slice.flatten() - expected_slice						).max() < 1e-2