
"""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,
)


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

lowercase_         =       "\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       A_      (       lowercase    ,			lowercase    ,			lowercase=8   )  ->				List[Any]:


     """simple docstring"""







     UpperCAmelCase_    :     Union[str, Any]            =	height // scale_factor**2
     if height % scale_factor**2 != 0:
          new_height += 1
     UpperCAmelCase_    :     Optional[int]            =	width // scale_factor**2
     if width % scale_factor**2 != 0:
          new_width += 1
     return new_height * scale_factor, new_width * scale_factor



class 		UpperCAmelCase_						(lowerCamelCase_    ):
 """simple docstring"""




 def __init__(			self							:			Optional[int]  , a_							:			UNetaDConditionModel  , a_							:			DDPMScheduler  , a_							:			VQModel  , )->       int:



      """simple docstring"""

      super().__init__()

      self.register_modules(
          unet=a_  , scheduler=a_  , movq=a_  , )
      UpperCAmelCase_    :     Optional[int]            =	2 ** (len(self.movq.config.block_out_channels			) - 1)




 def 	a   (			self							:			Union[str, Any]  , a_							:			Optional[int]  , a_							:			str  , a_							:			Optional[int]  , a_							:			Any  , a_							:			Optional[int]  , a_							:			Union[str, Any]			)->       Union[str, Any]:



      """simple docstring"""

      if latents is None:
           UpperCAmelCase_    :     List[Any]            =	randn_tensor(a_  , generator=a_  , device=a_  , dtype=a_			)
      else:
           if latents.shape != shape:
                raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''			)
           UpperCAmelCase_    :     Optional[Any]            =	latents.to(a_			)

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




 def 	a   (			self							:			Optional[int]  , a_							:			Any=0			)->       Dict:



      """simple docstring"""

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

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

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




 def 	a   (			self							:			Tuple  , a_							:			List[Any]=0			)->       Optional[int]:



      """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_    :     Optional[int]            =	torch.device(f'''cuda:{gpu_id}'''			)

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

      UpperCAmelCase_    :     int            =	None
      for cpu_offloaded_model in [self.unet, self.movq]:
           UpperCAmelCase_       ,UpperCAmelCase_    :     List[Any]            =	cpu_offload_with_hook(a_  , a_  , prev_module_hook=a_			)

      # We'll offload the last model manually.
      UpperCAmelCase_    :     Union[str, Any]            =	hook




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



      """simple docstring"""

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



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	self._execution_device

      UpperCAmelCase_    :     List[str]            =	guidance_scale > 1.0

      if isinstance(a_  , a_			):
           UpperCAmelCase_    :     Dict            =	torch.cat(a_  , dim=0			)
      if isinstance(a_  , a_			):
           UpperCAmelCase_    :     List[Any]            =	torch.cat(a_  , dim=0			)
      if isinstance(a_  , a_			):
           UpperCAmelCase_    :     Tuple            =	torch.cat(a_  , dim=0			)

      UpperCAmelCase_    :     List[str]            =	image_embeds.shape[0] * num_images_per_prompt

      if do_classifier_free_guidance:
           UpperCAmelCase_    :     str            =	image_embeds.repeat_interleave(a_  , dim=0			)
           UpperCAmelCase_    :     int            =	negative_image_embeds.repeat_interleave(a_  , dim=0			)
           UpperCAmelCase_    :     List[Any]            =	hint.repeat_interleave(a_  , dim=0			)

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

      UpperCAmelCase_    :     Tuple            =	self.movq.config.latent_channels

      UpperCAmelCase_       ,UpperCAmelCase_    :     Union[str, Any]            =	downscale_height_and_width(a_  , a_  , self.movq_scale_factor			)

      # create initial latent
      UpperCAmelCase_    :     Union[str, Any]            =	self.prepare_latents(
          (batch_size, num_channels_latents, height, width)  , image_embeds.dtype  , a_  , a_  , a_  , self.scheduler  , )

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

           UpperCAmelCase_    :     str            =	{"""image_embeds""": image_embeds, """hint""": hint}
           UpperCAmelCase_    :     Any            =	self.unet(
               sample=a_  , timestep=a_  , encoder_hidden_states=a_  , added_cond_kwargs=a_  , return_dict=a_  , )[0]

           if do_classifier_free_guidance:
                UpperCAmelCase_       ,UpperCAmelCase_    :     Tuple            =	noise_pred.split(latents.shape[1]  , dim=1			)
                UpperCAmelCase_       ,UpperCAmelCase_    :     Union[str, Any]            =	noise_pred.chunk(2			)
                UpperCAmelCase_       ,UpperCAmelCase_    :     int            =	variance_pred.chunk(2			)
                UpperCAmelCase_    :     Union[str, Any]            =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                UpperCAmelCase_    :     str            =	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_    :     Tuple            =	noise_pred.split(latents.shape[1]  , dim=1			)

           # compute the previous noisy sample x_t -> x_t-1
           UpperCAmelCase_    :     List[Any]            =	self.scheduler.step(
               a_  , a_  , a_  , generator=a_  , )[0]
      # post-processing
      UpperCAmelCase_    :     str            =	self.movq.decode(a_  , force_not_quantize=a_			)["""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_    :     Dict            =	image.clamp(0  , 1			)
           UpperCAmelCase_    :     Any            =	image.cpu().permute(0  , 2  , 3  , 1			).float().numpy()

      if output_type == "pil":
           UpperCAmelCase_    :     Optional[int]            =	self.numpy_to_pil(a_			)

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=a_			)



"""simple docstring"""







class 		UpperCAmelCase_						:
 """simple docstring"""




 def __init__(			self							:			Optional[Any]  , a_							:			int			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	n
      UpperCAmelCase_    :     str            =	[None] * self.n
      UpperCAmelCase_    :     List[Any]            =	0  # index of the first element
      UpperCAmelCase_    :     List[Any]            =	0
      UpperCAmelCase_    :     Dict            =	0




 def __len__(			self							:			Union[str, Any]			)->       int:



      """simple docstring"""

      return self.size




 def 	a   (			self							:			Dict			)->       bool:



      """simple docstring"""

      return self.size == 0




 def 	a   (			self							:			List[Any]			)->       Optional[int]:



      """simple docstring"""

      return False if self.is_empty() else self.array[self.front]




 def 	a   (			self							:			Dict  , a_							:			int			)->       Optional[int]:



      """simple docstring"""

      if self.size >= self.n:
           raise Exception("""QUEUE IS FULL"""			)

      UpperCAmelCase_    :     List[str]            =	data
      UpperCAmelCase_    :     Optional[int]            =	(self.rear + 1) % self.n
      self.size += 1
      return self






 def 	a   (			self							:			Union[str, Any]			)->       List[Any]:



      """simple docstring"""

      if self.size == 0:
           raise Exception("""UNDERFLOW"""			)

      UpperCAmelCase_    :     Tuple            =	self.array[self.front]
      UpperCAmelCase_    :     List[str]            =	None
      UpperCAmelCase_    :     Dict            =	(self.front + 1) % self.n
      self.size -= 1
      return temp

