

'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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 KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

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

        >>> prompt = \"A red cartoon frog, 4k\"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16
        ... )
        >>> pipe.to(\"cuda\")

        >>> init_image = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/frog.png\"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

        >>> image[0].save(\"red_frog.png\")
        ```
"""
def      _lowerCAmelCase(	__A   ,       __A   ,       __A=8			):
  UpperCAmelCase					      =  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  UpperCAmelCase					      =  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor
def      _lowerCAmelCase(	__A   ,       __A=512   ,       __A=512			):
  UpperCAmelCase					      =  pil_image.resize((w, h)   ,       resample=Image.BICUBIC   ,       reducing_gap=1			)
  UpperCAmelCase					      =  np.array(pil_image.convert("RGB"			)			)
  UpperCAmelCase					      =  arr.astype(np.floataa			) / 127.5 - 1
  UpperCAmelCase					      =  np.transpose(__A   ,       [2, 0, 1]			)
  UpperCAmelCase					      =  torch.from_numpy(__A			).unsqueeze(0			)
  return image






class 						__magic_name__    (       _a    ):


 def __init__(						self		: str			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Optional[int]			,						)    ->      Union[str, Any]:
   super().__init__()

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


 def _UpperCamelCase	(						self		: Tuple			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Any    )    ->      Union[str, Any]:
   # get the original timestep using init_timestep
   UpperCAmelCase					      =  min(int(num_inference_steps * strength    )			,						snake_case_    )

   UpperCAmelCase					      =  max(num_inference_steps - init_timestep			,						0    )
   UpperCAmelCase					      =  self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start


 def _UpperCamelCase	(						self		: List[str]			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: Dict			,						lowerCAmelCase__		: int			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: List[str]=None    )    ->      str:
   if not isinstance(snake_case_			,						(torch.Tensor, PIL.Image.Image, list)    ):
     raise ValueError(
         f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case_    )}"    )

   UpperCAmelCase					      =  image.to(device=snake_case_			,						dtype=snake_case_    )

   UpperCAmelCase					      =  batch_size * num_images_per_prompt

   if image.shape[1] == 4:
     UpperCAmelCase					      =  image

   else:
     if isinstance(snake_case_			,						snake_case_    ) and len(snake_case_    ) != batch_size:
       raise ValueError(
           f"You have passed a list of generators of length {len(snake_case_    )}, but requested an effective batch"
           f" size of {batch_size}. Make sure the batch size matches the length of the generators."    )

     elif isinstance(snake_case_			,						snake_case_    ):
       UpperCAmelCase					      =  [
           self.movq.encode(image[i : i + 1]    ).latent_dist.sample(generator[i]    ) for i in range(snake_case_    )
       ]
       UpperCAmelCase					      =  torch.cat(snake_case_			,						dim=0    )
     else:
       UpperCAmelCase					      =  self.movq.encode(snake_case_    ).latent_dist.sample(snake_case_    )

     UpperCAmelCase					      =  self.movq.config.scaling_factor * init_latents

   UpperCAmelCase					      =  torch.cat([init_latents]			,						dim=0    )

   UpperCAmelCase					      =  init_latents.shape
   UpperCAmelCase					      =  randn_tensor(snake_case_			,						generator=snake_case_			,						device=snake_case_			,						dtype=snake_case_    )

   # get latents
   UpperCAmelCase					      =  self.scheduler.add_noise(snake_case_			,						snake_case_			,						snake_case_    )

   UpperCAmelCase					      =  init_latents

   return latents


 def _UpperCamelCase	(						self		: Dict			,						lowerCAmelCase__		: Union[str, Any]=0    )    ->      Tuple:
   if is_accelerate_available():
     from accelerate import cpu_offload
   else:
     raise ImportError("Please install accelerate via `pip install accelerate`"    )

   UpperCAmelCase					      =  torch.device(f"cuda:{gpu_id}"    )

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


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: str=0    )    ->      List[str]:
   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					      =  torch.device(f"cuda:{gpu_id}"    )

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

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

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


 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def _UpperCamelCase	(						self		: Optional[Any]    )    ->      Optional[int]:
   if not hasattr(self.unet			,						"_hf_hook"    ):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(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(snake_case_    )
 def __call__(						self		: str			,						lowerCAmelCase__		: List[Any]			,						lowerCAmelCase__		: Any			,						lowerCAmelCase__		: Tuple			,						lowerCAmelCase__		: List[str] = 5_1_2			,						lowerCAmelCase__		: Any = 5_1_2			,						lowerCAmelCase__		: int = 1_0_0			,						lowerCAmelCase__		: int = 4.0			,						lowerCAmelCase__		: List[str] = 0.3			,						lowerCAmelCase__		: Tuple = 1			,						lowerCAmelCase__		: int = None			,						lowerCAmelCase__		: Union[str, Any] = "pil"			,						lowerCAmelCase__		: Tuple = True			,						)    ->      List[str]:
   UpperCAmelCase					      =  self._execution_device

   UpperCAmelCase					      =  guidance_scale > 1.0

   if isinstance(snake_case_			,						snake_case_    ):
     UpperCAmelCase					      =  torch.cat(snake_case_			,						dim=0    )
   UpperCAmelCase					      =  image_embeds.shape[0]
   if isinstance(snake_case_			,						snake_case_    ):
     UpperCAmelCase					      =  torch.cat(snake_case_			,						dim=0    )

   if do_classifier_free_guidance:
     UpperCAmelCase					      =  image_embeds.repeat_interleave(snake_case_			,						dim=0    )
     UpperCAmelCase					      =  negative_image_embeds.repeat_interleave(snake_case_			,						dim=0    )

   UpperCAmelCase					      =  torch.cat([negative_image_embeds, image_embeds]			,						dim=0    ).to(dtype=self.unet.dtype			,						device=snake_case_    )

   if not isinstance(snake_case_			,						snake_case_    ):
     UpperCAmelCase					      =  [image]
   if not all(isinstance(snake_case_			,						(PIL.Image.Image, torch.Tensor)    ) for i in image    ):
     raise ValueError(
         f"Input is in incorrect format: {[type(snake_case_    ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"    )

   UpperCAmelCase					      =  torch.cat([prepare_image(snake_case_			,						snake_case_			,						snake_case_    ) for i in image]			,						dim=0    )
   UpperCAmelCase					      =  image.to(dtype=image_embeds.dtype			,						device=snake_case_    )

   UpperCAmelCase					      =  self.movq.encode(snake_case_    )["latents"]
   UpperCAmelCase					      =  latents.repeat_interleave(snake_case_			,						dim=0    )
   self.scheduler.set_timesteps(snake_case_			,						device=snake_case_    )
   UpperCAmelCase      ,	UpperCAmelCase					      =  self.get_timesteps(snake_case_			,						snake_case_			,						snake_case_    )
   UpperCAmelCase					      =  timesteps[:1].repeat(batch_size * num_images_per_prompt    )
   UpperCAmelCase      ,	UpperCAmelCase					      =  downscale_height_and_width(snake_case_			,						snake_case_			,						self.movq_scale_factor    )
   UpperCAmelCase					      =  self.prepare_latents(
       snake_case_			,						snake_case_			,						snake_case_			,						snake_case_			,						image_embeds.dtype			,						snake_case_			,						snake_case_    )
   for i, t in enumerate(self.progress_bar(snake_case_    )    ):
     # expand the latents if we are doing classifier free guidance
     UpperCAmelCase					      =  torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents

     UpperCAmelCase					      =  {"image_embeds": image_embeds}
     UpperCAmelCase					      =  self.unet(
         sample=snake_case_			,						timestep=snake_case_			,						encoder_hidden_states=snake_case_			,						added_cond_kwargs=snake_case_			,						return_dict=snake_case_			,						)[0]

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

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase					      =  self.scheduler.step(
         snake_case_			,						snake_case_			,						snake_case_			,						generator=snake_case_			,						)[0]

   # post-processing
   UpperCAmelCase					      =  self.movq.decode(snake_case_			,						force_not_quantize=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					      =  image * 0.5 + 0.5
     UpperCAmelCase					      =  image.clamp(0			,						1    )
     UpperCAmelCase					      =  image.cpu().permute(0			,						2			,						3			,						1    ).float().numpy()

   if output_type == "pil":
     UpperCAmelCase					      =  self.numpy_to_pil(snake_case_    )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=snake_case_    )






lowerCAmelCase__       = "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

lowerCAmelCase__       = [{"type": "code", "content": INSTALL_CONTENT}]
lowerCAmelCase__       = {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



