"""simple docstring"""




from __future__ import annotations

from typing import Any
class 		__A							:






     def __init__(	self       ,  a__ = 6						):
       _lowerCAmelCase		: Node | None						    =  None
       _lowerCAmelCase		: Node | None						    =  None
       self.create_linked_list(a__						)






     def 					__A						(	self       ,  a__						):
       _lowerCAmelCase		: List[Any]						    =  Node()
       _lowerCAmelCase		: Any						    =  current_node
       _lowerCAmelCase		: Optional[Any]						    =  current_node
       _lowerCAmelCase		: Any						    =  current_node
       for _ in range(1       ,  a__						):
         _lowerCAmelCase		: Dict						    =  Node()
         _lowerCAmelCase		: Union[str, Any]						    =  current_node
         _lowerCAmelCase		: Optional[Any]						    =  previous_node
         _lowerCAmelCase		: Optional[Any]						    =  current_node
       _lowerCAmelCase		: List[str]						    =  self.front
       _lowerCAmelCase		: Dict						    =  previous_node






     def 					__A						(	self						):
       return (
           self.front == self.rear
           and self.front is not None
           and self.front.data is None
       )






     def 					__A						(	self						):
       self.check_can_perform_operation()
       return self.front.data if self.front else None






     def 					__A						(	self       ,  a__						):
       if self.rear is None:
         return

       self.check_is_full()
       if not self.is_empty():
         _lowerCAmelCase		: Dict						    =  self.rear.next
       if self.rear:
         _lowerCAmelCase		: int						    =  data






     def 					__A						(	self						):
       self.check_can_perform_operation()
       if self.rear is None or self.front is None:
         return None
       if self.front == self.rear:
         _lowerCAmelCase		: Tuple						    =  self.front.data
         _lowerCAmelCase		: Tuple						    =  None
         return data

       _lowerCAmelCase		: Optional[int]						    =  self.front
       _lowerCAmelCase		: List[Any]						    =  old_front.next
       _lowerCAmelCase		: int						    =  old_front.data
       _lowerCAmelCase		: Dict						    =  None
       return data






     def 					__A						(	self						):
       if self.is_empty():
         raise Exception("""Empty Queue"""						)






     def 					__A						(	self						):
       if self.rear and self.rear.next == self.front:
         raise Exception("""Full Queue"""						)







class 		__A							:






     def __init__(	self						):
       _lowerCAmelCase		: Any | None						    =  None
       _lowerCAmelCase		: Node | None						    =  None
       _lowerCAmelCase		: Node | None						    =  None


if __name__ == "__main__":
     import doctest

     doctest.testmod()

"""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,
)


_a   :	Union[str, Any] 					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_a   :	int 					=     '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Dict=8			) ->   Any:
  _lowerCAmelCase		: List[Any]						    =  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCAmelCase		: Optional[Any]						    =  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Any=512    ,_lowerCamelCase		: Dict=512			) ->   List[Any]:
  _lowerCAmelCase		: Any						    =  pil_image.resize((w, h)    ,resample=Image.BICUBIC    ,reducing_gap=1			)
  _lowerCAmelCase		: Dict						    =  np.array(pil_image.convert("""RGB"""			)			)
  _lowerCAmelCase		: List[str]						    =  arr.astype(np.floataa			) / 1_27.5 - 1
  _lowerCAmelCase		: int						    =  np.transpose(_lowerCamelCase    ,[2, 0, 1]			)
  _lowerCAmelCase		: Optional[Any]						    =  torch.from_numpy(_lowerCamelCase			).unsqueeze(0			)
  return image






class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()

       self.register_modules(
           unet=a__       ,  scheduler=a__       ,  movq=a__       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  2 ** (len(self.movq.config.block_out_channels						) - 1)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       # get the original timestep using init_timestep
       _lowerCAmelCase		: Optional[Any]						    =  min(int(num_inference_steps * strength						)       ,  a__						)

       _lowerCAmelCase		: List[Any]						    =  max(num_inference_steps - init_timestep       ,  0						)
       _lowerCAmelCase		: Dict						    =  self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=None						):
       if not isinstance(a__       ,  (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(a__						)}"						)

       _lowerCAmelCase		: Union[str, Any]						    =  image.to(device=a__       ,  dtype=a__						)

       _lowerCAmelCase		: int						    =  batch_size * num_images_per_prompt

       if image.shape[1] == 4:
         _lowerCAmelCase		: int						    =  image

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

         elif isinstance(a__       ,  a__						):
           _lowerCAmelCase		: Optional[int]						    =  [
               self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(a__						)
           ]
           _lowerCAmelCase		: Optional[int]						    =  torch.cat(a__       ,  dim=0						)
         else:
           _lowerCAmelCase		: List[Any]						    =  self.movq.encode(a__						).latent_dist.sample(a__						)

         _lowerCAmelCase		: Dict						    =  self.movq.config.scaling_factor * init_latents

       _lowerCAmelCase		: str						    =  torch.cat([init_latents]       ,  dim=0						)

       _lowerCAmelCase		: Dict						    =  init_latents.shape
       _lowerCAmelCase		: str						    =  randn_tensor(a__       ,  generator=a__       ,  device=a__       ,  dtype=a__						)

       # get latents
       _lowerCAmelCase		: Optional[Any]						    =  self.scheduler.add_noise(a__       ,  a__       ,  a__						)

       _lowerCAmelCase		: int						    =  init_latents

       return latents






     def 					__A						(	self       ,  a__=0						):
       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		: int						    =  [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(a__       ,  a__						)






     def 					__A						(	self       ,  a__=0						):
       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		: 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)

       _lowerCAmelCase		: List[str]						    =  None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  cpu_offload_with_hook(a__       ,  a__       ,  prev_module_hook=a__						)

       # We'll offload the last model manually.
       _lowerCAmelCase		: Tuple						    =  hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__A						(	self						):
       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       ,  a__       ,  a__       ,  a__       ,  a__ = 512       ,  a__ = 512       ,  a__ = 100       ,  a__ = 4.0       ,  a__ = 0.3       ,  a__ = 1       ,  a__ = None       ,  a__ = "pil"       ,  a__ = True       ,  ):
       _lowerCAmelCase		: Dict						    =  self._execution_device

       _lowerCAmelCase		: Optional[Any]						    =  guidance_scale > 1.0

       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Dict						    =  torch.cat(a__       ,  dim=0						)
       _lowerCAmelCase		: Dict						    =  image_embeds.shape[0]
       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: List[Any]						    =  torch.cat(a__       ,  dim=0						)

       if do_classifier_free_guidance:
         _lowerCAmelCase		: int						    =  image_embeds.repeat_interleave(a__       ,  dim=0						)
         _lowerCAmelCase		: Any						    =  negative_image_embeds.repeat_interleave(a__       ,  dim=0						)

       _lowerCAmelCase		: Optional[int]						    =  torch.cat([negative_image_embeds, image_embeds]       ,  dim=0						).to(dtype=self.unet.dtype       ,  device=a__						)

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

       _lowerCAmelCase		: Tuple						    =  torch.cat([prepare_image(a__       ,  a__       ,  a__						) for i in image]       ,  dim=0						)
       _lowerCAmelCase		: Union[str, Any]						    =  image.to(dtype=image_embeds.dtype       ,  device=a__						)

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

         _lowerCAmelCase		: int						    =  {"""image_embeds""": image_embeds}
         _lowerCAmelCase		: List[str]						    =  self.unet(
             sample=a__       ,  timestep=a__       ,  encoder_hidden_states=a__       ,  added_cond_kwargs=a__       ,  return_dict=a__       ,  )[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase		: List[str]						    =  self.scheduler.step(
             a__       ,  a__       ,  a__       ,  generator=a__       ,  )[0]

       # post-processing
       _lowerCAmelCase		: int						    =  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"]:
         _lowerCAmelCase		: List[Any]						    =  image * 0.5 + 0.5
         _lowerCAmelCase		: Any						    =  image.clamp(0       ,  1						)
         _lowerCAmelCase		: Tuple						    =  image.cpu().permute(0       ,  2       ,  3       ,  1						).float().numpy()

       if output_type == "pil":
         _lowerCAmelCase		: List[str]						    =  self.numpy_to_pil(a__						)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__						)




