
'''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,
)


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

A__   :		Optional[Any] 		='\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")\n        >>> pipe_prior.to("cuda")\n        >>> prompt = "red cat, 4k photo"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")\n        >>> pipe.to("cuda")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save("cat.png")\n        ```\n'







def        A_     (						__SCREAMING_SNAKE_CASE						:  str			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  Dict=8 )				->       List[str]:





  """simple docstring"""

  __A		:     str												=		height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __A		:     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  __A				( _a			):
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    List[str]				,   ):



   """simple docstring"""





   super().__init__()

   self.register_modules(
       unet=_A				,   scheduler=_A				,   movq=_A				,   )
   __A		:     Union[str, Any]												=		2 ** (len(self.movq.config.block_out_channels       ) - 1)
 def 		lowercase_(   self			:    List[Any]				,   lowerCamelCase			:    Union[str, Any]				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    Dict				,   lowerCamelCase			:    int				,   lowerCamelCase			:    Optional[Any]       ):



   """simple docstring"""





   if latents is None:
     __A		:     Optional[int]												=		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}"       )
     __A		:     Dict												=		latents.to(_A       )

   __A		:     Union[str, Any]												=		latents * scheduler.init_noise_sigma
   return latents
 def 		lowercase_(   self			:    Union[str, Any]				,   lowerCamelCase			:    str=0       ):



   """simple docstring"""





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

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

   __A		:     List[str]												=		[
       self.unet,
       self.movq,
   ]
   for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
       cpu_offload(_A				,   _A       )
 def 		lowercase_(   self			:    int				,   lowerCamelCase			:    Tuple=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."""       )

   __A		:     Any												=		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)

   __A		:     int												=		None
   for cpu_offloaded_model in [self.unet, self.movq]:
     __A							,					__A		:     Any												=		cpu_offload_with_hook(_A				,   _A				,   prev_module_hook=_A       )

   # We'll offload the last model manually.
   __A		:     List[Any]												=		hook
 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 		lowercase_(   self			:    Dict       ):



   """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			:    str				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Optional[Any]				,   lowerCamelCase			:    Dict = 5_12				,   lowerCamelCase			:    Optional[int] = 5_12				,   lowerCamelCase			:    Dict = 1_00				,   lowerCamelCase			:    List[Any] = 4.0				,   lowerCamelCase			:    str = 1				,   lowerCamelCase			:    Dict = None				,   lowerCamelCase			:    Optional[int] = None				,   lowerCamelCase			:    Dict = "pil"				,   lowerCamelCase			:    Optional[int] = True				,   ):



   """simple docstring"""





   __A		:     int												=		self._execution_device

   __A		:     List[str]												=		guidance_scale > 1.0

   if isinstance(_A				,   _A       ):
     __A		:     Dict												=		torch.cat(_A				,   dim=0       )
   __A		:     Optional[Any]												=		image_embeds.shape[0] * num_images_per_prompt
   if isinstance(_A				,   _A       ):
     __A		:     Optional[Any]												=		torch.cat(_A				,   dim=0       )

   if do_classifier_free_guidance:
     __A		:     List[Any]												=		image_embeds.repeat_interleave(_A				,   dim=0       )
     __A		:     List[str]												=		negative_image_embeds.repeat_interleave(_A				,   dim=0       )

   __A		:     Tuple												=		torch.cat([negative_image_embeds, image_embeds]				,   dim=0       ).to(dtype=self.unet.dtype				,   device=_A       )

   self.scheduler.set_timesteps(_A				,   device=_A       )
   __A		:     List[Any]												=		self.scheduler.timesteps

   __A		:     str												=		self.unet.config.in_channels

   __A							,					__A		:     Optional[Any]												=		downscale_height_and_width(_A				,   _A				,   self.movq_scale_factor       )

   # create initial latent
   __A		:     Dict												=		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
     __A		:     Tuple												=		torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

     __A		:     List[Any]												=		{"""image_embeds""": image_embeds}
     __A		:     str												=		self.unet(
         sample=_A				,   timestep=_A				,   encoder_hidden_states=_A				,   added_cond_kwargs=_A				,   return_dict=_A				,   )[0]

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

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

   if output_type == "pil":
     __A		:     Union[str, Any]												=		self.numpy_to_pil(_A       )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=_A       )





'''simple docstring'''
from packaging import version

from .import_utils import is_accelerate_available


if is_accelerate_available():
     import accelerate







def        A_     (						__SCREAMING_SNAKE_CASE						:  Dict )				->       Optional[int]:





  """simple docstring"""

  if not is_accelerate_available():
    return method
  __A		:     int												=		version.parse(accelerate.__version__ ).base_version
  if version.parse(__SCREAMING_SNAKE_CASE ) < version.parse("""0.17.0""" ):
    return method

  def wrapper(self						:  str			,     *__SCREAMING_SNAKE_CASE						:  Tuple			,     **__SCREAMING_SNAKE_CASE						:  Tuple ):
    if hasattr(self			,     """_hf_hook""" ) and hasattr(self._hf_hook			,     """pre_forward""" ):
      self._hf_hook.pre_forward(self )
    return method(self			,     *__SCREAMING_SNAKE_CASE			,     **__SCREAMING_SNAKE_CASE )

  return wrapper



