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

_a				:		Optional[int]	   =       '''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Tuple      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Dict=8     )	->				int:
  _lowerCAmelCase						:	List[str]        =					height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCAmelCase						:	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    ,					a__    ,					a__    ,					a__    ,					):
       super().__init__()

       self.register_modules(
           unet=__lowerCamelCase    ,					scheduler=__lowerCamelCase    ,					movq=__lowerCamelCase    ,					)
       _lowerCAmelCase						:	Tuple        =					2 ** (len(self.movq.config.block_out_channels  ) - 1)






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__    ,					a__  ):
       if latents is None:
         _lowerCAmelCase						:	Any        =					randn_tensor(__lowerCamelCase    ,					generator=__lowerCamelCase    ,					device=__lowerCamelCase    ,					dtype=__lowerCamelCase  )
       else:
         if latents.shape != shape:
           raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
         _lowerCAmelCase						:	Dict        =					latents.to(__lowerCamelCase  )

       _lowerCAmelCase						:	Tuple        =					latents * scheduler.init_noise_sigma
       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						:	Tuple        =					torch.device(F"cuda:{gpu_id}"  )

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






     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						:	Any        =					torch.device(F"cuda:{gpu_id}"  )

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

       _lowerCAmelCase						:	Tuple        =					None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase						:	Any        =					cpu_offload_with_hook(__lowerCamelCase    ,					__lowerCamelCase    ,					prev_module_hook=__lowerCamelCase  )

       # We'll offload the last model manually.
       _lowerCAmelCase						:	int        =					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(__lowerCamelCase    ,					"""_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(__lowerCamelCase  )
     def __call__(		self    ,					a__    ,					a__    ,					a__ = 512    ,					a__ = 512    ,					a__ = 100    ,					a__ = 4.0    ,					a__ = 1    ,					a__ = None    ,					a__ = None    ,					a__ = "pil"    ,					a__ = True    ,					):
       _lowerCAmelCase						:	List[Any]        =					self._execution_device

       _lowerCAmelCase						:	Any        =					guidance_scale > 1.0

       if isinstance(__lowerCamelCase    ,					__lowerCamelCase  ):
         _lowerCAmelCase						:	List[str]        =					torch.cat(__lowerCamelCase    ,					dim=0  )
       _lowerCAmelCase						:	int        =					image_embeds.shape[0] * num_images_per_prompt
       if isinstance(__lowerCamelCase    ,					__lowerCamelCase  ):
         _lowerCAmelCase						:	List[Any]        =					torch.cat(__lowerCamelCase    ,					dim=0  )

       if do_classifier_free_guidance:
         _lowerCAmelCase						:	List[Any]        =					image_embeds.repeat_interleave(__lowerCamelCase    ,					dim=0  )
         _lowerCAmelCase						:	Union[str, Any]        =					negative_image_embeds.repeat_interleave(__lowerCamelCase    ,					dim=0  )

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

       self.scheduler.set_timesteps(__lowerCamelCase    ,					device=__lowerCamelCase  )
       _lowerCAmelCase						:	str        =					self.scheduler.timesteps

       _lowerCAmelCase						:	Optional[Any]        =					self.unet.config.in_channels

       _lowerCAmelCase						:	Union[str, Any]        =					downscale_height_and_width(__lowerCamelCase    ,					__lowerCamelCase    ,					self.movq_scale_factor  )

       # create initial latent
       _lowerCAmelCase						:	Optional[Any]        =					self.prepare_latents(
           (batch_size, num_channels_latents, height, width)    ,					image_embeds.dtype    ,					__lowerCamelCase    ,					__lowerCamelCase    ,					__lowerCamelCase    ,					self.scheduler    ,					)

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

         _lowerCAmelCase						:	Optional[int]        =					{"""image_embeds""": image_embeds}
         _lowerCAmelCase						:	Dict        =					self.unet(
             sample=__lowerCamelCase    ,					timestep=__lowerCamelCase    ,					encoder_hidden_states=__lowerCamelCase    ,					added_cond_kwargs=__lowerCamelCase    ,					return_dict=__lowerCamelCase    ,					)[0]

         if do_classifier_free_guidance:
           _lowerCAmelCase						:	List[str]        =					noise_pred.split(latents.shape[1]    ,					dim=1  )
           _lowerCAmelCase						:	Tuple        =					noise_pred.chunk(2  )
           _lowerCAmelCase						:	str        =					variance_pred.chunk(2  )
           _lowerCAmelCase						:	Optional[Any]        =					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						:	Tuple        =					noise_pred.split(latents.shape[1]    ,					dim=1  )

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

       if output_type == "pil":
         _lowerCAmelCase						:	Optional[Any]        =					self.numpy_to_pil(__lowerCamelCase  )

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=__lowerCamelCase  )





"""simple docstring"""




_a				:		Any	   =       '\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

_a				:		List[Any]	   =       [{'type': 'code', 'content': INSTALL_CONTENT}]
_a				:		Union[str, Any]	   =       {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}




