





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							:			List[str]         =			"\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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=8				)							-> List[str]:




    """simple docstring"""




    _A     =							height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    _A     =							width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor







class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


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

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




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_				,     snake_case_							):
         if latents is None:
             _A     =							randn_tensor(snake_case_				,     generator=snake_case_				,     device=snake_case_				,     dtype=snake_case_							)
         else:
             if latents.shape != shape:
                 raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"							)
             _A     =							latents.to(snake_case_							)

         _A     =							latents * scheduler.init_noise_sigma
         return latents




     def        lowerCAmelCase__		(    self				,     snake_case_=0							):
         if is_accelerate_available():
             from accelerate import cpu_offload
         else:
             raise ImportError('Please install accelerate via `pip install accelerate`'							)

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

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




     def        lowerCAmelCase__		(    self				,     snake_case_=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.'							)

         _A     =							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)

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

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




     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def        lowerCAmelCase__		(    self							):
         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				,     snake_case_				,     snake_case_				,     snake_case_ = 512				,     snake_case_ = 512				,     snake_case_ = 100				,     snake_case_ = 4.0				,     snake_case_ = 1				,     snake_case_ = None				,     snake_case_ = None				,     snake_case_ = "pil"				,     snake_case_ = True				,     ):
         _A     =							self._execution_device

         _A     =							guidance_scale > 1.0

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

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

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

         self.scheduler.set_timesteps(snake_case_				,     device=snake_case_							)
         _A     =							self.scheduler.timesteps

         _A     =							self.unet.config.in_channels

         _A,			_A     =							downscale_height_and_width(snake_case_				,     snake_case_				,     self.movq_scale_factor							)

         # create initial latent
         _A     =							self.prepare_latents(
             (batch_size, num_channels_latents, height, width)				,     image_embeds.dtype				,     snake_case_				,     snake_case_				,     snake_case_				,     self.scheduler				,     )

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

             _A     =							{'image_embeds': image_embeds}
             _A     =							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:
                 _A,			_A     =							noise_pred.split(latents.shape[1]				,     dim=1							)
                 _A,			_A     =							noise_pred.chunk(2							)
                 _A,			_A     =							variance_pred.chunk(2							)
                 _A     =							noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                 _A     =							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     =							noise_pred.split(latents.shape[1]				,     dim=1							)

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

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

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=snake_case_							)












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


__A							:			Optional[Any]         =			{}


try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A							:			Optional[int]         =			["MLukeTokenizer"]

if TYPE_CHECKING:
 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_mluke import MLukeTokenizer


else:
 import sys

 __A							:			List[str]         =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





