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

_a   :	Dict 							=   "\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 _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=8						)					->       List[str]:





  """simple docstring"""


  __UpperCAmelCase  :				Union[str, Any]       				=		height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __UpperCAmelCase  :				str       				=		width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor






class 		__A       (_UpperCAmelCase   ):






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

         self.register_modules(
             unet=lowerCamelCase_				,     scheduler=lowerCamelCase_				,     movq=lowerCamelCase_				,     )
         __UpperCAmelCase  :				str       				=		2 ** (len(self.movq.config.block_out_channels      ) - 1)






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):




         if latents is None:
           __UpperCAmelCase  :				Union[str, 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}"""      )
           __UpperCAmelCase  :				List[str]       				=		latents.to(lowerCamelCase_      )

         __UpperCAmelCase  :				Union[str, Any]       				=		latents * scheduler.init_noise_sigma
         return latents






       def 			_snake_case						(      self				,     UpperCamelCase_=0      ):




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

         __UpperCAmelCase  :				Union[str, Any]       				=		torch.device(f"""cuda:{gpu_id}"""      )

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






       def 			_snake_case						(      self				,     UpperCamelCase_=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."      )

         __UpperCAmelCase  :				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)

         __UpperCAmelCase  :				str       				=		None
         for cpu_offloaded_model in [self.unet, self.movq]:
           __UpperCAmelCase  :				Dict       				=		cpu_offload_with_hook(lowerCamelCase_				,     lowerCamelCase_				,     prev_module_hook=lowerCamelCase_      )

         # We'll offload the last model manually.
         __UpperCAmelCase  :				str       				=		hook






       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 			_snake_case						(      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				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_ = 5_12				,     UpperCamelCase_ = 5_12				,     UpperCamelCase_ = 1_00				,     UpperCamelCase_ = 4.0				,     UpperCamelCase_ = 1				,     UpperCamelCase_ = None				,     UpperCamelCase_ = None				,     UpperCamelCase_ = "pil"				,     UpperCamelCase_ = True				,     ):
         __UpperCAmelCase  :				Any       				=		self._execution_device

         __UpperCAmelCase  :				Optional[int]       				=		guidance_scale > 1.0

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

         if do_classifier_free_guidance:
           __UpperCAmelCase  :				Optional[int]       				=		image_embeds.repeat_interleave(lowerCamelCase_				,     dim=0      )
           __UpperCAmelCase  :				str       				=		negative_image_embeds.repeat_interleave(lowerCamelCase_				,     dim=0      )

         __UpperCAmelCase  :				Any       				=		torch.cat([negative_image_embeds, image_embeds]				,     dim=0      ).to(dtype=self.unet.dtype				,     device=lowerCamelCase_      )

         self.scheduler.set_timesteps(lowerCamelCase_				,     device=lowerCamelCase_      )
         __UpperCAmelCase  :				str       				=		self.scheduler.timesteps

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

         __UpperCAmelCase  :				List[str]       				=		downscale_height_and_width(lowerCamelCase_				,     lowerCamelCase_				,     self.movq_scale_factor      )

         # create initial latent
         __UpperCAmelCase  :				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
           __UpperCAmelCase  :				int       				=		torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

           __UpperCAmelCase  :				Union[str, Any]       				=		{'''image_embeds''': image_embeds}
           __UpperCAmelCase  :				Optional[int]       				=		self.unet(
               sample=lowerCamelCase_				,     timestep=lowerCamelCase_				,     encoder_hidden_states=lowerCamelCase_				,     added_cond_kwargs=lowerCamelCase_				,     return_dict=lowerCamelCase_				,     )[0]

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

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

         if output_type == "pil":
           __UpperCAmelCase  :				str       				=		self.numpy_to_pil(lowerCamelCase_      )

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=lowerCamelCase_      )







'''simple docstring'''




from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments

def _lowercase	(      )					->       Dict:





       """simple docstring"""


       __UpperCAmelCase  :				str       				=		HfArgumentParser(lowerCamelCase__						)
       __UpperCAmelCase  :				Optional[Any]       				=		parser.parse_args_into_dataclasses()[0]
       __UpperCAmelCase  :				Any       				=		TensorFlowBenchmark(args=lowerCamelCase__						)
       try:
              __UpperCAmelCase  :				List[Any]       				=		parser.parse_args_into_dataclasses()[0]
       except ValueError as e:
              __UpperCAmelCase  :				str       				=		"Arg --no_{0} is no longer used, please use --no-{0} instead."
              __UpperCAmelCase  :				Tuple       				=		" ".join(str(lowerCamelCase__						).split(" "						)[:-1]						)
              __UpperCAmelCase  :				Any       				=		""
              __UpperCAmelCase  :				List[Any]       				=		eval(str(lowerCamelCase__						).split(" "						)[-1]						)
              __UpperCAmelCase  :				Optional[int]       				=		[]
              for arg in depreciated_args:
                     # arg[2:] removes '--'
                     if arg[2:] in TensorFlowBenchmark.deprecated_args:
                            # arg[5:] removes '--no_'
                            full_error_msg += arg_error_msg.format(arg[5:]						)
                     else:
                            wrong_args.append(lowerCamelCase__						)
              if len(lowerCamelCase__						) > 0:
                     __UpperCAmelCase  :				Union[str, Any]       				=		full_error_msg + begin_error_msg + str(lowerCamelCase__						)
              raise ValueError(lowerCamelCase__						)
       benchmark.run()


if __name__ == "__main__":
   main()






