'''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 :						Optional[int]							= 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 _lowercase						(   lowerCamelCase__				,     lowerCamelCase__				,     lowerCamelCase__=8    )       ->							Optional[Any]:





       """simple docstring"""


       __UpperCAmelCase	:					List[Any]												=		height // scale_factor**2
       if height % scale_factor**2 != 0:
              new_height += 1
       __UpperCAmelCase	:					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							(__magic_name__ ):






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

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






       def       _snake_case    (   self					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_					,  UpperCamelCase_   ):
              if latents is None:
                     __UpperCAmelCase	:					List[Any]												=		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}"""   )
                     __UpperCAmelCase	:					Union[str, Any]												=		latents.to(a_   )

              __UpperCAmelCase	:					Optional[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	:					List[Any]												=		torch.device(f"""cuda:{gpu_id}"""   )

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






       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	:					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)

              __UpperCAmelCase	:					List[Any]												=		None
              for cpu_offloaded_model in [self.unet, self.movq]:
                     __UpperCAmelCase					,			__UpperCAmelCase	:					Union[str, Any]												=		cpu_offload_with_hook(a_					,  a_					,  prev_module_hook=a_   )

              # 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(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					,  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	:					int												=		guidance_scale > 1.0

              if isinstance(a_					,  a_   ):
                     __UpperCAmelCase	:					Union[str, Any]												=		torch.cat(a_					,  dim=0   )
              __UpperCAmelCase	:					Any												=		image_embeds.shape[0] * num_images_per_prompt
              if isinstance(a_					,  a_   ):
                     __UpperCAmelCase	:					Tuple												=		torch.cat(a_					,  dim=0   )

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

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

              self.scheduler.set_timesteps(a_					,  device=a_   )
              __UpperCAmelCase	:					List[Any]												=		self.scheduler.timesteps

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

              __UpperCAmelCase					,			__UpperCAmelCase	:					List[Any]												=		downscale_height_and_width(a_					,  a_					,  self.movq_scale_factor   )

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

                     __UpperCAmelCase	:					Dict												=		{"image_embeds": image_embeds}
                     __UpperCAmelCase	:					Union[str, Any]												=		self.unet(
                         sample=a_					,  timestep=a_					,  encoder_hidden_states=a_					,  added_cond_kwargs=a_					,  return_dict=a_					,  )[0]

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

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

              if output_type == "pil":
                     __UpperCAmelCase	:					Optional[int]												=		self.numpy_to_pil(a_   )

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=a_   )











import random
from typing import Any







def     UpperCAmelCase_		(					snake_case__					)	->     list[Any]:



				"""simple docstring"""





				for _ in range(len(snake_case__					)					):
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__		  =  random.randint(0    ,			len(snake_case__					) - 1					)
								lowerCAmelCase__  ,   lowerCAmelCase__		  =  data[b], data[a]
				return data


if __name__ == "__main__":
	_lowerCAmelCase	:   int    =   [0, 1, 2, 3, 4, 5, 6, 7]
	_lowerCAmelCase	:   Optional[Any]    =   ["python", "says", "hello", "!"]
	print("Fisher-Yates Shuffle:")
	print("List", integers, strings)
	print("FY Shuffle", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
