




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

__A						:							Tuple							=  '''
    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 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=8   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			SCREAMING_SNAKE_CASE           =					width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor




class 			UpperCAmelCase_      (   UpperCamelCase__       ):




							'''simple docstring'''



							def __init__(					self       :	List[str]       ,     a       :	List[str]       ,     a       :	Tuple       ,     a       :	Any       ,     )			->					Union[str, Any]:
										super().__init__()

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





							def    _UpperCAmelCase     (					self       :	int       ,     a       :	List[str]       ,     a       :	int       ,     a       :	str       ,     a       :	List[str]       ,     a       :	Optional[int]       ,     a       :	Dict			)			->					Tuple:
										if latents is None:
													SCREAMING_SNAKE_CASE           =					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}"""			)
													SCREAMING_SNAKE_CASE           =					latents.to(a			)

										SCREAMING_SNAKE_CASE           =					latents * scheduler.init_noise_sigma
										return latents





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	int=0			)			->					Any:
										if is_accelerate_available():
													from accelerate import cpu_offload
										else:
													raise ImportError("""Please install accelerate via `pip install accelerate`"""			)

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

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





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	int=0			)			->					Union[str, Any]:
										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."""			)

										SCREAMING_SNAKE_CASE           =					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)

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

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





							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					Union[str, Any]:
										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       :	Any       ,     a       :	Optional[int]       ,     a       :	Union[str, Any]       ,     a       :	int = 512       ,     a       :	Optional[Any] = 512       ,     a       :	Union[str, Any] = 100       ,     a       :	Dict = 4.0       ,     a       :	List[str] = 1       ,     a       :	str = None       ,     a       :	int = None       ,     a       :	Any = "pil"       ,     a       :	Optional[Any] = True       ,     )			->					int:
										SCREAMING_SNAKE_CASE           =					self._execution_device

										SCREAMING_SNAKE_CASE           =					guidance_scale > 1.0

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

										if do_classifier_free_guidance:
													SCREAMING_SNAKE_CASE           =					image_embeds.repeat_interleave(a       ,     dim=0			)
													SCREAMING_SNAKE_CASE           =					negative_image_embeds.repeat_interleave(a       ,     dim=0			)

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

										self.scheduler.set_timesteps(a       ,     device=a			)
										SCREAMING_SNAKE_CASE           =					self.scheduler.timesteps

										SCREAMING_SNAKE_CASE           =					self.unet.config.in_channels

										SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					downscale_height_and_width(a       ,     a       ,     self.movq_scale_factor			)

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

													SCREAMING_SNAKE_CASE           =					{"""image_embeds""": image_embeds}
													SCREAMING_SNAKE_CASE           =					self.unet(
													    sample=a       ,     timestep=a       ,     encoder_hidden_states=a       ,     added_cond_kwargs=a       ,     return_dict=a       ,     )[0]

													if do_classifier_free_guidance:
																SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					noise_pred.split(latents.shape[1]       ,     dim=1			)
																SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					noise_pred.chunk(2			)
																SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					variance_pred.chunk(2			)
																SCREAMING_SNAKE_CASE           =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																SCREAMING_SNAKE_CASE           =					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"]
													):
																SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE           =					noise_pred.split(latents.shape[1]       ,     dim=1			)

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

										if output_type == "pil":
													SCREAMING_SNAKE_CASE           =					self.numpy_to_pil(a			)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=a			)












def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			if not all(char in """01""" for char in bin_string   ):
						raise ValueError("""Non-binary value was passed to the function"""   )
			if not bin_string:
						raise ValueError("""Empty string was passed to the function"""   )
			SCREAMING_SNAKE_CASE           =					""""""
			while len(SCREAMING_SNAKE_CASE   ) % 3 != 0:
						SCREAMING_SNAKE_CASE           =					"""0""" + bin_string
			SCREAMING_SNAKE_CASE           =					[
			    bin_string[index : index + 3]
			    for index in range(len(SCREAMING_SNAKE_CASE   )   )
			    if index % 3 == 0
			]
			for bin_group in bin_string_in_3_list:
						SCREAMING_SNAKE_CASE           =					0
						for index, val in enumerate(SCREAMING_SNAKE_CASE   ):
									oct_val += int(2 ** (2 - index) * int(SCREAMING_SNAKE_CASE   )   )
						oct_string += str(SCREAMING_SNAKE_CASE   )
			return oct_string


if __name__ == "__main__":
		from doctest import testmod

		testmod()






