


'''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,
)


lowerCAmelCase				:Optional[Any]        =      logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCAmelCase				:Union[str, Any]        =      '''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)["depth"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline("depth-estimation")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")


        >>> img = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/cat.png"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")

        >>> prompt = "A robot, 4k photo"
        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"

        >>> generator = torch.Generator(device="cuda").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save("robot_cat.png")
        ```
'''





def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		,		lowerCAmelCase    :     List[Any]		,		lowerCAmelCase    :     Union[str, Any]=8		):





	"""simple docstring"""







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





class _lowerCamelCase	(						__lowercase							):



		'''simple docstring'''







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

			self.register_modules(
			    unet=_a  ,					scheduler=_a  ,					movq=_a  ,					)
			__magic_name__	:   List[Any]					     =					2 ** (len(self.movq.config.block_out_channels      ) - 1)







		def        __lowerCAmelCase		( self		:						List[Any]  ,					_A		:						List[Any]  ,					_A		:						List[str]  ,					_A		:						Union[str, Any]  ,					_A		:						Tuple  ,					_A		:						Union[str, Any]  ,					_A		:						Optional[Any]      )  ->			List[str]:
			if latents is None:
				__magic_name__	:   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}'      )
				__magic_name__	:   Union[str, Any]					     =					latents.to(_a      )

			__magic_name__	:   Dict					     =					latents * scheduler.init_noise_sigma
			return latents







		def        __lowerCAmelCase		( self		:						int  ,					_A		:						Union[str, Any]=0      )  ->			List[str]:
			if is_accelerate_available():
				from accelerate import cpu_offload
			else:
				raise ImportError('Please install accelerate via `pip install accelerate`'      )

			__magic_name__	:   List[str]					     =					torch.device(F'cuda:{gpu_id}'      )

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







		def        __lowerCAmelCase		( self		:						Optional[int]  ,					_A		:						str=0      )  ->			List[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.'      )

			__magic_name__	:   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)

			__magic_name__	:   List[Any]					     =					None
			for cpu_offloaded_model in [self.unet, self.movq]:
				__magic_name__	:   int					     =					cpu_offload_with_hook(_a  ,					_a  ,					prev_module_hook=_a      )

			# We'll offload the last model manually.
			__magic_name__	:   Optional[Any]					     =					hook







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

			__magic_name__	:   int					     =					guidance_scale > 1.0

			if isinstance(_a  ,					_a      ):
				__magic_name__	:   Optional[int]					     =					torch.cat(_a  ,					dim=0      )
			if isinstance(_a  ,					_a      ):
				__magic_name__	:   Union[str, Any]					     =					torch.cat(_a  ,					dim=0      )
			if isinstance(_a  ,					_a      ):
				__magic_name__	:   Optional[Any]					     =					torch.cat(_a  ,					dim=0      )

			__magic_name__	:   Union[str, Any]					     =					image_embeds.shape[0] * num_images_per_prompt

			if do_classifier_free_guidance:
				__magic_name__	:   Optional[Any]					     =					image_embeds.repeat_interleave(_a  ,					dim=0      )
				__magic_name__	:   Any					     =					negative_image_embeds.repeat_interleave(_a  ,					dim=0      )
				__magic_name__	:   Union[str, Any]					     =					hint.repeat_interleave(_a  ,					dim=0      )

			__magic_name__	:   Optional[Any]					     =					torch.cat([negative_image_embeds, image_embeds]  ,					dim=0      ).to(dtype=self.unet.dtype  ,					device=_a      )
			__magic_name__	:   Tuple					     =					torch.cat([hint, hint]  ,					dim=0      ).to(dtype=self.unet.dtype  ,					device=_a      )
			self.scheduler.set_timesteps(_a  ,					device=_a      )
			__magic_name__	:   Optional[Any]					     =					self.scheduler.timesteps

			__magic_name__	:   str					     =					self.movq.config.latent_channels

			__magic_name__	:   Union[str, Any]					     =					downscale_height_and_width(_a  ,					_a  ,					self.movq_scale_factor      )

			# create initial latent
			__magic_name__	:   Optional[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
				__magic_name__	:   Dict					     =					torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

				__magic_name__	:   Union[str, Any]					     =					{'''image_embeds''': image_embeds, '''hint''': hint}
				__magic_name__	:   str					     =					self.unet(
				    sample=_a  ,					timestep=_a  ,					encoder_hidden_states=_a  ,					added_cond_kwargs=_a  ,					return_dict=_a  ,					)[0]

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

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

			if output_type == "pil":
				__magic_name__	:   Tuple					     =					self.numpy_to_pil(_a      )

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=_a      )



'''simple docstring'''

def lowerCamelCase      (      lowerCAmelCase    :     str		):





	"""simple docstring"""







	return "".join(chr(ord(lowerCAmelCase		) - 32		) if 'a' <= char <= 'z' else char for char in word		)


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

							testmod()