



'''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				:    int     						='''
    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 						__UpperCamelCase				(							_lowercase,				_lowercase,				_lowercase=8					) ->			List[Any]:
	_lowercase					:       str       =		height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	_lowercase					:       List[str]       =		width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					def __init__(  self				:	int     ,  UpperCamelCase_				:	UNetaDConditionModel     ,  UpperCamelCase_				:	DDPMScheduler     ,  UpperCamelCase_				:	VQModel     ,  )  ->       Optional[Any]:



						'''simple docstring'''





						super().__init__()

						self.register_modules(
						    unet=UpperCamelCase_     ,  scheduler=UpperCamelCase_     ,  movq=UpperCamelCase_     ,  )
						_lowercase					:       Any       =		2 ** (len(self.movq.config.block_out_channels					) - 1)




					def 						__UpperCAmelCase     (  self				:	List[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	List[Any]     ,  UpperCamelCase_				:	Union[str, Any]     ,  UpperCamelCase_				:	Optional[Any]     ,  UpperCamelCase_				:	Any					)  ->       Optional[int]:



						'''simple docstring'''





						if latents is None:
							_lowercase					:       str       =		randn_tensor(UpperCamelCase_     ,  generator=UpperCamelCase_     ,  device=UpperCamelCase_     ,  dtype=UpperCamelCase_					)
						else:
							if latents.shape != shape:
								raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''					)
							_lowercase					:       Dict       =		latents.to(UpperCamelCase_					)

						_lowercase					:       Optional[Any]       =		latents * scheduler.init_noise_sigma
						return latents




					def 						__UpperCAmelCase     (  self				:	Optional[int]     ,  UpperCamelCase_				:	Union[str, Any]=0					)  ->       Dict:



						'''simple docstring'''





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

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

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




					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	List[str]=0					)  ->       Union[str, Any]:



						'''simple docstring'''





						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.'					)

						_lowercase					:       int       =		torch.device(F'''cuda:{gpu_id}'''					)

						if self.device.type != "cpu":
							self.to('cpu'     ,  silence_dtype_warnings=UpperCamelCase_					)
							torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

						_lowercase					:       Union[str, Any]       =		None
						for cpu_offloaded_model in [self.unet, self.movq]:
							_lowercase  ,    _lowercase					:       Optional[int]       =		cpu_offload_with_hook(UpperCamelCase_     ,  UpperCamelCase_     ,  prev_module_hook=UpperCamelCase_					)

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




					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def 						__UpperCAmelCase     (  self				:	Dict					)  ->       Optional[Any]:



						'''simple docstring'''





						if not hasattr(self.unet     ,  '_hf_hook'					):
							return self.device
						for module in self.unet.modules():
							if (
							    hasattr(UpperCamelCase_     ,  '_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(UpperCamelCase_					)
					def __call__(  self				:	Dict     ,  UpperCamelCase_				:	Union[torch.FloatTensor, List[torch.FloatTensor]]     ,  UpperCamelCase_				:	Union[torch.FloatTensor, List[torch.FloatTensor]]     ,  UpperCamelCase_				:	torch.FloatTensor     ,  UpperCamelCase_				:	int = 512     ,  UpperCamelCase_				:	int = 512     ,  UpperCamelCase_				:	int = 100     ,  UpperCamelCase_				:	float = 4.0     ,  UpperCamelCase_				:	int = 1     ,  UpperCamelCase_				:	Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  UpperCamelCase_				:	Optional[torch.FloatTensor] = None     ,  UpperCamelCase_				:	Optional[str] = "pil"     ,  UpperCamelCase_				:	bool = True     ,  )  ->       List[Any]:



						'''simple docstring'''





						_lowercase					:       Dict       =		self._execution_device

						_lowercase					:       List[str]       =		guidance_scale > 1.0

						if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Optional[int]       =		torch.cat(UpperCamelCase_     ,  dim=0					)
						if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Optional[Any]       =		torch.cat(UpperCamelCase_     ,  dim=0					)
						if isinstance(UpperCamelCase_     ,  UpperCamelCase_					):
							_lowercase					:       Tuple       =		torch.cat(UpperCamelCase_     ,  dim=0					)

						_lowercase					:       Dict       =		image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
							_lowercase					:       Optional[int]       =		image_embeds.repeat_interleave(UpperCamelCase_     ,  dim=0					)
							_lowercase					:       List[str]       =		negative_image_embeds.repeat_interleave(UpperCamelCase_     ,  dim=0					)
							_lowercase					:       int       =		hint.repeat_interleave(UpperCamelCase_     ,  dim=0					)

						_lowercase					:       Optional[int]       =		torch.cat([negative_image_embeds, image_embeds]     ,  dim=0					).to(dtype=self.unet.dtype     ,  device=UpperCamelCase_					)
						_lowercase					:       List[str]       =		torch.cat([hint, hint]     ,  dim=0					).to(dtype=self.unet.dtype     ,  device=UpperCamelCase_					)
						self.scheduler.set_timesteps(UpperCamelCase_     ,  device=UpperCamelCase_					)
						_lowercase					:       Optional[Any]       =		self.scheduler.timesteps

						_lowercase					:       List[Any]       =		self.movq.config.latent_channels

						_lowercase  ,    _lowercase					:       List[str]       =		downscale_height_and_width(UpperCamelCase_     ,  UpperCamelCase_     ,  self.movq_scale_factor					)

						# create initial latent
						_lowercase					:       Union[str, Any]       =		self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)     ,  image_embeds.dtype     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  UpperCamelCase_     ,  self.scheduler     ,  )

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

							_lowercase					:       Dict       =		{'image_embeds': image_embeds, 'hint': hint}
							_lowercase					:       str       =		self.unet(
							    sample=UpperCamelCase_     ,  timestep=UpperCamelCase_     ,  encoder_hidden_states=UpperCamelCase_     ,  added_cond_kwargs=UpperCamelCase_     ,  return_dict=UpperCamelCase_     ,  )[0]

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

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

						if output_type == "pil":
							_lowercase					:       Any       =		self.numpy_to_pil(UpperCamelCase_					)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=UpperCamelCase_					)





'''simple docstring'''

_A				:    Dict     						='''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

_A				:    Dict     						=[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
_A				:    Dict     						={
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}
