



from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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,
)


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

UpperCamelCase__	:     Tuple    =			'''
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline
        >>> 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 = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to("cuda")

        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.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)

        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)
        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)

        >>> images = pipe(
        ...     image=img,
        ...     strength=0.5,
        ...     image_embeds=img_emb.image_embeds,
        ...     negative_image_embeds=negative_emb.image_embeds,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

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


def     lowerCAmelCase_       (     _lowerCamelCase:       Union[str, Any]	,				_lowerCamelCase:       Tuple	,				_lowerCamelCase:       Any=8 ):
		__SCREAMING_SNAKE_CASE	:		int   	=		height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		__SCREAMING_SNAKE_CASE	:		List[Any]   	=		width // scale_factor**2
		if width % scale_factor**2 != 0:
				new_width += 1
		return new_height * scale_factor, new_width * scale_factor


def     lowerCAmelCase_       (     _lowerCamelCase:       int	,				_lowerCamelCase:       Tuple=5_12	,				_lowerCamelCase:       Dict=5_12 ):
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		pil_image.resize((w, h)	,				resample=Image.BICUBIC	,				reducing_gap=1 )
		__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		np.array(pil_image.convert("""RGB""" ) )
		__SCREAMING_SNAKE_CASE	:		str   	=		arr.astype(np.floataa ) / 1_27.5 - 1
		__SCREAMING_SNAKE_CASE	:		Tuple   	=		np.transpose(_lowerCamelCase	,				[2, 0, 1] )
		__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		torch.from_numpy(_lowerCamelCase ).unsqueeze(0 )
		return image






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			def __init__(							self					:			int							,					lowerCAmelCase__					:			UNetaDConditionModel							,					lowerCAmelCase__					:			DDPMScheduler							,					lowerCAmelCase__					:			VQModel							,					):





					"""simple docstring"""






					super().__init__()

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

			def UpperCamelCase__ (							self					:			List[Any]							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Tuple							,					lowerCAmelCase__					:			List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		min(int(num_inference_steps * strength						)							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		max(num_inference_steps - init_timestep							,					0						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.scheduler.timesteps[t_start:]

					return timesteps, num_inference_steps - t_start

			def UpperCamelCase__ (							self					:			Optional[int]							,					lowerCAmelCase__					:			Any							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Optional[int]							,					lowerCAmelCase__					:			List[Any]							,					lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			str=None						):





					"""simple docstring"""






					if not isinstance(lowerCAmelCase__							,					(torch.Tensor, PIL.Image.Image, list)						):
							raise ValueError(
							    F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__						)}"						)

					__SCREAMING_SNAKE_CASE	:		str   	=		image.to(device=lowerCAmelCase__							,					dtype=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		int   	=		batch_size * num_images_per_prompt

					if image.shape[1] == 4:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		image

					else:
							if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						) and len(lowerCAmelCase__						) != batch_size:
									raise ValueError(
									    F"You have passed a list of generators of length {len(lowerCAmelCase__						)}, but requested an effective batch"
									    F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

							elif isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
									__SCREAMING_SNAKE_CASE	:		int   	=		[
									    self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(lowerCAmelCase__						)
									]
									__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.cat(lowerCAmelCase__							,					dim=0						)
							else:
									__SCREAMING_SNAKE_CASE	:		Any   	=		self.movq.encode(lowerCAmelCase__						).latent_dist.sample(lowerCAmelCase__						)

							__SCREAMING_SNAKE_CASE	:		Dict   	=		self.movq.config.scaling_factor * init_latents

					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		torch.cat([init_latents]							,					dim=0						)

					__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		init_latents.shape
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		randn_tensor(lowerCAmelCase__							,					generator=lowerCAmelCase__							,					device=lowerCAmelCase__							,					dtype=lowerCAmelCase__						)

					# get latents
					__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.scheduler.add_noise(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		init_latents

					return latents

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Union[str, Any]=0						):





					"""simple docstring"""






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

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.device(F"cuda:{gpu_id}"						)

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

			def UpperCamelCase__ (							self					:			Optional[Any]							,					lowerCAmelCase__					:			Optional[Any]=0						):





					"""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."""						)

					__SCREAMING_SNAKE_CASE	:		List[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)

					__SCREAMING_SNAKE_CASE	:		Tuple   	=		None
					for cpu_offloaded_model in [self.unet, self.movq]:
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		cpu_offload_with_hook(lowerCAmelCase__							,					lowerCAmelCase__							,					prev_module_hook=lowerCAmelCase__						)

					# We'll offload the last model manually.
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		hook

			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def UpperCamelCase__ (							self					:			Tuple						):





					"""simple docstring"""






					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					:			Any							,					lowerCAmelCase__					:			Union[torch.FloatTensor, List[torch.FloatTensor]]							,					lowerCAmelCase__					:			Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]							,					lowerCAmelCase__					:			Union[torch.FloatTensor, List[torch.FloatTensor]]							,					lowerCAmelCase__					:			torch.FloatTensor							,					lowerCAmelCase__					:			int = 5_1_2							,					lowerCAmelCase__					:			int = 5_1_2							,					lowerCAmelCase__					:			int = 1_0_0							,					lowerCAmelCase__					:			float = 4.0							,					lowerCAmelCase__					:			float = 0.3							,					lowerCAmelCase__					:			int = 1							,					lowerCAmelCase__					:			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,					lowerCAmelCase__					:			Optional[str] = "pil"							,					lowerCAmelCase__					:			bool = True							,					):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		self._execution_device

					__SCREAMING_SNAKE_CASE	:		List[str]   	=		guidance_scale > 1.0

					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		int   	=		torch.cat(lowerCAmelCase__							,					dim=0						)
					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		torch.cat(lowerCAmelCase__							,					dim=0						)
					if isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		torch.cat(lowerCAmelCase__							,					dim=0						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		image_embeds.shape[0]

					if do_classifier_free_guidance:
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		image_embeds.repeat_interleave(lowerCAmelCase__							,					dim=0						)
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		negative_image_embeds.repeat_interleave(lowerCAmelCase__							,					dim=0						)
							__SCREAMING_SNAKE_CASE	:		int   	=		hint.repeat_interleave(lowerCAmelCase__							,					dim=0						)

					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		torch.cat([negative_image_embeds, image_embeds]							,					dim=0						).to(dtype=self.unet.dtype							,					device=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		str   	=		torch.cat([hint, hint]							,					dim=0						).to(dtype=self.unet.dtype							,					device=lowerCAmelCase__						)

					if not isinstance(lowerCAmelCase__							,					lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		[image]
					if not all(isinstance(lowerCAmelCase__							,					(PIL.Image.Image, torch.Tensor)						) for i in image						):
							raise ValueError(
							    F"Input is in incorrect format: {[type(lowerCAmelCase__						) for i in image]}. Currently, we only support  PIL image and pytorch tensor"						)

					__SCREAMING_SNAKE_CASE	:		str   	=		torch.cat([prepare_image(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						) for i in image]							,					dim=0						)
					__SCREAMING_SNAKE_CASE	:		List[str]   	=		image.to(dtype=image_embeds.dtype							,					device=lowerCAmelCase__						)

					__SCREAMING_SNAKE_CASE	:		Any   	=		self.movq.encode(lowerCAmelCase__						)["""latents"""]
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		latents.repeat_interleave(lowerCAmelCase__							,					dim=0						)
					self.scheduler.set_timesteps(lowerCAmelCase__							,					device=lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		Any   	=		self.get_timesteps(lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		timesteps[:1].repeat(batch_size * num_images_per_prompt						)
					__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		downscale_height_and_width(lowerCAmelCase__							,					lowerCAmelCase__							,					self.movq_scale_factor						)
					__SCREAMING_SNAKE_CASE	:		int   	=		self.prepare_latents(
					    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					image_embeds.dtype							,					lowerCAmelCase__							,					lowerCAmelCase__						)
					for i, t in enumerate(self.progress_bar(lowerCAmelCase__						)						):
							# expand the latents if we are doing classifier free guidance
							__SCREAMING_SNAKE_CASE	:		List[str]   	=		torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

							__SCREAMING_SNAKE_CASE	:		str   	=		{"""image_embeds""": image_embeds, """hint""": hint}
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		self.unet(
							    sample=lowerCAmelCase__							,					timestep=lowerCAmelCase__							,					encoder_hidden_states=lowerCAmelCase__							,					added_cond_kwargs=lowerCAmelCase__							,					return_dict=lowerCAmelCase__							,					)[0]

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

							# compute the previous noisy sample x_t -> x_t-1

							__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		self.scheduler.step(
							    lowerCAmelCase__							,					lowerCAmelCase__							,					lowerCAmelCase__							,					generator=lowerCAmelCase__							,					)[0]

					# post-processing
					__SCREAMING_SNAKE_CASE	:		List[Any]   	=		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"]:
							__SCREAMING_SNAKE_CASE	:		Union[str, Any]   	=		image * 0.5 + 0.5
							__SCREAMING_SNAKE_CASE	:		List[Any]   	=		image.clamp(0							,					1						)
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		image.cpu().permute(0							,					2							,					3							,					1						).float().numpy()

					if output_type == "pil":
							__SCREAMING_SNAKE_CASE	:		Tuple   	=		self.numpy_to_pil(lowerCAmelCase__						)

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCAmelCase__						)




'''simple docstring'''




import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class 						_UpperCamelCase  (							lowerCamelCase__   ):


			'''simple docstring'''

			_A				:		int         =		(VQDiffusionScheduler,)

			def UpperCamelCase__ (							self					:			List[str]							,					**lowerCAmelCase__					:			Dict						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		int   	=		{
					    """num_vec_classes""": 4_0_9_7,
					    """num_train_timesteps""": 1_0_0,
					}

					config.update(**lowerCAmelCase__						)
					return config

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			int						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE	:		Dict   	=		4
					__SCREAMING_SNAKE_CASE	:		Tuple   	=		8
					__SCREAMING_SNAKE_CASE	:		int   	=		8

					__SCREAMING_SNAKE_CASE	:		Dict   	=		torch.randint(0							,					lowerCAmelCase__							,					(batch_size, height * width)						)

					return sample

			@property
			def UpperCamelCase__ (							self					:			str						):





					"""simple docstring"""






					assert False

			def UpperCamelCase__ (							self					:			Dict							,					lowerCAmelCase__					:			str						):





					"""simple docstring"""






					def model(lowerCAmelCase__					:			Optional[Any]							,					lowerCAmelCase__					:			Any							,					*lowerCAmelCase__					:			Optional[int]						):
							__SCREAMING_SNAKE_CASE	,						__SCREAMING_SNAKE_CASE	:		Optional[int]   	=		sample.shape
							__SCREAMING_SNAKE_CASE	:		Optional[Any]   	=		torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)						)
							__SCREAMING_SNAKE_CASE	:		str   	=		F.log_softmax(logits.double()							,					dim=1						).float()
							return return_value

					return model

			def UpperCamelCase__ (							self					:			Optional[int]						):





					"""simple docstring"""






					for timesteps in [2, 5, 1_0_0, 1_0_0_0]:
							self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			int						):





					"""simple docstring"""






					for num_vec_classes in [5, 1_0_0, 1_0_0_0, 4_0_0_0]:
							self.check_over_configs(num_vec_classes=lowerCAmelCase__						)

			def UpperCamelCase__ (							self					:			Any						):





					"""simple docstring"""






					for t in [0, 5_0, 9_9]:
							self.check_over_forward(time_step=lowerCAmelCase__						)



			def UpperCamelCase__ (							self					:			List[Any]						):





					"""simple docstring"""






					pass