


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

UpperCAmelCase__      =							'''
    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 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   int=8   ):






			"""simple docstring"""




			__A=  height // scale_factor**2
			if height % scale_factor**2 != 0:
						new_height += 1
			__A=  width // scale_factor**2
			if width % scale_factor**2 != 0:
						new_width += 1
			return new_height * scale_factor, new_width * scale_factor






def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   Any=512,_SCREAMING_SNAKE_CASE				:   Optional[Any]=512   ):






			"""simple docstring"""




			__A=  pil_image.resize((w, h),resample=Image.BICUBIC,reducing_gap=1   )
			__A=  np.array(pil_image.convert('RGB'   )   )
			__A=  arr.astype(np.floataa   ) / 127.5 - 1
			__A=  np.transpose(_SCREAMING_SNAKE_CASE,[2, 0, 1]   )
			__A=  torch.from_numpy(_SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
			return image






class 	a__				(				a_      ):





			'''simple docstring'''

			def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : UNetaDConditionModel ,						lowerCAmelCase_       : DDPMScheduler ,						lowerCAmelCase_       : VQModel ,						)			->		Optional[Any]:
						super().__init__()

						self.register_modules(
						    unet=lowerCAmelCase_ ,						scheduler=lowerCAmelCase_ ,						movq=lowerCAmelCase_ ,						)
						__A=  2 ** (len(self.movq.config.block_out_channels  ) - 1)
			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		Union[str, Any]:
						# get the original timestep using init_timestep
						__A=  min(int(num_inference_steps * strength  ) ,						lowerCAmelCase_  )

						__A=  max(num_inference_steps - init_timestep ,						0  )
						__A=  self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start
			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Optional[int] ,						lowerCAmelCase_       : Any ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : int ,						lowerCAmelCase_       : Optional[Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : int=None  )			->		List[Any]:
						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_  )}"""  )

						__A=  image.to(device=lowerCAmelCase_ ,						dtype=lowerCAmelCase_  )

						__A=  batch_size * num_images_per_prompt

						if image.shape[1] == 4:
									__A=  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_  ):
												__A=  [
												    self.movq.encode(image[i : i + 1]  ).latent_dist.sample(generator[i]  ) for i in range(lowerCAmelCase_  )
												]
												__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
									else:
												__A=  self.movq.encode(lowerCAmelCase_  ).latent_dist.sample(lowerCAmelCase_  )

									__A=  self.movq.config.scaling_factor * init_latents

						__A=  torch.cat([init_latents] ,						dim=0  )

						__A=  init_latents.shape
						__A=  randn_tensor(lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						device=lowerCAmelCase_ ,						dtype=lowerCAmelCase_  )

						# get latents
						__A=  self.scheduler.add_noise(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )

						__A=  init_latents

						return latents
			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : Any=0  )			->		Tuple:
						if is_accelerate_available():
									from accelerate import cpu_offload
						else:
									raise ImportError('Please install accelerate via `pip install accelerate`'  )

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

						__A=  [
						    self.unet,
						    self.movq,
						]
						for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
												cpu_offload(lowerCAmelCase_ ,						lowerCAmelCase_  )
			def 				lowerCAmelCase    (     self       : int ,						lowerCAmelCase_       : List[Any]=0  )			->		int:
						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.'  )

						__A=  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)

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

						# We'll offload the last model manually.
						__A=  hook
			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Union[str, Any]:
						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       : Optional[int] ,						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 = 512 ,						lowerCAmelCase_       : int = 512 ,						lowerCAmelCase_       : int = 100 ,						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 ,						)			->		Optional[Any]:
						__A=  self._execution_device

						__A=  guidance_scale > 1.0

						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )
						if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
									__A=  torch.cat(lowerCAmelCase_ ,						dim=0  )

						__A=  image_embeds.shape[0]

						if do_classifier_free_guidance:
									__A=  image_embeds.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
									__A=  negative_image_embeds.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
									__A=  hint.repeat_interleave(lowerCAmelCase_ ,						dim=0  )

						__A=  torch.cat([negative_image_embeds, image_embeds] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=lowerCAmelCase_  )
						__A=  torch.cat([hint, hint] ,						dim=0  ).to(dtype=self.unet.dtype ,						device=lowerCAmelCase_  )

						if not isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
									__A=  [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"""  )

						__A=  torch.cat([prepare_image(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  ) for i in image] ,						dim=0  )
						__A=  image.to(dtype=image_embeds.dtype ,						device=lowerCAmelCase_  )

						__A=  self.movq.encode(lowerCAmelCase_  )['latents']
						__A=  latents.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
						self.scheduler.set_timesteps(lowerCAmelCase_ ,						device=lowerCAmelCase_  )
						__A,  __A=  self.get_timesteps(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
						__A=  timesteps[:1].repeat(batch_size * num_images_per_prompt  )
						__A,  __A=  downscale_height_and_width(lowerCAmelCase_ ,						lowerCAmelCase_ ,						self.movq_scale_factor  )
						__A=  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
									__A=  torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents

									__A=  {'image_embeds': image_embeds, 'hint': hint}
									__A=  self.unet(
									    sample=lowerCAmelCase_ ,						timestep=lowerCAmelCase_ ,						encoder_hidden_states=lowerCAmelCase_ ,						added_cond_kwargs=lowerCAmelCase_ ,						return_dict=lowerCAmelCase_ ,						)[0]

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

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

									__A=  self.scheduler.step(
									    lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						)[0]

						# post-processing
						__A=  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"]:
									__A=  image * 0.5 + 0.5
									__A=  image.clamp(0 ,						1  )
									__A=  image.cpu().permute(0 ,						2 ,						3 ,						1  ).float().numpy()

						if output_type == "pil":
									__A=  self.numpy_to_pil(lowerCAmelCase_  )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=lowerCAmelCase_  )







'''simple docstring'''





from typing import Any
class 	a__				:





			'''simple docstring'''

			def __init__(     self       : Any  )			->		int:
						__A=  []
						__A=  0
			def __str__(     self       : Tuple  )			->		Dict:
						__A=  '<' + str(self.stack  )[1:-1] + '>'
						return printed
			def 				lowerCAmelCase    (     self       : Tuple ,						lowerCAmelCase_       : Any  )			->		None:
						self.stack.append(lowerCAmelCase_  )
						__A=  self.length + 1
			def 				lowerCAmelCase    (     self       : int  )			->		Any:
						self.rotate(1  )
						__A=  self.stack[self.length - 1]
						__A=  self.stack[:-1]
						self.rotate(self.length - 1  )
						__A=  self.length - 1
						return dequeued
			def 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : int  )			->		None:
						for _ in range(lowerCAmelCase_  ):
									__A=  self.stack[0]
									__A=  self.stack[1:]
									self.put(lowerCAmelCase_  )
									__A=  self.length - 1
			def 				lowerCAmelCase    (     self       : Tuple  )			->		Any:
						__A=  self.get()
						self.put(lowerCAmelCase_  )
						self.rotate(self.length - 1  )
						return front
			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
						return self.length



