





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,
)


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

__lowercase							:Any							=	"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorEmb2EmbPipeline, KandinskyV22ControlnetImg2ImgPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorEmb2EmbPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetImg2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> img_emb = pipe_prior(prompt=prompt, image=img, strength=0.85, generator=generator)\n        >>> negative_emb = pipe_prior(prompt=negative_prior_prompt, image=img, strength=1, generator=generator)\n\n        >>> images = pipe(\n        ...     image=img,\n        ...     strength=0.5,\n        ...     image_embeds=img_emb.image_embeds,\n        ...     negative_image_embeds=negative_emb.image_embeds,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"







def    UpperCAmelCase				(  _lowerCamelCase   :	Any      , _lowerCamelCase   :	Dict      , _lowerCamelCase   :	Optional[int]=8  ):





						'''simple docstring'''


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







def    UpperCAmelCase				(  _lowerCamelCase   :	int      , _lowerCamelCase   :	Tuple=512      , _lowerCamelCase   :	Dict=512  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					List[Any] =       pil_image.resize((w, h)      , resample=Image.BICUBIC      , reducing_gap=1  )
						SCREAMING_SNAKE_CASE__  :					Tuple =       np.array(pil_image.convert("RGB"  )  )
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       arr.astype(np.floataa  ) / 1_2_7.5 - 1
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       np.transpose(_lowerCamelCase      , [2, 0, 1]  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       torch.from_numpy(_lowerCamelCase  ).unsqueeze(0  )
						return image



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  str				,						a					:  UNetaDConditionModel				,						a					:  DDPMScheduler				,						a					:  VQModel				,						)						->List[str]:
													super().__init__()

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

							def   A_	(   self					:  Union[str, Any]				,						a					:  Optional[Any]				,						a					:  int				,						a					:  Optional[int]     )						->Optional[int]:
													# get the original timestep using init_timestep
													SCREAMING_SNAKE_CASE__  :					Optional[int] =       min(int(num_inference_steps * strength     )				,						a     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       max(num_inference_steps - init_timestep				,						0     )
													SCREAMING_SNAKE_CASE__  :					str =       self.scheduler.timesteps[t_start:]

													return timesteps, num_inference_steps - t_start

							def   A_	(   self					:  List[Any]				,						a					:  str				,						a					:  Tuple				,						a					:  Dict				,						a					:  Any				,						a					:  str				,						a					:  Union[str, Any]				,						a					:  Union[str, Any]=None     )						->List[str]:
													if not isinstance(a				,						(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(a     )}"""     )

													SCREAMING_SNAKE_CASE__  :					int =       image.to(device=a				,						dtype=a     )

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       batch_size * num_images_per_prompt

													if image.shape[1] == 4:
																			SCREAMING_SNAKE_CASE__  :					Dict =       image

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

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

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

													SCREAMING_SNAKE_CASE__  :					str =       torch.cat([init_latents]				,						dim=0     )

													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       init_latents.shape
													SCREAMING_SNAKE_CASE__  :					List[Any] =       randn_tensor(a				,						generator=a				,						device=a				,						dtype=a     )

													# get latents
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.scheduler.add_noise(a				,						a				,						a     )

													SCREAMING_SNAKE_CASE__  :					Any =       init_latents

													return latents

							def   A_	(   self					:  List[str]				,						a					:  Optional[int]=0     )						->Dict:
													if is_accelerate_available():
																			from accelerate import cpu_offload
													else:
																			raise ImportError("Please install accelerate via `pip install accelerate`"     )

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

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

							def   A_	(   self					:  str				,						a					:  List[str]=0     )						->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__  :					str =       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__  :					List[Any] =       None
													for cpu_offloaded_model in [self.unet, self.movq]:
																			SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       cpu_offload_with_hook(a				,						a				,						prev_module_hook=a     )

													# We'll offload the last model manually.
													SCREAMING_SNAKE_CASE__  :					str =       hook

							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def   A_	(   self					:  Union[str, 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					:  Union[torch.FloatTensor, List[torch.FloatTensor]]				,						a					:  Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]				,						a					:  Union[torch.FloatTensor, List[torch.FloatTensor]]				,						a					:  torch.FloatTensor				,						a					:  int = 5_12				,						a					:  int = 5_12				,						a					:  int = 1_00				,						a					:  float = 4.0				,						a					:  float = 0.3				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Optional[int]:
													SCREAMING_SNAKE_CASE__  :					List[Any] =       self._execution_device

													SCREAMING_SNAKE_CASE__  :					List[Any] =       guidance_scale > 1.0

													if isinstance(a				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.cat(a				,						dim=0     )
													if isinstance(a				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					Tuple =       torch.cat(a				,						dim=0     )
													if isinstance(a				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       torch.cat(a				,						dim=0     )

													SCREAMING_SNAKE_CASE__  :					int =       image_embeds.shape[0]

													if do_classifier_free_guidance:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       image_embeds.repeat_interleave(a				,						dim=0     )
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       negative_image_embeds.repeat_interleave(a				,						dim=0     )
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       hint.repeat_interleave(a				,						dim=0     )

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

													if not isinstance(a				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [image]
													if not all(isinstance(a				,						(PIL.Image.Image, torch.Tensor)     ) for i in image     ):
																			raise ValueError(
																			    f"""Input is in incorrect format: {[type(a     ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"""     )

													SCREAMING_SNAKE_CASE__  :					List[Any] =       torch.cat([prepare_image(a				,						a				,						a     ) for i in image]				,						dim=0     )
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       image.to(dtype=image_embeds.dtype				,						device=a     )

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

																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       {"image_embeds": image_embeds, "hint": hint}
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       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__  :					Optional[Any] =       noise_pred.split(latents.shape[1]				,						dim=1     )
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       noise_pred.chunk(2     )
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					str =       variance_pred.chunk(2     )
																									SCREAMING_SNAKE_CASE__  :					str =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									SCREAMING_SNAKE_CASE__  :					Dict =       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__  :					Optional[Any] =       noise_pred.split(latents.shape[1]				,						dim=1     )

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

																			SCREAMING_SNAKE_CASE__  :					Any =       self.scheduler.step(
																			    a				,						a				,						a				,						generator=a				,						)[0]

													# post-processing
													SCREAMING_SNAKE_CASE__  :					Tuple =       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__  :					str =       image * 0.5 + 0.5
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.clamp(0				,						1     )
																			SCREAMING_SNAKE_CASE__  :					Dict =       image.cpu().permute(0				,						2				,						3				,						1     ).float().numpy()

													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )






from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __init__(   self					:  Dict				,						a					:  str				,						a					:  Optional[Any]     )						->Any:
													super().__init__()
													self.register_modules(unet=a				,						scheduler=a     )






							@torch.no_grad()
							def __call__(   self					:  int				,						a					:  int = 1				,						a					:  Optional[Union[torch.Generator, List[torch.Generator]]] = None				,						a					:  int = 10_00				,						a					:  Optional[str] = "pil"				,						a					:  bool = True				,						)						->Union[ImagePipelineOutput, Tuple]:
													# Sample gaussian noise to begin loop
													if isinstance(self.unet.config.sample_size				,						a     ):
																			SCREAMING_SNAKE_CASE__  :					int =       (
																			    batch_size,
																			    self.unet.config.in_channels,
																			    self.unet.config.sample_size,
																			    self.unet.config.sample_size,
																			)
													else:
																			SCREAMING_SNAKE_CASE__  :					Dict =       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

													if self.device.type == "mps":
																			# randn does not work reproducibly on mps
																			SCREAMING_SNAKE_CASE__  :					List[str] =       randn_tensor(a				,						generator=a     )
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       image.to(self.device     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       randn_tensor(a				,						generator=a				,						device=self.device     )

													# set step values
													self.scheduler.set_timesteps(a     )

													for t in self.progress_bar(self.scheduler.timesteps     ):
																			# 1. predict noise model_output
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.unet(a				,						a     ).sample

																			# 2. compute previous image: x_t -> x_t-1
																			SCREAMING_SNAKE_CASE__  :					Tuple =       self.scheduler.step(a				,						a				,						a				,						generator=a     ).prev_sample

													SCREAMING_SNAKE_CASE__  :					List[str] =       (image / 2 + 0.5).clamp(0				,						1     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       image.cpu().permute(0				,						2				,						3				,						1     ).numpy()
													if output_type == "pil":
																			SCREAMING_SNAKE_CASE__  :					Dict =       self.numpy_to_pil(a     )

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=a     )