




"""simple docstring"""

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

UpperCamelCase__			:     Optional[int]			       =				"\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    snake_case__						(							lowerCAmelCase :		Tuple					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Any=8		):
					__snake_case        =						height // scale_factor**2
					if height % scale_factor**2 != 0:
										new_height += 1
					__snake_case        =						width // scale_factor**2
					if width % scale_factor**2 != 0:
										new_width += 1
					return new_height * scale_factor, new_width * scale_factor




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		int=512					,       lowerCAmelCase :		Union[str, Any]=512		):
					__snake_case        =						pil_image.resize((w, h)					,       resample=Image.BICUBIC					,       reducing_gap=1		)
					__snake_case        =						np.array(pil_image.convert("""RGB"""		)		)
					__snake_case        =						arr.astype(np.floataa		) / 1_2_7.5 - 1
					__snake_case        =						np.transpose(lowerCAmelCase					,       [2, 0, 1]		)
					__snake_case        =						torch.from_numpy(lowerCAmelCase		).unsqueeze(0		)
					return image

class      A__   (   lowercase_  ):




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

						self.register_modules(
						    unet=lowercase   ,     scheduler=lowercase   ,     movq=lowercase   ,     )
						__snake_case        =						2 ** (len(self.movq.config.block_out_channels						) - 1)




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						Union[str, Any]   ,     lowercase	:						List[Any]   ,     lowercase	:						Dict						)       ->			Optional[Any]:
						# get the original timestep using init_timestep
						__snake_case        =						min(int(num_inference_steps * strength						)   ,     lowercase						)

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

						return timesteps, num_inference_steps - t_start




	def 				a_		( self	:						Any   ,     lowercase	:						Any   ,     lowercase	:						Any   ,     lowercase	:						Optional[Any]   ,     lowercase	:						List[Any]   ,     lowercase	:						List[Any]   ,     lowercase	:						Optional[Any]   ,     lowercase	:						Dict=None						)       ->			List[Any]:
						if not isinstance(lowercase   ,     (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(lowercase						)}'''						)

						__snake_case        =						image.to(device=lowercase   ,     dtype=lowercase						)

						__snake_case        =						batch_size * num_images_per_prompt

						if image.shape[1] == 4:
											__snake_case        =						image

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

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

											__snake_case        =						self.movq.config.scaling_factor * init_latents

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

						__snake_case        =						init_latents.shape
						__snake_case        =						randn_tensor(lowercase   ,     generator=lowercase   ,     device=lowercase   ,     dtype=lowercase						)

						# get latents
						__snake_case        =						self.scheduler.add_noise(lowercase   ,     lowercase   ,     lowercase						)

						__snake_case        =						init_latents

						return latents




	def 				a_		( self	:						Tuple   ,     lowercase	:						List[str]=0						)       ->			List[str]:
						if is_accelerate_available():
											from accelerate import cpu_offload
						else:
											raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

						__snake_case        =						torch.device(f'''cuda:{gpu_id}'''						)

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




	def 				a_		( self	:						Optional[Any]   ,     lowercase	:						Any=0						)       ->			List[str]:
						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."""						)

						__snake_case        =						torch.device(f'''cuda:{gpu_id}'''						)

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

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

						# We'll offload the last model manually.
						__snake_case        =						hook




	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def 				a_		( self	:						Union[str, Any]						)       ->			Optional[int]:
						if not hasattr(self.unet   ,     """_hf_hook"""						):
											return self.device
						for module in self.unet.modules():
											if (
											    hasattr(lowercase   ,     """_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(lowercase						)
	def __call__( self	:						List[str]   ,     lowercase	:						Union[torch.FloatTensor, List[torch.FloatTensor]]   ,     lowercase	:						Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]   ,     lowercase	:						Union[torch.FloatTensor, List[torch.FloatTensor]]   ,     lowercase	:						torch.FloatTensor   ,     lowercase	:						int = 512   ,     lowercase	:						int = 512   ,     lowercase	:						int = 100   ,     lowercase	:						float = 4.0   ,     lowercase	:						float = 0.3   ,     lowercase	:						int = 1   ,     lowercase	:						Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,     lowercase	:						Optional[str] = "pil"   ,     lowercase	:						bool = True   ,     )       ->			int:
						__snake_case        =						self._execution_device

						__snake_case        =						guidance_scale > 1.0

						if isinstance(lowercase   ,     lowercase						):
											__snake_case        =						torch.cat(lowercase   ,     dim=0						)
						if isinstance(lowercase   ,     lowercase						):
											__snake_case        =						torch.cat(lowercase   ,     dim=0						)
						if isinstance(lowercase   ,     lowercase						):
											__snake_case        =						torch.cat(lowercase   ,     dim=0						)

						__snake_case        =						image_embeds.shape[0]

						if do_classifier_free_guidance:
											__snake_case        =						image_embeds.repeat_interleave(lowercase   ,     dim=0						)
											__snake_case        =						negative_image_embeds.repeat_interleave(lowercase   ,     dim=0						)
											__snake_case        =						hint.repeat_interleave(lowercase   ,     dim=0						)

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

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

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

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

											__snake_case        =						{"""image_embeds""": image_embeds, """hint""": hint}
											__snake_case        =						self.unet(
											    sample=lowercase   ,     timestep=lowercase   ,     encoder_hidden_states=lowercase   ,     added_cond_kwargs=lowercase   ,     return_dict=lowercase   ,     )[0]

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

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

											__snake_case        =						self.scheduler.step(
											    lowercase   ,     lowercase   ,     lowercase   ,     generator=lowercase   ,     )[0]

						# post-processing
						__snake_case        =						self.movq.decode(lowercase   ,     force_not_quantize=lowercase						)["""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"]:
											__snake_case        =						image * 0.5 + 0.5
											__snake_case        =						image.clamp(0   ,     1						)
											__snake_case        =						image.cpu().permute(0   ,     2   ,     3   ,     1						).float().numpy()

						if output_type == "pil":
											__snake_case        =						self.numpy_to_pil(lowercase						)

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=lowercase						)








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
from ..clip.configuration_clip import CLIPVisionConfig


UpperCamelCase__			:     Optional[Any]			       =				logging.get_logger(__name__)
class      A__   (   lowercase_  ):
	A_				:					List[str]      =   """vision-text-dual-encoder"""
	A_				:					Tuple      =   True




	def __init__( self	:						Tuple   ,     lowercase	:						List[Any]=512   ,     lowercase	:						Any=2.65_92   ,     **lowercase	:						int						)       ->			int:
						super().__init__(**lowercase						)

						if "vision_config" not in kwargs:
											raise ValueError("""`vision_config` can not be `None`."""						)

						if "text_config" not in kwargs:
											raise ValueError("""`text_config` can not be `None`."""						)

						__snake_case        =						kwargs.pop("""vision_config"""						)
						__snake_case        =						kwargs.pop("""text_config"""						)

						__snake_case        =						vision_config.pop("""model_type"""						)
						__snake_case        =						text_config.pop("""model_type"""						)

						if vision_model_type == "clip":
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						).vision_config
						elif vision_model_type == "clip_vision_model":
											__snake_case        =						CLIPVisionConfig(**lowercase						)
						else:
											__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						AutoConfig.for_model(lowercase   ,     **lowercase						)

						__snake_case        =						projection_dim
						__snake_case        =						logit_scale_init_value




	@classmethod
	def 				a_		( cls	:						Union[str, Any]   ,     lowercase	:						PretrainedConfig   ,     lowercase	:						PretrainedConfig   ,     **lowercase	:						Optional[int]						)       ->			Dict:
						return cls(vision_config=vision_config.to_dict()   ,     text_config=text_config.to_dict()   ,     **lowercase						)




	def 				a_		( self	:						Dict						)       ->			Dict:
						__snake_case        =						copy.deepcopy(self.__dict__						)
						__snake_case        =						self.vision_config.to_dict()
						__snake_case        =						self.text_config.to_dict()
						__snake_case        =						self.__class__.model_type
						return output


