


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 :				List[str]						    =       logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase :				Optional[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_  (				__snake_case			:						Any	,			__snake_case			:						Union[str, Any]	,			__snake_case			:						Any=8     )->      str:




						'''simple docstring'''
						__magic_name__ 		=			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__magic_name__ 		=			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor
def 							UpperCamelCase_  (				__snake_case			:						List[str]	,			__snake_case			:						Optional[int]=5_12	,			__snake_case			:						Tuple=5_12     )->      List[str]:




						'''simple docstring'''
						__magic_name__ 		=			pil_image.resize((w, h)	,			resample=Image.BICUBIC	,			reducing_gap=1     )
						__magic_name__ 		=			np.array(pil_image.convert('RGB'     )     )
						__magic_name__ 		=			arr.astype(np.floataa     ) / 1_27.5 - 1
						__magic_name__ 		=			np.transpose(_UpperCamelCase	,			[2, 0, 1]     )
						__magic_name__ 		=			torch.from_numpy(_UpperCamelCase     ).unsqueeze(0     )
						return image




class     __snake_case							(      SCREAMING_SNAKE_CASE__):
					def __init__(	self					:	Optional[int]  ,			_lowerCAmelCase					:	UNetaDConditionModel  ,			_lowerCAmelCase					:	DDPMScheduler  ,			_lowerCAmelCase					:	VQModel  ,			)       ->				int:



											'''simple docstring'''





											super().__init__()

											self.register_modules(
											    unet=a_  ,			scheduler=a_  ,			movq=a_  ,			)
											__magic_name__ 		=			2 ** (len(self.movq.config.block_out_channels     ) - 1)
					def   a (	self					:	Dict  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	Dict  ,			_lowerCAmelCase					:	List[Any]     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			min(int(num_inference_steps * strength     )  ,			a_     )

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

											return timesteps, num_inference_steps - t_start
					def   a (	self					:	Tuple  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Any  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	int=None     )       ->				List[str]:



											'''simple docstring'''





											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_     )}'''     )

											__magic_name__ 		=			image.to(device=a_  ,			dtype=a_     )

											__magic_name__ 		=			batch_size * num_images_per_prompt

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

																	__magic_name__ 		=			self.movq.config.scaling_factor * init_latents

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

											__magic_name__ 		=			init_latents.shape
											__magic_name__ 		=			randn_tensor(a_  ,			generator=a_  ,			device=a_  ,			dtype=a_     )

											# get latents
											__magic_name__ 		=			self.scheduler.add_noise(a_  ,			a_  ,			a_     )

											__magic_name__ 		=			init_latents

											return latents
					def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	List[str]=0     )       ->				int:



											'''simple docstring'''





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

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

											__magic_name__ 		=			[
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																							cpu_offload(a_  ,			a_     )
					def   a (	self					:	Dict  ,			_lowerCAmelCase					:	int=0     )       ->				str:



											'''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.'     )

											__magic_name__ 		=			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)

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

											# We'll offload the last model manually.
											__magic_name__ 		=			hook
					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   a (	self					:	Tuple     )       ->				Dict:



											'''simple docstring'''





											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					:	Dict  ,			_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  ,			)       ->				Dict:



											'''simple docstring'''





											__magic_name__ 		=			self._execution_device

											__magic_name__ 		=			guidance_scale > 1.0

											if isinstance(a_  ,			a_     ):
																	__magic_name__ 		=			torch.cat(a_  ,			dim=0     )
											if isinstance(a_  ,			a_     ):
																	__magic_name__ 		=			torch.cat(a_  ,			dim=0     )
											if isinstance(a_  ,			a_     ):
																	__magic_name__ 		=			torch.cat(a_  ,			dim=0     )

											__magic_name__ 		=			image_embeds.shape[0]

											if do_classifier_free_guidance:
																	__magic_name__ 		=			image_embeds.repeat_interleave(a_  ,			dim=0     )
																	__magic_name__ 		=			negative_image_embeds.repeat_interleave(a_  ,			dim=0     )
																	__magic_name__ 		=			hint.repeat_interleave(a_  ,			dim=0     )

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

											if not isinstance(a_  ,			a_     ):
																	__magic_name__ 		=			[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'''     )

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

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

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

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

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

																	__magic_name__ 		=			self.scheduler.step(
																	    a_  ,			a_  ,			a_  ,			generator=a_  ,			)[0]

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

											if output_type == "pil":
																	__magic_name__ 		=			self.numpy_to_pil(a_     )

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=a_     )



import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding

class     __snake_case							(      _UpperCAmelCase):
					SCREAMING_SNAKE_CASE_		:					Union[str, Any] 				= ["image_processor", "tokenizer"]
					SCREAMING_SNAKE_CASE_		:					Tuple 				= "AutoImageProcessor"
					SCREAMING_SNAKE_CASE_		:					Dict 				= "AutoTokenizer"
					def __init__(	self					:	List[str]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			**_lowerCAmelCase					:	List[str]     )       ->				Optional[int]:



											'''simple docstring'''





											__magic_name__ 		=			None
											if "feature_extractor" in kwargs:
																	warnings.warn(
																	    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
																	    ' instead.'  ,			_lowerCAmelCase  ,			)
																	__magic_name__ 		=			kwargs.pop('feature_extractor'     )

											__magic_name__ 		=			image_processor if image_processor is not None else feature_extractor
											if image_processor is None:
																	raise ValueError('You have to specify an image_processor.'     )
											if tokenizer is None:
																	raise ValueError('You have to specify a tokenizer.'     )

											super().__init__(_lowerCAmelCase  ,			_lowerCAmelCase     )
											__magic_name__ 		=			self.image_processor
					def __call__(	self					:	str  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	int=None  ,			_lowerCAmelCase					:	List[str]=None  ,			**_lowerCAmelCase					:	int     )       ->				int:



											'''simple docstring'''





											if text is None and images is None:
																	raise ValueError('You have to specify either text or images. Both cannot be none.'     )

											if text is not None:
																	__magic_name__ 		=			self.tokenizer(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if images is not None:
																	__magic_name__ 		=			self.image_processor(_lowerCAmelCase  ,			return_tensors=_lowerCAmelCase  ,			**_lowerCAmelCase     )

											if text is not None and images is not None:
																	__magic_name__ 		=			image_features.pixel_values
																	return encoding
											elif text is not None:
																	return encoding
											else:
																	return BatchEncoding(data=dict(**_lowerCAmelCase     )  ,			tensor_type=_lowerCAmelCase     )
					def   a (	self					:	Optional[int]  ,			*_lowerCAmelCase					:	Optional[int]  ,			**_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[int]:



											'''simple docstring'''





											return self.tokenizer.batch_decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					def   a (	self					:	Union[str, Any]  ,			*_lowerCAmelCase					:	str  ,			**_lowerCAmelCase					:	Tuple     )       ->				int:



											'''simple docstring'''





											return self.tokenizer.decode(*_lowerCAmelCase  ,			**_lowerCAmelCase     )
					@property
					def   a (	self					:	List[Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self.tokenizer.model_input_names
											__magic_name__ 		=			self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )
					@property
					def   a (	self					:	Optional[Any]     )       ->				Dict:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor_class







					@property
					def   a (	self					:	Dict     )       ->				Tuple:



											'''simple docstring'''





											warnings.warn(
											    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'  ,			_lowerCAmelCase  ,			)
											return self.image_processor

