


'''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,
)


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

lowerCamelCase__    	=     '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=8   ):
		_UpperCAmelCase		:       Optional[Any]								=      height // scale_factor**2
		if height % scale_factor**2 != 0:
				new_height += 1
		_UpperCAmelCase		:       Optional[int]								=      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      , __lowerCAmelCase=512      , __lowerCAmelCase=512   ):
		_UpperCAmelCase		:       Optional[int]								=      pil_image.resize((w, h)      , resample=Image.BICUBIC      , reducing_gap=1   )
		_UpperCAmelCase		:       Tuple								=      np.array(pil_image.convert("RGB"   )   )
		_UpperCAmelCase		:       List[str]								=      arr.astype(np.floataa   ) / 1_2_7.5 - 1
		_UpperCAmelCase		:       int								=      np.transpose(__lowerCAmelCase      , [2, 0, 1]   )
		_UpperCAmelCase		:       str								=      torch.from_numpy(__lowerCAmelCase   ).unsqueeze(0   )
		return image






class 	lowerCAmelCase__			(			UpperCAmelCase__	):




			def __init__(		self  :     int	,  lowerCamelCase__  :     UNetaDConditionModel	,  lowerCamelCase__  :     DDPMScheduler	,  lowerCamelCase__  :     VQModel	,  )   ->Union[str, Any]:



					'''simple docstring'''







					super().__init__()

					self.register_modules(
					    unet=lowerCamelCase__	,  scheduler=lowerCamelCase__	,  movq=lowerCamelCase__	,  )
					_UpperCAmelCase		:       Dict								=      2 ** (len(self.movq.config.block_out_channels ) - 1)




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     int	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Union[str, Any] )   ->Union[str, Any]:



					'''simple docstring'''







					_UpperCAmelCase		:       int								=      min(int(num_inference_steps * strength )	,  lowerCamelCase__ )

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

					return timesteps, num_inference_steps - t_start




			def 							lowerCAmelCase__    (		self  :     List[Any]	,  lowerCamelCase__  :     Tuple	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     Optional[Any]	,  lowerCamelCase__  :     Dict	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     List[str]	,  lowerCamelCase__  :     Dict=None )   ->Any:



					'''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__ )}""" )

					_UpperCAmelCase		:       str								=      image.to(device=lowerCamelCase__	,  dtype=lowerCamelCase__ )

					_UpperCAmelCase		:       Tuple								=      batch_size * num_images_per_prompt

					if image.shape[1] == 4:
							_UpperCAmelCase		:       Union[str, Any]								=      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__ ):
									_UpperCAmelCase		:       List[Any]								=      [
									    self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCamelCase__ )
									]
									_UpperCAmelCase		:       str								=      torch.cat(lowerCamelCase__	,  dim=0 )
							else:
									_UpperCAmelCase		:       List[str]								=      self.movq.encode(lowerCamelCase__ ).latent_dist.sample(lowerCamelCase__ )

							_UpperCAmelCase		:       List[str]								=      self.movq.config.scaling_factor * init_latents

					_UpperCAmelCase		:       List[Any]								=      torch.cat([init_latents]	,  dim=0 )

					_UpperCAmelCase		:       int								=      init_latents.shape
					_UpperCAmelCase		:       List[Any]								=      randn_tensor(lowerCamelCase__	,  generator=lowerCamelCase__	,  device=lowerCamelCase__	,  dtype=lowerCamelCase__ )

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

					_UpperCAmelCase		:       Dict								=      init_latents

					return latents




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     List[Any]=0 )   ->str:



					'''simple docstring'''







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

					_UpperCAmelCase		:       List[str]								=      torch.device(F"""cuda:{gpu_id}""" )

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




			def 							lowerCAmelCase__    (		self  :     Any	,  lowerCamelCase__  :     Tuple=0 )   ->List[Any]:



					'''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." )

					_UpperCAmelCase		:       Dict								=      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)

					_UpperCAmelCase		:       Union[str, Any]								=      None
					for cpu_offloaded_model in [self.unet, self.movq]:
							_UpperCAmelCase					,    _UpperCAmelCase		:       Tuple								=      cpu_offload_with_hook(lowerCamelCase__	,  lowerCamelCase__	,  prev_module_hook=lowerCamelCase__ )

					# We'll offload the last model manually.
					_UpperCAmelCase		:       Optional[Any]								=      hook




			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							lowerCAmelCase__    (		self  :     Any )   ->Optional[Any]:



					'''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  :     str	,  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__  :     int = 5_12	,  lowerCamelCase__  :     int = 5_12	,  lowerCamelCase__  :     int = 1_00	,  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	,  )   ->str:



					'''simple docstring'''







					_UpperCAmelCase		:       Any								=      self._execution_device

					_UpperCAmelCase		:       str								=      guidance_scale > 1.0

					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       List[str]								=      torch.cat(lowerCamelCase__	,  dim=0 )
					_UpperCAmelCase		:       Any								=      image_embeds.shape[0]
					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Optional[Any]								=      torch.cat(lowerCamelCase__	,  dim=0 )

					if do_classifier_free_guidance:
							_UpperCAmelCase		:       str								=      image_embeds.repeat_interleave(lowerCamelCase__	,  dim=0 )
							_UpperCAmelCase		:       Optional[int]								=      negative_image_embeds.repeat_interleave(lowerCamelCase__	,  dim=0 )

					_UpperCAmelCase		:       str								=      torch.cat([negative_image_embeds, image_embeds]	,  dim=0 ).to(dtype=self.unet.dtype	,  device=lowerCamelCase__ )

					if not isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       List[Any]								=      [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""" )

					_UpperCAmelCase		:       Dict								=      torch.cat([prepare_image(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ ) for i in image]	,  dim=0 )
					_UpperCAmelCase		:       int								=      image.to(dtype=image_embeds.dtype	,  device=lowerCamelCase__ )

					_UpperCAmelCase		:       Dict								=      self.movq.encode(lowerCamelCase__ )["latents"]
					_UpperCAmelCase		:       List[str]								=      latents.repeat_interleave(lowerCamelCase__	,  dim=0 )
					self.scheduler.set_timesteps(lowerCamelCase__	,  device=lowerCamelCase__ )
					_UpperCAmelCase					,    _UpperCAmelCase		:       Any								=      self.get_timesteps(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )
					_UpperCAmelCase		:       List[str]								=      timesteps[:1].repeat(batch_size * num_images_per_prompt )
					_UpperCAmelCase					,    _UpperCAmelCase		:       Union[str, Any]								=      downscale_height_and_width(lowerCamelCase__	,  lowerCamelCase__	,  self.movq_scale_factor )
					_UpperCAmelCase		:       List[Any]								=      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
							_UpperCAmelCase		:       Optional[Any]								=      torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

							_UpperCAmelCase		:       Union[str, Any]								=      {"image_embeds": image_embeds}
							_UpperCAmelCase		:       Any								=      self.unet(
							    sample=lowerCamelCase__	,  timestep=lowerCamelCase__	,  encoder_hidden_states=lowerCamelCase__	,  added_cond_kwargs=lowerCamelCase__	,  return_dict=lowerCamelCase__	,  )[0]

							if do_classifier_free_guidance:
									_UpperCAmelCase					,    _UpperCAmelCase		:       List[str]								=      noise_pred.split(latents.shape[1]	,  dim=1 )
									_UpperCAmelCase					,    _UpperCAmelCase		:       Optional[int]								=      noise_pred.chunk(2 )
									_UpperCAmelCase					,    _UpperCAmelCase		:       Tuple								=      variance_pred.chunk(2 )
									_UpperCAmelCase		:       str								=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
									_UpperCAmelCase		:       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"]
							):
									_UpperCAmelCase					,    _UpperCAmelCase		:       int								=      noise_pred.split(latents.shape[1]	,  dim=1 )

							# compute the previous noisy sample x_t -> x_t-1
							_UpperCAmelCase		:       int								=      self.scheduler.step(
							    lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__	,  generator=lowerCamelCase__	,  )[0]

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

					if output_type == "pil":
							_UpperCAmelCase		:       Tuple								=      self.numpy_to_pil(lowerCamelCase__ )

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCamelCase__ )







'''simple docstring'''


from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
						if not (is_transformers_available() and is_torch_available()):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
						from .multicontrolnet import MultiControlNetModel
						from .pipeline_controlnet import StableDiffusionControlNetPipeline
						from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
						from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
						from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline



