"""simple docstring"""




from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_a   :	Tuple 					=     logging.get_logger(__name__)  # pylint: disable=invalid-name
class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()

       if hasattr(scheduler.config       ,  """steps_offset"""						) and scheduler.config.steps_offset != 1:
         _lowerCAmelCase		: Union[str, Any]						    =  (
             F"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
             F" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
             """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
             """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
             """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
             """ file"""
         )
         deprecate("""steps_offset!=1"""       ,  """1.0.0"""       ,  a__       ,  standard_warn=a__						)
         _lowerCAmelCase		: str						    =  dict(scheduler.config						)
         _lowerCAmelCase		: Union[str, Any]						    =  1
         _lowerCAmelCase		: List[Any]						    =  FrozenDict(a__						)

       if hasattr(scheduler.config       ,  """skip_prk_steps"""						) and scheduler.config.skip_prk_steps is False:
         _lowerCAmelCase		: Dict						    =  (
             F"The configuration file of this scheduler: {scheduler} has not set the configuration"
             """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
             """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
             """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
             """ Hub, it would be very nice if you could open a Pull request for the"""
             """ `scheduler/scheduler_config.json` file"""
         )
         deprecate("""skip_prk_steps not set"""       ,  """1.0.0"""       ,  a__       ,  standard_warn=a__						)
         _lowerCAmelCase		: Dict						    =  dict(scheduler.config						)
         _lowerCAmelCase		: Any						    =  True
         _lowerCAmelCase		: Any						    =  FrozenDict(a__						)

       if safety_checker is None:
         logger.warning(
             F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
             """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
             """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
             """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
             """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
             """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""						)

       self.register_modules(
           segmentation_model=a__       ,  segmentation_processor=a__       ,  vae=a__       ,  text_encoder=a__       ,  tokenizer=a__       ,  unet=a__       ,  scheduler=a__       ,  safety_checker=a__       ,  feature_extractor=a__       ,  )






     def 					__A						(	self       ,  a__ = "auto"						):
       if slice_size == "auto":
         # half the attention head size is usually a good trade-off between
         # speed and memory
         _lowerCAmelCase		: List[str]						    =  self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(a__						)






     def 					__A						(	self						):
       self.enable_attention_slicing(a__						)






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

       _lowerCAmelCase		: int						    =  torch.device("""cuda"""						)

       for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
         if cpu_offloaded_model is not None:
           cpu_offload(a__       ,  a__						)






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__A						(	self						):
       if self.device != torch.device("""meta"""						) or 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()
     def __call__(	self       ,  a__       ,  a__       ,  a__       ,  a__ = 512       ,  a__ = 512       ,  a__ = 50       ,  a__ = 7.5       ,  a__ = None       ,  a__ = 1       ,  a__ = 0.0       ,  a__ = None       ,  a__ = None       ,  a__ = "pil"       ,  a__ = True       ,  a__ = None       ,  a__ = 1       ,  **a__       ,  ):
       _lowerCAmelCase		: Any						    =  self.segmentation_processor(
           text=[text]       ,  images=[image]       ,  padding="""max_length"""       ,  return_tensors="""pt"""						).to(self.device						)
       _lowerCAmelCase		: Dict						    =  self.segmentation_model(**a__						)
       _lowerCAmelCase		: Tuple						    =  torch.sigmoid(outputs.logits						).cpu().detach().unsqueeze(-1						).numpy()
       _lowerCAmelCase		: Optional[Any]						    =  self.numpy_to_pil(a__						)[0].resize(image.size						)

       # Run inpainting pipeline with the generated mask
       _lowerCAmelCase		: Optional[int]						    =  StableDiffusionInpaintPipeline(
           vae=self.vae       ,  text_encoder=self.text_encoder       ,  tokenizer=self.tokenizer       ,  unet=self.unet       ,  scheduler=self.scheduler       ,  safety_checker=self.safety_checker       ,  feature_extractor=self.feature_extractor       ,  )
       return inpainting_pipeline(
           prompt=a__       ,  image=a__       ,  mask_image=a__       ,  height=a__       ,  width=a__       ,  num_inference_steps=a__       ,  guidance_scale=a__       ,  negative_prompt=a__       ,  num_images_per_prompt=a__       ,  eta=a__       ,  generator=a__       ,  latents=a__       ,  output_type=a__       ,  return_dict=a__       ,  callback=a__       ,  callback_steps=a__       ,  )





"""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,
)


_a   :	Union[str, Any] 					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_a   :	int 					=     '\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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Dict=8			) ->   Any:
  _lowerCAmelCase		: List[Any]						    =  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCAmelCase		: Optional[Any]						    =  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Any=512    ,_lowerCamelCase		: Dict=512			) ->   List[Any]:
  _lowerCAmelCase		: Any						    =  pil_image.resize((w, h)    ,resample=Image.BICUBIC    ,reducing_gap=1			)
  _lowerCAmelCase		: Dict						    =  np.array(pil_image.convert("""RGB"""			)			)
  _lowerCAmelCase		: List[str]						    =  arr.astype(np.floataa			) / 1_27.5 - 1
  _lowerCAmelCase		: int						    =  np.transpose(_lowerCamelCase    ,[2, 0, 1]			)
  _lowerCAmelCase		: Optional[Any]						    =  torch.from_numpy(_lowerCamelCase			).unsqueeze(0			)
  return image






class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()

       self.register_modules(
           unet=a__       ,  scheduler=a__       ,  movq=a__       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  2 ** (len(self.movq.config.block_out_channels						) - 1)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       # get the original timestep using init_timestep
       _lowerCAmelCase		: Optional[Any]						    =  min(int(num_inference_steps * strength						)       ,  a__						)

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

       return timesteps, num_inference_steps - t_start






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=None						):
       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__						)}"						)

       _lowerCAmelCase		: Union[str, Any]						    =  image.to(device=a__       ,  dtype=a__						)

       _lowerCAmelCase		: int						    =  batch_size * num_images_per_prompt

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

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

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

       _lowerCAmelCase		: Dict						    =  init_latents.shape
       _lowerCAmelCase		: str						    =  randn_tensor(a__       ,  generator=a__       ,  device=a__       ,  dtype=a__						)

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

       _lowerCAmelCase		: int						    =  init_latents

       return latents






     def 					__A						(	self       ,  a__=0						):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

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

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






     def 					__A						(	self       ,  a__=0						):
       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."""						)

       _lowerCAmelCase		: Optional[int]						    =  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)

       _lowerCAmelCase		: List[str]						    =  None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  cpu_offload_with_hook(a__       ,  a__       ,  prev_module_hook=a__						)

       # We'll offload the last model manually.
       _lowerCAmelCase		: Tuple						    =  hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__A						(	self						):
       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       ,  a__       ,  a__       ,  a__       ,  a__ = 512       ,  a__ = 512       ,  a__ = 100       ,  a__ = 4.0       ,  a__ = 0.3       ,  a__ = 1       ,  a__ = None       ,  a__ = "pil"       ,  a__ = True       ,  ):
       _lowerCAmelCase		: Dict						    =  self._execution_device

       _lowerCAmelCase		: Optional[Any]						    =  guidance_scale > 1.0

       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Dict						    =  torch.cat(a__       ,  dim=0						)
       _lowerCAmelCase		: Dict						    =  image_embeds.shape[0]
       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: List[Any]						    =  torch.cat(a__       ,  dim=0						)

       if do_classifier_free_guidance:
         _lowerCAmelCase		: int						    =  image_embeds.repeat_interleave(a__       ,  dim=0						)
         _lowerCAmelCase		: Any						    =  negative_image_embeds.repeat_interleave(a__       ,  dim=0						)

       _lowerCAmelCase		: Optional[int]						    =  torch.cat([negative_image_embeds, image_embeds]       ,  dim=0						).to(dtype=self.unet.dtype       ,  device=a__						)

       if not isinstance(a__       ,  a__						):
         _lowerCAmelCase		: 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"						)

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

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

         _lowerCAmelCase		: int						    =  {"""image_embeds""": image_embeds}
         _lowerCAmelCase		: List[str]						    =  self.unet(
             sample=a__       ,  timestep=a__       ,  encoder_hidden_states=a__       ,  added_cond_kwargs=a__       ,  return_dict=a__       ,  )[0]

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

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase		: List[str]						    =  self.scheduler.step(
             a__       ,  a__       ,  a__       ,  generator=a__       ,  )[0]

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

       if output_type == "pil":
         _lowerCAmelCase		: List[str]						    =  self.numpy_to_pil(a__						)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__						)




