'''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


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







class lowercase_       (		_UpperCamelCase			):







   """simple docstring"""

   def __init__(      self      :			List[Any], UpperCamelCase__      :			CLIPSegForImageSegmentation, UpperCamelCase__      :			CLIPSegProcessor, UpperCamelCase__      :			AutoencoderKL, UpperCamelCase__      :			CLIPTextModel, UpperCamelCase__      :			CLIPTokenizer, UpperCamelCase__      :			UNetaDConditionModel, UpperCamelCase__      :			Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler], UpperCamelCase__      :			StableDiffusionSafetyChecker, UpperCamelCase__      :			CLIPImageProcessor, )  ->			int:
        super().__init__()

        if hasattr(scheduler.config, 'steps_offset'     ) and scheduler.config.steps_offset != 1:
             _A    		=  (
                 f'The configuration file of this scheduler: {scheduler} is outdated.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', UpperCamelCase__, standard_warn=UpperCamelCase__     )
             _A    		=  dict(scheduler.config     )
             _A    		=  1
             _A    		=  FrozenDict(UpperCamelCase__     )

        if hasattr(scheduler.config, 'skip_prk_steps'     ) and scheduler.config.skip_prk_steps is False:
             _A    		=  (
                 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', UpperCamelCase__, standard_warn=UpperCamelCase__     )
             _A    		=  dict(scheduler.config     )
             _A    		=  True
             _A    		=  FrozenDict(UpperCamelCase__     )

        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=UpperCamelCase__, segmentation_processor=UpperCamelCase__, vae=UpperCamelCase__, text_encoder=UpperCamelCase__, tokenizer=UpperCamelCase__, unet=UpperCamelCase__, scheduler=UpperCamelCase__, safety_checker=UpperCamelCase__, feature_extractor=UpperCamelCase__, )
   def 	__UpperCAmelCase   (      self      :			Optional[Any], UpperCamelCase__      :			Optional[Union[str, int]] = "auto"     )  ->			List[Any]:
        if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             _A    		=  self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			Tuple     )  ->			List[str]:
        self.enable_attention_slicing(UpperCamelCase__     )
   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[str]:
        if is_accelerate_available():
             from accelerate import cpu_offload
        else:
             raise ImportError('Please install accelerate via `pip install accelerate`'     )

        _A    		=  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(UpperCamelCase__, UpperCamelCase__     )
   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	__UpperCAmelCase   (      self      :			int     )  ->			Dict:
        if self.device != torch.device('meta'     ) or not hasattr(self.unet, '_hf_hook'     ):
             return self.device
        for module in self.unet.modules():
             if (
                 hasattr(UpperCamelCase__, '_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      :			Optional[int], UpperCamelCase__      :			Union[str, List[str]], UpperCamelCase__      :			Union[torch.FloatTensor, PIL.Image.Image], UpperCamelCase__      :			str, UpperCamelCase__      :			int = 5_12, UpperCamelCase__      :			int = 5_12, UpperCamelCase__      :			int = 50, UpperCamelCase__      :			float = 7.5, UpperCamelCase__      :			Optional[Union[str, List[str]]] = None, UpperCamelCase__      :			Optional[int] = 1, UpperCamelCase__      :			float = 0.0, UpperCamelCase__      :			Optional[torch.Generator] = None, UpperCamelCase__      :			Optional[torch.FloatTensor] = None, UpperCamelCase__      :			Optional[str] = "pil", UpperCamelCase__      :			bool = True, UpperCamelCase__      :			Optional[Callable[[int, int, torch.FloatTensor], None]] = None, UpperCamelCase__      :			int = 1, **UpperCamelCase__      :			Dict, )  ->			Tuple:
        _A    		=  self.segmentation_processor(
            text=[text], images=[image], padding='max_length', return_tensors='pt'     ).to(self.device     )
        _A    		=  self.segmentation_model(**UpperCamelCase__     )
        _A    		=  torch.sigmoid(outputs.logits     ).cpu().detach().unsqueeze(-1     ).numpy()
        _A    		=  self.numpy_to_pil(UpperCamelCase__     )[0].resize(image.size     )

        # Run inpainting pipeline with the generated mask
        _A    		=  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=UpperCamelCase__, image=UpperCamelCase__, mask_image=UpperCamelCase__, height=UpperCamelCase__, width=UpperCamelCase__, num_inference_steps=UpperCamelCase__, guidance_scale=UpperCamelCase__, negative_prompt=UpperCamelCase__, num_images_per_prompt=UpperCamelCase__, eta=UpperCamelCase__, generator=UpperCamelCase__, latents=UpperCamelCase__, output_type=UpperCamelCase__, return_dict=UpperCamelCase__, callback=UpperCamelCase__, callback_steps=UpperCamelCase__, )






'''simple docstring'''



import warnings

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




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

      __SCREAMING_SNAKE_CASE      = image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
           raise ValueError("""You need to specify an `image_processor`."""     )
      if tokenizer is None:
           raise ValueError("""You need to specify a `tokenizer`."""     )

      super().__init__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					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:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

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

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


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.tokenizer.model_input_names
      __SCREAMING_SNAKE_CASE      = self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names     )     )






