





"""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


lowerCamelCase_     :						Optional[Any]						=					logging.get_logger(__name__)  # pylint: disable=invalid-name

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_					,      ):

        """simple docstring"""

        super().__init__()

        if hasattr(scheduler.config					,      'steps_offset'  ) and scheduler.config.steps_offset != 1:
          A_  :       Dict										=  (
              F"""The configuration file of this scheduler: {scheduler} is outdated.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'					,      snake_case_					,      standard_warn=snake_case_  )
          A_  :       Optional[Any]										=  dict(scheduler.config  )
          A_  :       List[Any]										=  1
          A_  :       int										=  FrozenDict(snake_case_  )

        if hasattr(scheduler.config					,      'skip_prk_steps'  ) and scheduler.config.skip_prk_steps is False:
          A_  :       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'					,      snake_case_					,      standard_warn=snake_case_  )
          A_  :       List[Any]										=  dict(scheduler.config  )
          A_  :       Optional[Any]										=  True
          A_  :       List[Any]										=  FrozenDict(snake_case_  )

        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=snake_case_					,      segmentation_processor=snake_case_					,      vae=snake_case_					,      text_encoder=snake_case_					,      tokenizer=snake_case_					,      unet=snake_case_					,      scheduler=snake_case_					,      safety_checker=snake_case_					,      feature_extractor=snake_case_					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_ = "auto"  ):

        """simple docstring"""

        if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          A_  :       Tuple										=  self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        self.enable_attention_slicing(snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

        A_  :       Optional[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(snake_case_					,      snake_case_  )

      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.device != torch.device('meta'  ) or not hasattr(self.unet					,      '_hf_hook'  ):
          return self.device
        for module in self.unet.modules():
          if (
              hasattr(snake_case_					,      '_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					,      snake_case_					,      snake_case_					,      snake_case_					,      snake_case_ = 5_1_2					,      snake_case_ = 5_1_2					,      snake_case_ = 5_0					,      snake_case_ = 7.5					,      snake_case_ = None					,      snake_case_ = 1					,      snake_case_ = 0.0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = "pil"					,      snake_case_ = True					,      snake_case_ = None					,      snake_case_ = 1					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       List[str]										=  self.segmentation_processor(
            text=[text]					,      images=[image]					,      padding='max_length'					,      return_tensors='pt'  ).to(self.device  )
        A_  :       Optional[Any]										=  self.segmentation_model(**snake_case_  )
        A_  :       Tuple										=  torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
        A_  :       List[Any]										=  self.numpy_to_pil(snake_case_  )[0].resize(image.size  )

        # Run inpainting pipeline with the generated mask
        A_  :       List[Any]										=  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=snake_case_					,      image=snake_case_					,      mask_image=snake_case_					,      height=snake_case_					,      width=snake_case_					,      num_inference_steps=snake_case_					,      guidance_scale=snake_case_					,      negative_prompt=snake_case_					,      num_images_per_prompt=snake_case_					,      eta=snake_case_					,      generator=snake_case_					,      latents=snake_case_					,      output_type=snake_case_					,      return_dict=snake_case_					,      callback=snake_case_					,      callback_steps=snake_case_					,      )






"""simple docstring"""




import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : Dict	    =   ["""image_processor""", """tokenizer"""]
      lowercase_      : Union[str, Any]	    =   """ViltImageProcessor"""
      lowercase_      : Any	    =   ("""BertTokenizer""", """BertTokenizerFast""")

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        A_  :       Union[str, Any]										=  None
        if "feature_extractor" in kwargs:
          warnings.warn(
              'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
              ' instead.'					,      snake_case_					,      )
          A_  :       Dict										=  kwargs.pop('feature_extractor'  )

        A_  :       Dict										=  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__(snake_case_					,      snake_case_  )
        A_  :       List[str]										=  self.image_processor

      def __call__(     self					,      snake_case_					,      snake_case_ = None					,      snake_case_ = True					,      snake_case_ = False					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = 0					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = None					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = False					,      snake_case_ = True					,      snake_case_ = None					,      **snake_case_					,      ):

        """simple docstring"""

        A_  :       str										=  self.tokenizer(
            text=snake_case_					,      add_special_tokens=snake_case_					,      padding=snake_case_					,      truncation=snake_case_					,      max_length=snake_case_					,      stride=snake_case_					,      pad_to_multiple_of=snake_case_					,      return_token_type_ids=snake_case_					,      return_attention_mask=snake_case_					,      return_overflowing_tokens=snake_case_					,      return_special_tokens_mask=snake_case_					,      return_offsets_mapping=snake_case_					,      return_length=snake_case_					,      verbose=snake_case_					,      return_tensors=snake_case_					,      **snake_case_					,      )
        # add pixel_values + pixel_mask
        A_  :       Optional[int]										=  self.image_processor(snake_case_					,      return_tensors=snake_case_  )
        encoding.update(snake_case_  )

        return encoding

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        return self.tokenizer.batch_decode(*snake_case_					,      **snake_case_  )

      def 					lowerCamelCase_			(     self					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

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

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.tokenizer.model_input_names
        A_  :       Any										=  self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

      @property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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