





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






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 def __init__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__()

     if hasattr(scheduler.config					,						'''steps_offset'''  ) and scheduler.config.steps_offset != 1:
         __A    :						List[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             F"""The configuration file of this scheduler: {scheduler} is outdated.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'''					,						__lowerCamelCase					,						standard_warn=__lowerCamelCase  )
         __A    :						List[Any]						 =     dict(scheduler.config  )
         __A    :						List[str]						 =     1
         __A    :						Tuple						 =     FrozenDict(__lowerCamelCase  )

     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'''					,						__lowerCamelCase					,						standard_warn=__lowerCamelCase  )
         __A    :						str						 =     dict(scheduler.config  )
         __A    :						str						 =     True
         __A    :						Union[str, Any]						 =     FrozenDict(__lowerCamelCase  )

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

 def 					UpperCamelCase__(    self					,						__lowerCamelCase = "auto"  ):



     '''simple docstring'''




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

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     self.enable_attention_slicing(__lowerCamelCase  )

 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




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

     __A    :						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(__lowerCamelCase					,						__lowerCamelCase  )

 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 					UpperCamelCase__(    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(__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()
 def __call__(    self					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase					,						__lowerCamelCase = 512					,						__lowerCamelCase = 512					,						__lowerCamelCase = 50					,						__lowerCamelCase = 7.5					,						__lowerCamelCase = None					,						__lowerCamelCase = 1					,						__lowerCamelCase = 0.0					,						__lowerCamelCase = None					,						__lowerCamelCase = None					,						__lowerCamelCase = "pil"					,						__lowerCamelCase = True					,						__lowerCamelCase = None					,						__lowerCamelCase = 1					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     __A    :						Dict						 =     self.segmentation_processor(
         text=[text]					,						images=[image]					,						padding='''max_length'''					,						return_tensors='''pt'''  ).to(self.device  )
     __A    :						int						 =     self.segmentation_model(**__lowerCamelCase  )
     __A    :						List[str]						 =     torch.sigmoid(outputs.logits  ).cpu().detach().unsqueeze(-1  ).numpy()
     __A    :						str						 =     self.numpy_to_pil(__lowerCamelCase  )[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=__lowerCamelCase					,						image=__lowerCamelCase					,						mask_image=__lowerCamelCase					,						height=__lowerCamelCase					,						width=__lowerCamelCase					,						num_inference_steps=__lowerCamelCase					,						guidance_scale=__lowerCamelCase					,						negative_prompt=__lowerCamelCase					,						num_images_per_prompt=__lowerCamelCase					,						eta=__lowerCamelCase					,						generator=__lowerCamelCase					,						latents=__lowerCamelCase					,						output_type=__lowerCamelCase					,						return_dict=__lowerCamelCase					,						callback=__lowerCamelCase					,						callback_steps=__lowerCamelCase					,						)











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_ =			{
    """configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
    """tokenization_perceiver""": ["""PerceiverTokenizer"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			["""PerceiverFeatureExtractor"""]
       a_ =			["""PerceiverImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_ =			[
           """PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """PerceiverForImageClassificationConvProcessing""",
           """PerceiverForImageClassificationFourier""",
           """PerceiverForImageClassificationLearned""",
           """PerceiverForMaskedLM""",
           """PerceiverForMultimodalAutoencoding""",
           """PerceiverForOpticalFlow""",
           """PerceiverForSequenceClassification""",
           """PerceiverLayer""",
           """PerceiverModel""",
           """PerceiverPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
       from .tokenization_perceiver import PerceiverTokenizer

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_perceiver import PerceiverFeatureExtractor
              from .image_processing_perceiver import PerceiverImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_perceiver import (
                  PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PerceiverForImageClassificationConvProcessing,
                  PerceiverForImageClassificationFourier,
                  PerceiverForImageClassificationLearned,
                  PerceiverForMaskedLM,
                  PerceiverForMultimodalAutoencoding,
                  PerceiverForOpticalFlow,
                  PerceiverForSequenceClassification,
                  PerceiverLayer,
                  PerceiverModel,
                  PerceiverPreTrainedModel,
              )

else:
       import sys

       a_ =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




