'''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


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




class 			__A (					UpperCamelCase__      ):



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

         if hasattr(scheduler.config  ,     "steps_offset"					) and scheduler.config.steps_offset != 1:
              UpperCAmelCase_			       =  (
                  f"""The configuration file of this scheduler: {scheduler} is outdated.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					)
              UpperCAmelCase_			       =  dict(scheduler.config					)
              UpperCAmelCase_			       =  1
              UpperCAmelCase_			       =  FrozenDict(__a					)

         if hasattr(scheduler.config  ,     "skip_prk_steps"					) and scheduler.config.skip_prk_steps is False:
              UpperCAmelCase_			       =  (
                  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					)
              UpperCAmelCase_			       =  dict(scheduler.config					)
              UpperCAmelCase_			       =  True
              UpperCAmelCase_			       =  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    _lowercase		(self       :       str  ,     __a       :       Optional[Union[str, int]] = "auto"					):
         if slice_size == "auto":
              # half the attention head size is usually a good trade-off between
              # speed and memory
              UpperCAmelCase_			       =  self.unet.config.attention_head_dim // 2
         self.unet.set_attention_slice(__a					)



    def    _lowercase		(self       :       int					):
         self.enable_attention_slicing(__a					)



    def    _lowercase		(self       :       Optional[Any]					):
         if is_accelerate_available():
              from accelerate import cpu_offload
         else:
              raise ImportError("Please install accelerate via `pip install accelerate`"					)

         UpperCAmelCase_			       =  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    _lowercase		(self       :       Optional[int]					):
         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       :       Dict  ,     __a       :       Union[str, List[str]]  ,     __a       :       Union[torch.FloatTensor, PIL.Image.Image]  ,     __a       :       str  ,     __a       :       int = 512  ,     __a       :       int = 512  ,     __a       :       int = 50  ,     __a       :       float = 7.5  ,     __a       :       Optional[Union[str, List[str]]] = None  ,     __a       :       Optional[int] = 1  ,     __a       :       float = 0.0  ,     __a       :       Optional[torch.Generator] = None  ,     __a       :       Optional[torch.FloatTensor] = None  ,     __a       :       Optional[str] = "pil"  ,     __a       :       bool = True  ,     __a       :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None  ,     __a       :       int = 1  ,     **__a       :       int  ,     ):
         UpperCAmelCase_			       =  self.segmentation_processor(
             text=[text]  ,     images=[image]  ,     padding="max_length"  ,     return_tensors="pt"					).to(self.device					)
         UpperCAmelCase_			       =  self.segmentation_model(**__a					)
         UpperCAmelCase_			       =  torch.sigmoid(outputs.logits					).cpu().detach().unsqueeze(-1					).numpy()
         UpperCAmelCase_			       =  self.numpy_to_pil(__a					)[0].resize(image.size					)

         # Run inpainting pipeline with the generated mask
         UpperCAmelCase_			       =  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"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase       :			Tuple   	=logging.get_logger(__name__)

__lowerCAmelCase       :			Dict   	={
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Optional[int]				    =      'deta'
      snake_case__     :  Dict				    =      {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(		self						,			__lowerCAmelCase=None						,			__lowerCAmelCase=900						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=6						,			__lowerCAmelCase=2048						,			__lowerCAmelCase=8						,			__lowerCAmelCase=6						,			__lowerCAmelCase=1024						,			__lowerCAmelCase=8						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase="relu"						,			__lowerCAmelCase=256						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0						,			__lowerCAmelCase=0.0_2						,			__lowerCAmelCase=1.0						,			__lowerCAmelCase=True						,			__lowerCAmelCase=False						,			__lowerCAmelCase="sine"						,			__lowerCAmelCase=5						,			__lowerCAmelCase=4						,			__lowerCAmelCase=4						,			__lowerCAmelCase=True						,			__lowerCAmelCase=300						,			__lowerCAmelCase=True						,			__lowerCAmelCase=True						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=1						,			__lowerCAmelCase=1						,			__lowerCAmelCase=5						,			__lowerCAmelCase=2						,			__lowerCAmelCase=0.1						,			__lowerCAmelCase=0.2_5						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            if backbone_config is None:
                  logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""		)
                  lowercase								=      CONFIG_MAPPING["""resnet"""](out_features=["""stage2""", """stage3""", """stage4"""]		)
            else:
                  if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		):
                        lowercase								=      backbone_config.pop("""model_type"""		)
                        lowercase								=      CONFIG_MAPPING[backbone_model_type]
                        lowercase								=      config_class.from_dict(__lowerCAmelCase		)

            lowercase								=      backbone_config
            lowercase								=      num_queries
            lowercase								=      max_position_embeddings
            lowercase								=      d_model
            lowercase								=      encoder_ffn_dim
            lowercase								=      encoder_layers
            lowercase								=      encoder_attention_heads
            lowercase								=      decoder_ffn_dim
            lowercase								=      decoder_layers
            lowercase								=      decoder_attention_heads
            lowercase								=      dropout
            lowercase								=      attention_dropout
            lowercase								=      activation_dropout
            lowercase								=      activation_function
            lowercase								=      init_std
            lowercase								=      init_xavier_std
            lowercase								=      encoder_layerdrop
            lowercase								=      auxiliary_loss
            lowercase								=      position_embedding_type
            # deformable attributes
            lowercase								=      num_feature_levels
            lowercase								=      encoder_n_points
            lowercase								=      decoder_n_points
            lowercase								=      two_stage
            lowercase								=      two_stage_num_proposals
            lowercase								=      with_box_refine
            lowercase								=      assign_first_stage
            if two_stage is True and with_box_refine is False:
                  raise ValueError("""If two_stage is True, with_box_refine must be True."""		)
            # Hungarian matcher
            lowercase								=      class_cost
            lowercase								=      bbox_cost
            lowercase								=      giou_cost
            # Loss coefficients
            lowercase								=      mask_loss_coefficient
            lowercase								=      dice_loss_coefficient
            lowercase								=      bbox_loss_coefficient
            lowercase								=      giou_loss_coefficient
            lowercase								=      eos_coefficient
            lowercase								=      focal_alpha
            super().__init__(is_encoder_decoder=__lowerCAmelCase						,			**__lowerCAmelCase		)

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.encoder_attention_heads

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return self.d_model







      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      copy.deepcopy(self.__dict__		)
            lowercase								=      self.backbone_config.to_dict()
            lowercase								=      self.__class__.model_type
            return output



