



import copy
from typing import Dict, List, Optional

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


A__  :  Optional[Any]								=					{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A__  :  Dict								=					logging.get_logger(__name__)



class 				__snake_case						(     UpperCamelCase_						):
							_a			      = '''mask2former'''
							_a			      = ['''swin''']
							_a			      = {'''hidden_size''': '''hidden_dim'''}




							def __init__(       self     :       Any				,				A_     :       Optional[Dict] = None				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 2_5_6				,				A_     :       int = 1_0_2_4				,				A_     :       str = "relu"				,				A_     :       int = 6				,				A_     :       int = 1_0				,				A_     :       int = 8				,				A_     :       float = 0.0				,				A_     :       int = 2_0_4_8				,				A_     :       bool = False				,				A_     :       bool = False				,				A_     :       int = 4				,				A_     :       int = 2_5_5				,				A_     :       int = 1_0_0				,				A_     :       float = 0.1				,				A_     :       float = 2.0				,				A_     :       float = 5.0				,				A_     :       float = 5.0				,				A_     :       int = 1_2_5_4_4				,				A_     :       float = 3.0				,				A_     :       float = 0.75				,				A_     :       float = 0.02				,				A_     :       float = 1.0				,				A_     :       bool = True				,				A_     :       List[int] = [4, 8, 1_6, 3_2]				,				A_     :       bool = None				,				**A_     :       Dict				,				):
									if backbone_config is None:
											logger.info('''`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.''')
											lowerCAmelCase_			:     int									= CONFIG_MAPPING['''swin'''](
											    image_size=2_2_4				,				in_channels=3				,				patch_size=4				,				embed_dim=9_6				,				depths=[2, 2, 1_8, 2]				,				num_heads=[3, 6, 1_2, 2_4]				,				window_size=7				,				drop_path_rate=0.3				,				use_absolute_embeddings=A_				,				out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']				,				)

									if isinstance(A_				,				A_):
											lowerCAmelCase_			:     List[Any]									= backbone_config.pop('''model_type''')
											lowerCAmelCase_			:     Optional[Any]									= CONFIG_MAPPING[backbone_model_type]
											lowerCAmelCase_			:     List[Any]									= config_class.from_dict(A_)

									# verify that the backbone is supported
									if backbone_config.model_type not in self.backbones_supported:
											logger.warning_once(
											    F"""Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. """
											    F"""Supported model types: {",".join(self.backbones_supported)}""")

									lowerCAmelCase_			:     List[Any]									= backbone_config
									lowerCAmelCase_			:     str									= feature_size
									lowerCAmelCase_			:     Optional[Any]									= mask_feature_size
									lowerCAmelCase_			:     int									= hidden_dim
									lowerCAmelCase_			:     int									= encoder_feedforward_dim
									lowerCAmelCase_			:     Optional[int]									= activation_function
									lowerCAmelCase_			:     Any									= encoder_layers
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers
									lowerCAmelCase_			:     Optional[Any]									= num_attention_heads
									lowerCAmelCase_			:     Optional[int]									= dropout
									lowerCAmelCase_			:     List[str]									= dim_feedforward
									lowerCAmelCase_			:     Optional[Any]									= pre_norm
									lowerCAmelCase_			:     List[str]									= enforce_input_projection
									lowerCAmelCase_			:     Tuple									= common_stride
									lowerCAmelCase_			:     Optional[Any]									= ignore_value
									lowerCAmelCase_			:     Optional[Any]									= num_queries
									lowerCAmelCase_			:     int									= no_object_weight
									lowerCAmelCase_			:     Tuple									= class_weight
									lowerCAmelCase_			:     int									= mask_weight
									lowerCAmelCase_			:     Dict									= dice_weight
									lowerCAmelCase_			:     str									= train_num_points
									lowerCAmelCase_			:     Dict									= oversample_ratio
									lowerCAmelCase_			:     Tuple									= importance_sample_ratio
									lowerCAmelCase_			:     List[str]									= init_std
									lowerCAmelCase_			:     List[str]									= init_xavier_std
									lowerCAmelCase_			:     Optional[Any]									= use_auxiliary_loss
									lowerCAmelCase_			:     List[Any]									= feature_strides
									lowerCAmelCase_			:     int									= output_auxiliary_logits
									lowerCAmelCase_			:     Optional[Any]									= decoder_layers

									super().__init__(**A_)




							@classmethod
							def    UpperCAmelCase__			(       cls     :       List[str]				,				A_     :       PretrainedConfig				,				**A_     :       List[Any]):
									return cls(
									    backbone_config=A_				,				**A_				,				)







							def    UpperCAmelCase__			(       self     :       List[Any]):
									lowerCAmelCase_			:     str									= copy.deepcopy(self.__dict__)
									lowerCAmelCase_			:     Dict									= self.backbone_config.to_dict()
									lowerCAmelCase_			:     Optional[int]									= self.__class__.model_type
									return output






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







class __snake_case				(						lowerCamelCase_		):




   def __init__(    self			:				Dict       ,	_lowercase			:				CLIPSegForImageSegmentation       ,	_lowercase			:				CLIPSegProcessor       ,	_lowercase			:				AutoencoderKL       ,	_lowercase			:				CLIPTextModel       ,	_lowercase			:				CLIPTokenizer       ,	_lowercase			:				UNetaDConditionModel       ,	_lowercase			:				Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]       ,	_lowercase			:				StableDiffusionSafetyChecker       ,	_lowercase			:				CLIPImageProcessor       ,	):



     """simple docstring"""






     super().__init__()

     if hasattr(scheduler.config       ,	"""steps_offset"""      ) and scheduler.config.steps_offset != 1:
       SCREAMING_SNAKE_CASE__										=      (
           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"""       ,	_lowercase       ,	standard_warn=_lowercase      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      1
       SCREAMING_SNAKE_CASE__										=      FrozenDict(_lowercase      )

     if hasattr(scheduler.config       ,	"""skip_prk_steps"""      ) and scheduler.config.skip_prk_steps is False:
       SCREAMING_SNAKE_CASE__										=      (
           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"""       ,	_lowercase       ,	standard_warn=_lowercase      )
       SCREAMING_SNAKE_CASE__										=      dict(scheduler.config      )
       SCREAMING_SNAKE_CASE__										=      True
       SCREAMING_SNAKE_CASE__										=      FrozenDict(_lowercase      )

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




   def 	__a						(    self			:				List[Any]       ,	_lowercase			:				Optional[Union[str, int]] = "auto"      ):



     """simple docstring"""






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




   def 	__a						(    self			:				Any      ):



     """simple docstring"""






     self.enable_attention_slicing(_lowercase      )




   def 	__a						(    self			:				Optional[int]      ):



     """simple docstring"""






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

     SCREAMING_SNAKE_CASE__										=      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(_lowercase       ,	_lowercase      )




   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 	__a						(    self			:				Optional[int]      ):



     """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(_lowercase       ,	"""_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[Any]       ,	_lowercase			:				Union[str, List[str]]       ,	_lowercase			:				Union[torch.FloatTensor, PIL.Image.Image]       ,	_lowercase			:				str       ,	_lowercase			:				int = 5_12       ,	_lowercase			:				int = 5_12       ,	_lowercase			:				int = 50       ,	_lowercase			:				float = 7.5       ,	_lowercase			:				Optional[Union[str, List[str]]] = None       ,	_lowercase			:				Optional[int] = 1       ,	_lowercase			:				float = 0.0       ,	_lowercase			:				Optional[torch.Generator] = None       ,	_lowercase			:				Optional[torch.FloatTensor] = None       ,	_lowercase			:				Optional[str] = "pil"       ,	_lowercase			:				bool = True       ,	_lowercase			:				Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,	_lowercase			:				int = 1       ,	**_lowercase			:				str       ,	):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      self.segmentation_processor(
         text=[text]       ,	images=[image]       ,	padding="""max_length"""       ,	return_tensors="""pt"""      ).to(self.device      )
     SCREAMING_SNAKE_CASE__										=      self.segmentation_model(**_lowercase      )
     SCREAMING_SNAKE_CASE__										=      torch.sigmoid(outputs.logits      ).cpu().detach().unsqueeze(-1      ).numpy()
     SCREAMING_SNAKE_CASE__										=      self.numpy_to_pil(_lowercase      )[0].resize(image.size      )

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