
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


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





class       _snake_case					(     lowerCAmelCase_				):







			def __init__(				self						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   _a						,   ):
				super().__init__()

				if hasattr(scheduler.config						,   "steps_offset"		) and scheduler.config.steps_offset != 1:
					__magic_name__		:		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					    f'''The configuration file of this scheduler: {scheduler} is outdated.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"						,   lowerCamelCase__						,   standard_warn=lowerCamelCase__		)
					__magic_name__		:		Optional[int]	   =     dict(scheduler.config		)
					__magic_name__		:		Union[str, Any]	   =     1
					__magic_name__		:		Union[str, Any]	   =     FrozenDict(lowerCamelCase__		)

				if hasattr(scheduler.config						,   "skip_prk_steps"		) and scheduler.config.skip_prk_steps is False:
					__magic_name__		:		Union[str, Any]	   =     (
					    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__		)
					__magic_name__		:		Optional[Any]	   =     dict(scheduler.config		)
					__magic_name__		:		List[str]	   =     True
					__magic_name__		:		Dict	   =     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 			SCREAMING_SNAKE_CASE    (				self						,   _a = "auto"		):


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







			def 			SCREAMING_SNAKE_CASE    (				self		):
				self.enable_attention_slicing(lowerCamelCase__		)







			def 			SCREAMING_SNAKE_CASE    (				self		):


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

				__magic_name__		:		List[Any]	   =     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 			SCREAMING_SNAKE_CASE    (				self		):


				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						,   _a						,   _a						,   _a						,   _a = 512						,   _a = 512						,   _a = 50						,   _a = 7.5						,   _a = None						,   _a = 1						,   _a = 0.0						,   _a = None						,   _a = None						,   _a = "pil"						,   _a = True						,   _a = None						,   _a = 1						,   **_a						,   ):
				__magic_name__		:		Dict	   =     self.segmentation_processor(
				    text=[text]						,   images=[image]						,   padding="max_length"						,   return_tensors="pt"		).to(self.device		)
				__magic_name__		:		Optional[int]	   =     self.segmentation_model(**lowerCamelCase__		)
				__magic_name__		:		Any	   =     torch.sigmoid(outputs.logits		).cpu().detach().unsqueeze(-1		).numpy()
				__magic_name__		:		Tuple	   =     self.numpy_to_pil(lowerCamelCase__		)[0].resize(image.size		)

				# Run inpainting pipeline with the generated mask
				__magic_name__		:		Dict	   =     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__						,   )







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	Union[str, Any]			=			"table-transformer"
     __snake_case    :	Union[str, Any]			=			["past_key_values"]
     __snake_case    :	List[Any]			=			{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    Optional[Any]=None						,lowerCamelCase__						:    List[Any]=3						,lowerCamelCase__						:    Optional[int]=100						,lowerCamelCase__						:    List[Any]=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    List[Any]=8						,lowerCamelCase__						:    Dict=6						,lowerCamelCase__						:    Dict=2048						,lowerCamelCase__						:    Any=8						,lowerCamelCase__						:    Optional[int]=0.0						,lowerCamelCase__						:    int=0.0						,lowerCamelCase__						:    List[Any]=True						,lowerCamelCase__						:    Optional[int]="relu"						,lowerCamelCase__						:    Tuple=256						,lowerCamelCase__						:    Any=0.1						,lowerCamelCase__						:    Optional[Any]=0.0						,lowerCamelCase__						:    Tuple=0.0						,lowerCamelCase__						:    List[Any]=0.02						,lowerCamelCase__						:    int=1.0						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    Optional[Any]="sine"						,lowerCamelCase__						:    List[str]="resnet50"						,lowerCamelCase__						:    Optional[Any]=True						,lowerCamelCase__						:    List[str]=False						,lowerCamelCase__						:    int=1						,lowerCamelCase__						:    Dict=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    Union[str, Any]=1						,lowerCamelCase__						:    str=1						,lowerCamelCase__						:    Any=5						,lowerCamelCase__						:    Tuple=2						,lowerCamelCase__						:    str=0.1						,**lowerCamelCase__						:    List[str]						,)     ->		Optional[int]:




           '''simple docstring'''


           if backbone_config is not None and use_timm_backbone:
                 raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`."""       )

           if not use_timm_backbone:
                 if backbone_config is None:
                       logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]       )
                 elif isinstance(lowerCamelCase__						,lowerCamelCase__       ):
                       SCREAMING_SNAKE_CASE		=						backbone_config.get("""model_type"""       )
                       SCREAMING_SNAKE_CASE		=						CONFIG_MAPPING[backbone_model_type]
                       SCREAMING_SNAKE_CASE		=						config_class.from_dict(lowerCamelCase__       )
                 # set timm attributes to None
                 SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE,					SCREAMING_SNAKE_CASE		=						None, None, None

           SCREAMING_SNAKE_CASE		=						use_timm_backbone
           SCREAMING_SNAKE_CASE		=						backbone_config
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						num_queries
           SCREAMING_SNAKE_CASE		=						d_model
           SCREAMING_SNAKE_CASE		=						encoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						encoder_attention_heads
           SCREAMING_SNAKE_CASE		=						decoder_ffn_dim
           SCREAMING_SNAKE_CASE		=						decoder_layers
           SCREAMING_SNAKE_CASE		=						decoder_attention_heads
           SCREAMING_SNAKE_CASE		=						dropout
           SCREAMING_SNAKE_CASE		=						attention_dropout
           SCREAMING_SNAKE_CASE		=						activation_dropout
           SCREAMING_SNAKE_CASE		=						activation_function
           SCREAMING_SNAKE_CASE		=						init_std
           SCREAMING_SNAKE_CASE		=						init_xavier_std
           SCREAMING_SNAKE_CASE		=						encoder_layerdrop
           SCREAMING_SNAKE_CASE		=						decoder_layerdrop
           SCREAMING_SNAKE_CASE		=						encoder_layers
           SCREAMING_SNAKE_CASE		=						auxiliary_loss
           SCREAMING_SNAKE_CASE		=						position_embedding_type
           SCREAMING_SNAKE_CASE		=						backbone
           SCREAMING_SNAKE_CASE		=						use_pretrained_backbone
           SCREAMING_SNAKE_CASE		=						dilation
           # Hungarian matcher
           SCREAMING_SNAKE_CASE		=						class_cost
           SCREAMING_SNAKE_CASE		=						bbox_cost
           SCREAMING_SNAKE_CASE		=						giou_cost
           # Loss coefficients
           SCREAMING_SNAKE_CASE		=						mask_loss_coefficient
           SCREAMING_SNAKE_CASE		=						dice_loss_coefficient
           SCREAMING_SNAKE_CASE		=						bbox_loss_coefficient
           SCREAMING_SNAKE_CASE		=						giou_loss_coefficient
           SCREAMING_SNAKE_CASE		=						eos_coefficient
           super().__init__(is_encoder_decoder=lowerCamelCase__						,**lowerCamelCase__       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[Any]       )     ->		int:




           '''simple docstring'''


           return self.d_model

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			version.parse("1.11"   )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Mapping[str, Mapping[int, str]]:




           '''simple docstring'''


           return OrderedDict(
               [
                   ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
                   ("""pixel_mask""", {0: """batch"""}),
               ]       )






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Optional[int]       )     ->		float:




           '''simple docstring'''


           return 1e-5






     @property
     def 				SCREAMING_SNAKE_CASE__  (							self						:    Any       )     ->		int:




           '''simple docstring'''


           return 12


