





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


UpperCamelCase__      =logging.get_logger(__name__)

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





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    'table-transformer'
       __snake_case 			=    ['past_key_values']
       __snake_case 			=    {
           'hidden_size': 'd_model',
           'num_attention_heads': 'encoder_attention_heads',
       }




       def __init__(      self    ,						__lowerCamelCase=True    ,						__lowerCamelCase=None    ,						__lowerCamelCase=3    ,						__lowerCamelCase=1_0_0    ,						__lowerCamelCase=6    ,						__lowerCamelCase=2_0_4_8    ,						__lowerCamelCase=8    ,						__lowerCamelCase=6    ,						__lowerCamelCase=2_0_4_8    ,						__lowerCamelCase=8    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=True    ,						__lowerCamelCase="relu"    ,						__lowerCamelCase=2_5_6    ,						__lowerCamelCase=0.1    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.0    ,						__lowerCamelCase=0.02    ,						__lowerCamelCase=1.0    ,						__lowerCamelCase=False    ,						__lowerCamelCase="sine"    ,						__lowerCamelCase="resnet50"    ,						__lowerCamelCase=True    ,						__lowerCamelCase=False    ,						__lowerCamelCase=1    ,						__lowerCamelCase=5    ,						__lowerCamelCase=2    ,						__lowerCamelCase=1    ,						__lowerCamelCase=1    ,						__lowerCamelCase=5    ,						__lowerCamelCase=2    ,						__lowerCamelCase=0.1    ,						**__lowerCamelCase    ,						)			->     Optional[Any]:
            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       :	Union[str, Any]						    =							CONFIG_MAPPING["resnet"](out_features=["stage4"]							)
                 elif isinstance(__lowerCamelCase    ,						__lowerCamelCase							):
                      _SCREAMING_SNAKE_CASE       :	Dict						    =							backbone_config.get("model_type"							)
                      _SCREAMING_SNAKE_CASE       :	str						    =							CONFIG_MAPPING[backbone_model_type]
                      _SCREAMING_SNAKE_CASE       :	Tuple						    =							config_class.from_dict(__lowerCamelCase							)
                 # set timm attributes to None
                 _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							None, None, None

            _SCREAMING_SNAKE_CASE       :	List[str]						    =							use_timm_backbone
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							backbone_config
            _SCREAMING_SNAKE_CASE       :	int						    =							num_channels
            _SCREAMING_SNAKE_CASE       :	int						    =							num_queries
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							d_model
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							encoder_ffn_dim
            _SCREAMING_SNAKE_CASE       :	int						    =							encoder_layers
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							encoder_attention_heads
            _SCREAMING_SNAKE_CASE       :	Optional[int]						    =							decoder_ffn_dim
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							decoder_layers
            _SCREAMING_SNAKE_CASE       :	str						    =							decoder_attention_heads
            _SCREAMING_SNAKE_CASE       :	Any						    =							dropout
            _SCREAMING_SNAKE_CASE       :	str						    =							attention_dropout
            _SCREAMING_SNAKE_CASE       :	str						    =							activation_dropout
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							activation_function
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							init_std
            _SCREAMING_SNAKE_CASE       :	Dict						    =							init_xavier_std
            _SCREAMING_SNAKE_CASE       :	Dict						    =							encoder_layerdrop
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							decoder_layerdrop
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							encoder_layers
            _SCREAMING_SNAKE_CASE       :	int						    =							auxiliary_loss
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							position_embedding_type
            _SCREAMING_SNAKE_CASE       :	List[Any]						    =							backbone
            _SCREAMING_SNAKE_CASE       :	str						    =							use_pretrained_backbone
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							dilation
            # Hungarian matcher
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							class_cost
            _SCREAMING_SNAKE_CASE       :	str						    =							bbox_cost
            _SCREAMING_SNAKE_CASE       :	int						    =							giou_cost
            # Loss coefficients
            _SCREAMING_SNAKE_CASE       :	List[str]						    =							mask_loss_coefficient
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							dice_loss_coefficient
            _SCREAMING_SNAKE_CASE       :	Union[str, Any]						    =							bbox_loss_coefficient
            _SCREAMING_SNAKE_CASE       :	Tuple						    =							giou_loss_coefficient
            _SCREAMING_SNAKE_CASE       :	Optional[Any]						    =							eos_coefficient
            super().__init__(is_encoder_decoder=__lowerCamelCase    ,						**__lowerCamelCase							)




       @property
       def     UpperCamelCase_			(      self							)			->     int:
            return self.encoder_attention_heads







       @property
       def     UpperCamelCase_			(      self							)			->     int:
            return self.d_model





class 			lowerCAmelCase__( __lowercase    ):






       '''simple docstring'''


       __snake_case 			=    version.parse('1.11'    )




       @property
       def     UpperCamelCase_			(      self							)			->     Mapping[str, Mapping[int, str]]:
            return OrderedDict(
                [
                    ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                    ("pixel_mask", {0: "batch"}),
                ]							)




       @property
       def     UpperCamelCase_			(      self							)			->     float:
            return 1E-5







       @property
       def     UpperCamelCase_			(      self							)			->     int:
            return 1_2






from PIL import Image






def     lowerCamelCase__			(__lowerCamelCase	):
     _SCREAMING_SNAKE_CASE    ,			_SCREAMING_SNAKE_CASE       :	List[str]						    =							image.size
     _SCREAMING_SNAKE_CASE       :	Tuple						    =							0
     _SCREAMING_SNAKE_CASE       :	Dict						    =							image.load()
     for i in range(__lowerCamelCase	):
          for j in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	List[Any]						    =							pixels[j, i]
               mean += pixel
     mean //= width * height

     for j in range(__lowerCamelCase	):
          for i in range(__lowerCamelCase	):
               _SCREAMING_SNAKE_CASE       :	int						    =							255 if pixels[i, j] > mean else 0
     return image


if __name__ == "__main__":
       UpperCamelCase__      =mean_threshold(Image.open('path_to_image').convert('L'))
       image.save('output_image_path')