






"""simple docstring"""





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


lowerCamelCase      =		logging.get_logger(__name__)

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







       '''simple docstring'''


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



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


             '''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."					)
                         UpperCAmelCase_    				=							CONFIG_MAPPING["resnet"](out_features=["stage4"]					)
                   elif isinstance(SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_					):
                         UpperCAmelCase_    				=							backbone_config.get("model_type"					)
                         UpperCAmelCase_    				=							CONFIG_MAPPING[backbone_model_type]
                         UpperCAmelCase_    				=							config_class.from_dict(SCREAMING_SNAKE_CASE_					)
                   # set timm attributes to None
                   UpperCAmelCase_				,					UpperCAmelCase_				,					UpperCAmelCase_    				=							None, None, None

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



       @property
       def   lowercase__       (							self    :						List[str]					)				->		Tuple:


             '''simple docstring'''




             return self.encoder_attention_heads







       @property
       def   lowercase__       (							self    :						Any					)				->		str:


             '''simple docstring'''




             return self.d_model



class    lowercase__     (					__lowerCamelCase   ):







       '''simple docstring'''


       UpperCamelCase		=  version.parse('''1.11'''   )



       @property
       def   lowercase__       (							self    :						Dict					)				->		Optional[int]:


             '''simple docstring'''




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



       @property
       def   lowercase__       (							self    :						List[str]					)				->		Dict:


             '''simple docstring'''




             return 1e-5







       @property
       def   lowercase__       (							self    :						List[str]					)				->		Tuple:


             '''simple docstring'''




             return 12









"""simple docstring"""





import colorsys

from PIL import Image  # type: ignore
def      a__			(   lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   ):
      UpperCAmelCase_    				=							x
      UpperCAmelCase_    				=							y
      for step in range(lowerCAmelCase__   ):  # noqa: B007
            UpperCAmelCase_    				=							a * a - b * b + x
            UpperCAmelCase_    				=							2 * a * b + y
            UpperCAmelCase_    				=							a_new

            # divergence happens for all complex number with an absolute value
            # greater than 4
            if a * a + b * b > 4:
                  break
      return step / (max_step - 1)
def      a__			(   lowerCAmelCase__   ):
      if distance == 1:
            return (0, 0, 0)
      else:
            return (255, 255, 255)
def      a__			(   lowerCAmelCase__   ):
      if distance == 1:
            return (0, 0, 0)
      else:
            return tuple(round(i * 255   ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__			,    1			,    1   )   )
def      a__			(   lowerCAmelCase__ = 800			,    lowerCAmelCase__ = 600			,    lowerCAmelCase__ = -0.6			,    lowerCAmelCase__ = 0			,    lowerCAmelCase__ = 3.2			,    lowerCAmelCase__ = 50			,    lowerCAmelCase__ = True			,    ):
      UpperCAmelCase_    				=							Image.new("RGB"			,    (image_width, image_height)   )
      UpperCAmelCase_    				=							img.load()

      # loop through the image-coordinates
      for image_x in range(lowerCAmelCase__   ):
            for image_y in range(lowerCAmelCase__   ):
                  # determine the figure-coordinates based on the image-coordinates
                  UpperCAmelCase_    				=							figure_width / image_width * image_height
                  UpperCAmelCase_    				=							figure_center_x + (image_x / image_width - 0.5) * figure_width
                  UpperCAmelCase_    				=							figure_center_y + (image_y / image_height - 0.5) * figure_height

                  UpperCAmelCase_    				=							get_distance(lowerCAmelCase__			,    lowerCAmelCase__			,    lowerCAmelCase__   )

                  # color the corresponding pixel based on the selected coloring-function
                  if use_distance_color_coding:
                        UpperCAmelCase_    				=							get_color_coded_rgb(lowerCAmelCase__   )
                  else:
                        UpperCAmelCase_    				=							get_black_and_white_rgb(lowerCAmelCase__   )

      return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  lowerCamelCase      =		get_image()

  # uncomment for colored version, different section, zoomed in
  # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
  # figure_width = 0.8)

  # uncomment for black and white version, full figure
  # img = get_image(use_distance_color_coding = False)

  # uncomment to save the image
  # img.save("mandelbrot.png")

  img.show()




