





'''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


snake_case_     =	logging.get_logger(__name__)

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


class   _lowercase						(							a				):
   _UpperCamelCase       		=   """table-transformer"""
   _UpperCamelCase       		=   ["""past_key_values"""]
   _UpperCamelCase       		=   {
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }


   def __init__( self    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=None    ,   _UpperCAmelCase=3    ,   _UpperCAmelCase=100    ,   _UpperCAmelCase=6    ,   _UpperCAmelCase=2_048    ,   _UpperCAmelCase=8    ,   _UpperCAmelCase=6    ,   _UpperCAmelCase=2_048    ,   _UpperCAmelCase=8    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase="relu"    ,   _UpperCAmelCase=256    ,   _UpperCAmelCase=0.1    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.0    ,   _UpperCAmelCase=0.02    ,   _UpperCAmelCase=1.0    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase="sine"    ,   _UpperCAmelCase="resnet50"    ,   _UpperCAmelCase=True    ,   _UpperCAmelCase=False    ,   _UpperCAmelCase=1    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=1    ,   _UpperCAmelCase=1    ,   _UpperCAmelCase=5    ,   _UpperCAmelCase=2    ,   _UpperCAmelCase=0.1    ,   **_UpperCAmelCase    ,   ):
       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.'''					)
               A       :       List[Any]							=						CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
           elif isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               A       :       List[Any]							=						backbone_config.get('''model_type'''					)
               A       :       Tuple							=						CONFIG_MAPPING[backbone_model_type]
               A       :       str							=						config_class.from_dict(_UpperCAmelCase					)
           # set timm attributes to None
           A       :       List[str]							=						None, None, None

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


   @property
   def 					snake_case				( self					):
       return self.encoder_attention_heads


   @property
   def 					snake_case				( self					):
       return self.d_model







class   _lowercase						(							a				):
   _UpperCamelCase       		=   version.parse("""1.11"""				)


   @property
   def 					snake_case				( self					):
       return OrderedDict(
           [
               ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
               ('''pixel_mask''', {0: '''batch'''}),
           ]					)


   @property
   def 					snake_case				( self					):
       return 1E-5


   @property
   def 					snake_case				( self					):
       return 12







'''simple docstring'''






from __future__ import annotations


class   _lowercase						:


   def __init__( self    ,   _UpperCAmelCase					):
       A       :       str							=						data
       A       :       Node | None							=						None
       A       :       Node | None							=						None







def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  None:  # In Order traversal of the tree
    if tree:
        display(tree.left		)
        print(tree.data		)
        display(tree.right		)
def 							_lowerCamelCase(  UpperCamelCase__    : Node | None		)		->  int:
    return 1 + max(depth_of_tree(tree.left		)    ,       depth_of_tree(tree.right		)		) if tree else 0
def 							_lowerCamelCase(  UpperCamelCase__    : Node		)		->  bool:
    if not tree:
        return True
    if tree.left and tree.right:
        return is_full_binary_tree(tree.left		) and is_full_binary_tree(tree.right		)
    else:
        return not tree.left and not tree.right
def 							_lowerCamelCase(  )		->  None:  # Main function for testing.
    A       :       Optional[int]							=						Node(1		)
    A       :       Tuple							=						Node(2		)
    A       :       Dict							=						Node(3		)
    A       :       List[str]							=						Node(4		)
    A       :       Union[str, Any]							=						Node(5		)
    A       :       str							=						Node(6		)
    A       :       Any							=						Node(7		)
    A       :       str							=						Node(8		)
    A       :       Optional[int]							=						Node(9		)

    print(is_full_binary_tree(UpperCamelCase__		)		)
    print(depth_of_tree(UpperCamelCase__		)		)
    print('''Tree is: '''		)
    display(UpperCamelCase__		)


if __name__ == "__main__":
     main()
