



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						:Optional[int]      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''microsoft/table-transformer-detection''': (
        '''https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'''
    ),
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''table-transformer'''
       UpperCamelCase__  :    Tuple											=						['''past_key_values''']
       UpperCamelCase__  :    List[str]											=						{
           '''hidden_size''': '''d_model''',
           '''num_attention_heads''': '''encoder_attention_heads''',
       }



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=None     ,		__SCREAMING_SNAKE_CASE							:     Tuple=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=100     ,		__SCREAMING_SNAKE_CASE							:     Tuple=6     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     Any=8     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=6     ,		__SCREAMING_SNAKE_CASE							:     str=2_048     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=0.0     ,		__SCREAMING_SNAKE_CASE							:     int=True     ,		__SCREAMING_SNAKE_CASE							:     Dict="relu"     ,		__SCREAMING_SNAKE_CASE							:     int=256     ,		__SCREAMING_SNAKE_CASE							:     Tuple=0.1     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.0     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.02     ,		__SCREAMING_SNAKE_CASE							:     Any=1.0     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     Dict="sine"     ,		__SCREAMING_SNAKE_CASE							:     str="resnet50"     ,		__SCREAMING_SNAKE_CASE							:     Tuple=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=1     ,		__SCREAMING_SNAKE_CASE							:     Dict=5     ,		__SCREAMING_SNAKE_CASE							:     str=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=5     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=2     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.1     ,		**__SCREAMING_SNAKE_CASE							:     str     ,		):


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

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



       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           return self.encoder_attention_heads





       @property
       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           return self.d_model


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Union[str, Any]											=						version.parse('''1.11'''    )



       @property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

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



       @property
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           return 1E-5





       @property
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           return 12






from collections import defaultdict




def       _snake_case	(   lowerCAmelCase							:   int	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any				      =    1
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    True
		for v in tree[start]:
				if v not in visited:
						ret += dfs(lowerCAmelCase	)
		if ret % 2 == 0:
				cuts.append(lowerCAmelCase	)
		return ret




def       _snake_case	(   ):
		"""simple docstring"""







		dfs(1	)


if __name__ == "__main__":
		__lowerCamelCase   ,						__lowerCamelCase					:	Union[str, Any]       =  10, 9
		__lowerCamelCase					:	Optional[int]       =  defaultdict(list)
		__lowerCamelCase					:	dict[int, bool]       =  {}
		__lowerCamelCase					:	list[int]       =  []
		__lowerCamelCase					:	Optional[Any]       =  0
		__lowerCamelCase					:	Any       =  [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
		for u, v in edges:
				tree[u].append(v)
				tree[v].append(u)
		even_tree()
		print(len(cuts) - 1)
