






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       :       List[str]        =				logging.get_logger(__name__)

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

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

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



					def __init__(     self :		int						,   __a :		int=True						,   __a :		int=None						,   __a :		Tuple=3						,   __a :		Dict=100						,   __a :		List[str]=6						,   __a :		Any=2048						,   __a :		List[str]=8						,   __a :		List[str]=6						,   __a :		Tuple=2048						,   __a :		Tuple=8						,   __a :		Dict=0.0						,   __a :		List[str]=0.0						,   __a :		Optional[Any]=True						,   __a :		Optional[int]="relu"						,   __a :		Optional[int]=256						,   __a :		Optional[int]=0.1						,   __a :		Optional[int]=0.0						,   __a :		Optional[Any]=0.0						,   __a :		Optional[int]=0.02						,   __a :		List[Any]=1.0						,   __a :		Union[str, Any]=False						,   __a :		int="sine"						,   __a :		Union[str, Any]="resnet50"						,   __a :		Optional[int]=True						,   __a :		List[Any]=False						,   __a :		str=1						,   __a :		int=5						,   __a :		Union[str, Any]=2						,   __a :		List[Any]=1						,   __a :		Any=1						,   __a :		List[Any]=5						,   __a :		Optional[Any]=2						,   __a :		Optional[Any]=0.1						,   **__a :		Optional[int]						,   )	->     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."""							)
																										__lowercase					:							Any			=   CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
																			elif isinstance(__a						,   __a							):
																										__lowercase					:							Tuple			=   backbone_config.get("""model_type"""							)
																										__lowercase					:							List[str]			=   CONFIG_MAPPING[backbone_model_type]
																										__lowercase					:							str			=   config_class.from_dict(__a							)
																			# set timm attributes to None
																			__lowercase	,	__lowercase	,	__lowercase					:							str			=   None, None, None

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



					@property
					def 					lowerCAmelCase (     self :		Dict							)	->     int:


												"""simple docstring"""

												return self.encoder_attention_heads



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


												"""simple docstring"""

												return self.d_model







class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	int     =					version.parse('''1.11'''	)



					@property
					def 					lowerCAmelCase (     self :		str							)	->     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 					lowerCAmelCase (     self :		List[Any]							)	->     float:


												"""simple docstring"""

												return 1E-5



					@property
					def 					lowerCAmelCase (     self :		int							)	->     int:


												"""simple docstring"""

												return 12







lowerCamelCase       :       Tuple        =				{'''a''': ['''c''', '''b'''], '''b''': ['''d''', '''e'''], '''c''': [], '''d''': [], '''e''': []}
lowerCamelCase       :       int        =				['''a''', '''b''', '''c''', '''d''', '''e''']
def 					snake_case_						(   lowerCAmelCase_				:  Optional[int]				,				lowerCAmelCase_				:  str				,				lowerCAmelCase_				:  List[Any]					):
							__lowercase					:							Dict			=   start
							# add current to visited
							visited.append(lowerCAmelCase_					)
							__lowercase					:							Dict			=   edges[current]
							for neighbor in neighbors:
														# if neighbor not in visited, visit
														if neighbor not in visited:
																					__lowercase					:							List[Any]			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # if all neighbors visited add current to sort
							sort.append(lowerCAmelCase_					)
							# if all vertices haven't been visited select a new one to visit
							if len(lowerCAmelCase_					) != len(lowerCAmelCase_					):
														for vertice in vertices:
																					if vertice not in visited:
																												__lowercase					:							Tuple			=   topological_sort(lowerCAmelCase_				,				lowerCAmelCase_				,				lowerCAmelCase_					)
    # return sort
							return sort


if __name__ == "__main__":
	lowerCamelCase       :       Any        =				topological_sort('''a''', [], [])
	print(sort)