





"""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


UpperCAmelCase_ 							=       logging.get_logger(__name__)

UpperCAmelCase_ 							=       {
    'microsoft/table-transformer-detection': (
        'https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json'
    ),
}
class      UpperCamelCase_						(					lowerCAmelCase__   ):
					lowerCAmelCase_							=		"table-transformer"
					lowerCAmelCase_							=		["past_key_values"]
					lowerCAmelCase_							=		{
					    "hidden_size": "d_model",
					    "num_attention_heads": "encoder_attention_heads",
					}




					def __init__(						self			,  lowerCAmelCase_=True			,  lowerCAmelCase_=None			,  lowerCAmelCase_=3			,  lowerCAmelCase_=100			,  lowerCAmelCase_=6			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=8			,  lowerCAmelCase_=6			,  lowerCAmelCase_=2048			,  lowerCAmelCase_=8			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=0.0			,  lowerCAmelCase_=True			,  lowerCAmelCase_="relu"			,  lowerCAmelCase_=256			,  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[int]:
										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.'					)
																				_snake_case													=			CONFIG_MAPPING['resnet'](out_features=['stage4']					)
															elif isinstance(_SCREAMING_SNAKE_CASE			,  _SCREAMING_SNAKE_CASE					):
																				_snake_case													=			backbone_config.get('model_type'					)
																				_snake_case													=			CONFIG_MAPPING[backbone_model_type]
																				_snake_case													=			config_class.from_dict(_SCREAMING_SNAKE_CASE					)
															# set timm attributes to None
															_snake_case							,       _snake_case							,       _snake_case													=			None, None, None

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




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






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





class      UpperCamelCase_						(					lowerCAmelCase__   ):
					lowerCAmelCase_							=		version.parse('''1.11'''   )




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






					@property
					def 		lowerCAmelCase     (						self					)						->							int:
										return 12










def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    int    ,      UpperCamelCase__     :    list[int]  )     ->	bool:



					'''simple docstring'''




					if graph[path[curr_ind - 1]][next_ver] == 0:
										return False

					# 2. Validate that next vertex is not already in path
					return not any(vertex == next_ver for vertex in path  )





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    list[int]    ,      UpperCamelCase__     :    int  )     ->	bool:



					'''simple docstring'''




					if curr_ind == len(UpperCamelCase__  ):
										# return whether path exists between current and starting vertices
										return graph[path[curr_ind - 1]][path[0]] == 1

					# Recursive Step
					for next_ver in range(0    ,      len(UpperCamelCase__  )  ):
										if valid_connection(UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__    ,      UpperCamelCase__  ):
															# Insert current vertex  into path as next transition
															_snake_case													=			next_ver
															# Validate created path
															if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      curr_ind + 1  ):
																				return True
															# Backtrack
															_snake_case													=			-1
					return False





def   lowerCamelCase__       (       UpperCamelCase__     :    list[list[int]]    ,      UpperCamelCase__     :    int = 0  )     ->	list[int]:



					'''simple docstring'''




					_snake_case													=			[-1] * (len(UpperCamelCase__  ) + 1)
					# initialize start and end of path with starting index
					_snake_case													=			_snake_case													=			start_index
					# evaluate and if we find answer return path either return empty array
					return path if util_hamilton_cycle(UpperCamelCase__    ,      UpperCamelCase__    ,      1  ) else []



