






import copy
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 : str						   =       logging.get_logger(__name__)

lowerCamelCase : Union[str, Any]						   =       {
    '''microsoft/conditional-detr-resnet-50''': (
        '''https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'''
    ),
}

class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

					_A						:			Tuple									=      '''conditional_detr'''
					_A						:			int									=      ['''past_key_values''']
					_A						:			Any									=      {
					    '''hidden_size''': '''d_model''',
					    '''num_attention_heads''': '''encoder_attention_heads''',
					}



					def __init__(  self				:	Optional[int]		,     __a				:	Optional[int]=True		,     __a				:	Dict=None		,     __a				:	int=3		,     __a				:	List[str]=300		,     __a				:	Optional[Any]=6		,     __a				:	Optional[Any]=2048		,     __a				:	str=8		,     __a				:	Tuple=6		,     __a				:	Tuple=2048		,     __a				:	List[str]=8		,     __a				:	Optional[Any]=0.0		,     __a				:	Tuple=0.0		,     __a				:	Union[str, Any]=True		,     __a				:	Dict="relu"		,     __a				:	Union[str, Any]=256		,     __a				:	Dict=0.1		,     __a				:	Tuple=0.0		,     __a				:	int=0.0		,     __a				:	Union[str, Any]=0.02		,     __a				:	Union[str, Any]=1.0		,     __a				:	Any=False		,     __a				:	Any="sine"		,     __a				:	List[str]="resnet50"		,     __a				:	str=True		,     __a				:	Tuple=False		,     __a				:	str=2		,     __a				:	Union[str, Any]=5		,     __a				:	Dict=2		,     __a				:	List[Any]=1		,     __a				:	int=1		,     __a				:	Dict=2		,     __a				:	Dict=5		,     __a				:	str=2		,     __a				:	List[str]=0.25		,     **__a				:	List[Any]		,     )			->  Dict:


												"""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      :							int				=     CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]							)
																			elif isinstance(__a		,     __a							):
																										__lowercase      :							Union[str, Any]				=     backbone_config.get("""model_type"""							)
																										__lowercase      :							Tuple				=     CONFIG_MAPPING[backbone_model_type]
																										__lowercase      :							Optional[int]				=     config_class.from_dict(__a							)

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



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


												"""simple docstring"""

												return self.encoder_attention_heads



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


												"""simple docstring"""

												return self.d_model



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     copy.deepcopy(self.__dict__							)
												if self.backbone_config is not None:
																			__lowercase      :							Optional[Any]				=     self.backbone_config.to_dict()
												__lowercase      :							Optional[int]				=     self.__class__.model_type
												return output







class 		lowerCAmelCase     ( __a			):

					'''simple docstring'''

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



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


												"""simple docstring"""

												return 12







from __future__ import annotations
def       snake_case_		(     lowerCAmelCase_       :						str       ,       lowerCAmelCase_       :						list[str] | None = None      ):
							__lowercase      :							Tuple				=     word_bank or []
							# create a table
							__lowercase      :							int				=     len(lowerCAmelCase_      ) + 1

							__lowercase      :							list[list[list[str]]]				=     []
							for _ in range(lowerCAmelCase_      ):
														table.append([]      )
							# seed value
							__lowercase      :							Dict				=     [[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(lowerCAmelCase_      ):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(lowerCAmelCase_      )] == word:
																																			__lowercase      :							list[list[str]]				=     [
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(lowerCAmelCase_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(lowerCAmelCase_      )]:
														combination.reverse()

							return table[len(lowerCAmelCase_      )]


if __name__ == "__main__":
	print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
	print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
	print(
	    all_construct(
	        '''hexagonosaurus''',
	        ['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
	    )
	)