





'''simple docstring'''

import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCamelCase : Union[str, Any]											=			logging.get_logger(__name__)

__lowerCamelCase : Dict											=			{
    "facebook/detr-resnet-50": "https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json",
    # See all DETR models at https://huggingface.co/models?filter=detr
}

class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	Tuple 			=		'''detr'''
		UpperCAmelCase					:	List[str] 			=		['''past_key_values''']
		UpperCAmelCase					:	List[Any] 			=		{
		    '''hidden_size''': '''d_model''',
		    '''num_attention_heads''': '''encoder_attention_heads''',
		}




		def __init__(self      :	Optional[Any]							,			A__      :	Tuple=True							,			A__      :	Union[str, Any]=None							,			A__      :	Tuple=3							,			A__      :	int=1_0_0							,			A__      :	int=6							,			A__      :	Tuple=2_0_4_8							,			A__      :	Dict=8							,			A__      :	Any=6							,			A__      :	Union[str, Any]=2_0_4_8							,			A__      :	Union[str, Any]=8							,			A__      :	str=0.0							,			A__      :	str=0.0							,			A__      :	Optional[Any]=True							,			A__      :	Any="relu"							,			A__      :	List[str]=2_5_6							,			A__      :	Any=0.1							,			A__      :	Union[str, Any]=0.0							,			A__      :	Dict=0.0							,			A__      :	Any=0.0_2							,			A__      :	Tuple=1.0							,			A__      :	str=False							,			A__      :	Optional[int]="sine"							,			A__      :	str="resnet50"							,			A__      :	int=True							,			A__      :	List[Any]=False							,			A__      :	Dict=1							,			A__      :	Optional[int]=5							,			A__      :	str=2							,			A__      :	int=1							,			A__      :	Dict=1							,			A__      :	Any=5							,			A__      :	int=2							,			A__      :	Any=0.1							,			**A__      :	Dict							,			)					-> Any:
						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         = CONFIG_MAPPING["resnet"](out_features=["stage4"]				)
										elif isinstance(A__							,			A__				):
														lowercase         = backbone_config.get("model_type"				)
														lowercase         = CONFIG_MAPPING[backbone_model_type]
														lowercase         = config_class.from_dict(A__				)
										# set timm attributes to None
										lowercase   ,							lowercase   ,							lowercase         = None, None, None

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




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




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




		@classmethod
		def 				UpperCAmelCase__   (cls      :	Optional[int]							,			A__      :	PretrainedConfig							,			**A__      :	Any				)					-> List[str]:
						return cls(backbone_config=A__							,			**A__				)





		def 				UpperCAmelCase__   (self      :	int				)					-> Dict[str, any]:
						lowercase         = copy.deepcopy(self.__dict__				)
						if output["backbone_config"] is not None:
										lowercase         = self.backbone_config.to_dict()
						lowercase         = self.__class__.model_type
						return output




class       UpperCAmelCase (						_lowercase  ):
		UpperCAmelCase					:	str 			=		version.parse('''1.11'''  )




		@property
		def 				UpperCAmelCase__   (self      :	Optional[int]				)					-> Mapping[str, Mapping[int, str]]:
						return OrderedDict(
						    [
						        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
						        ("pixel_mask", {0: "batch"}),
						    ]				)




		@property
		def 				UpperCAmelCase__   (self      :	str				)					-> float:
						return 1e-5





		@property
		def 				UpperCAmelCase__   (self      :	str				)					-> int:
						return 1_2











'''simple docstring'''

import functools







def 							UpperCAmelCase_    (	lowerCAmelCase_		,							lowerCAmelCase_							):

				"""simple docstring"""






				if not isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for day in days							):
								raise ValueError("The parameter days should be a list of integers"							)

				if len(lowerCAmelCase_							) != 3 or not all(isinstance(lowerCAmelCase_		,							lowerCAmelCase_							) for cost in costs							):
								raise ValueError("The parameter costs should be a list of three integers"							)

				if len(lowerCAmelCase_							) == 0:
								return 0

				if min(lowerCAmelCase_							) <= 0:
								raise ValueError("All days elements should be greater than 0"							)

				if max(lowerCAmelCase_							) >= 366:
								raise ValueError("All days elements should be less than 366"							)

				lowercase         = set(lowerCAmelCase_							)

				@functools.cache
				def dynamic_programming(lowerCAmelCase_							) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1							)

								return min(
								    costs[0] + dynamic_programming(index + 1							)		,							costs[1] + dynamic_programming(index + 7							)		,							costs[2] + dynamic_programming(index + 30							)		,							)

				return dynamic_programming(1							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()




