



import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


__SCREAMING_SNAKE_CASE           =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE           =  {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}

class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						a__   =  "dpt"







						def __init__(	self      :			Optional[int]	,	__lowerCamelCase      :			List[Any]=7_68	,	__lowerCamelCase      :			Tuple=12	,	__lowerCamelCase      :			List[str]=12	,	__lowerCamelCase      :			Optional[Any]=30_72	,	__lowerCamelCase      :			List[str]="gelu"	,	__lowerCamelCase      :			Tuple=0.0	,	__lowerCamelCase      :			Optional[int]=0.0	,	__lowerCamelCase      :			Optional[int]=0.02	,	__lowerCamelCase      :			Optional[int]=1e-12	,	__lowerCamelCase      :			List[Any]=3_84	,	__lowerCamelCase      :			Optional[int]=16	,	__lowerCamelCase      :			Optional[int]=3	,	__lowerCamelCase      :			Tuple=False	,	__lowerCamelCase      :			int=True	,	__lowerCamelCase      :			Tuple=[2, 5, 8, 11]	,	__lowerCamelCase      :			Tuple="project"	,	__lowerCamelCase      :			List[str]=[4, 2, 1, 0.5]	,	__lowerCamelCase      :			Tuple=[96, 1_92, 3_84, 7_68]	,	__lowerCamelCase      :			Optional[Any]=2_56	,	__lowerCamelCase      :			str=-1	,	__lowerCamelCase      :			Dict=False	,	__lowerCamelCase      :			List[str]=True	,	__lowerCamelCase      :			Union[str, Any]=0.4	,	__lowerCamelCase      :			Any=2_55	,	__lowerCamelCase      :			Union[str, Any]=0.1	,	__lowerCamelCase      :			Optional[Any]=[1, 10_24, 24, 24]	,	__lowerCamelCase      :			Optional[int]=[0, 1]	,	__lowerCamelCase      :			List[str]=None	,	**__lowerCamelCase      :			Dict	,	)      ->				Optional[int]:
									super().__init__(**__lowerCamelCase )

									A							:					str              =						hidden_size
									A							:					int              =						is_hybrid

									if self.is_hybrid:
												if backbone_config is None:
															logger.info("Initializing the config with a `BiT` backbone." )
															A							:					str              =						{
															    "global_padding": "same",
															    "layer_type": "bottleneck",
															    "depths": [3, 4, 9],
															    "out_features": ["stage1", "stage2", "stage3"],
															    "embedding_dynamic_padding": True,
															}
															A							:					Optional[int]              =						BitConfig(**__lowerCamelCase )
												elif isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															logger.info("Initializing the config with a `BiT` backbone." )
															A							:					Union[str, Any]              =						BitConfig(**__lowerCamelCase )
												elif isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															A							:					Union[str, Any]              =						backbone_config
												else:
															raise ValueError(
															    F"""backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.""" )

												A							:					int              =						backbone_featmap_shape
												A							:					Tuple              =						neck_ignore_stages

												if readout_type != "project":
															raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode." )
									else:
												A							:					int              =						None
												A							:					List[str]              =						None
												A							:					Any              =						[]

									A							:					int              =						num_hidden_layers
									A							:					str              =						num_attention_heads
									A							:					Union[str, Any]              =						intermediate_size
									A							:					List[Any]              =						hidden_act
									A							:					str              =						hidden_dropout_prob
									A							:					Dict              =						attention_probs_dropout_prob
									A							:					str              =						initializer_range
									A							:					List[Any]              =						layer_norm_eps
									A							:					Union[str, Any]              =						image_size
									A							:					Tuple              =						patch_size
									A							:					str              =						num_channels
									A							:					Dict              =						qkv_bias
									A							:					Union[str, Any]              =						backbone_out_indices
									if readout_type not in ["ignore", "add", "project"]:
												raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']" )
									A							:					List[Any]              =						readout_type
									A							:					List[Any]              =						reassemble_factors
									A							:					Union[str, Any]              =						neck_hidden_sizes
									A							:					Tuple              =						fusion_hidden_size
									A							:					Union[str, Any]              =						head_in_index
									A							:					Dict              =						use_batch_norm_in_fusion_residual
									# auxiliary head attributes (semantic segmentation)
									A							:					List[Any]              =						use_auxiliary_head
									A							:					Optional[Any]              =						auxiliary_loss_weight
									A							:					Optional[Any]              =						semantic_loss_ignore_index
									A							:					Any              =						semantic_classifier_dropout







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									A							:					Optional[int]              =						copy.deepcopy(self.__dict__ )

									if output["backbone_config"] is not None:
												A							:					Tuple              =						self.backbone_config.to_dict()

									A							:					Tuple              =						self.__class__.model_type
									return output




from collections.abc import Sequence




def 	UpperCAmelCase   (						_lowerCamelCase = None    ):
			if nums is None or not nums:
						raise ValueError("Input sequence should not be empty"    )

			A							:					Dict              =						nums[0]
			for i in range(1 ,    len(_lowerCamelCase    )    ):
						A							:					Tuple              =						nums[i]
						A							:					List[Any]              =						max(_lowerCamelCase ,    ans + num ,    _lowerCamelCase    )

			return ans


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							# Try on a sample input from the user
							__SCREAMING_SNAKE_CASE           =  int(input("""Enter number of elements : """).strip())
							__SCREAMING_SNAKE_CASE           =  list(map(int, input("""\nEnter the numbers : """).strip().split()))[:n]
							print(max_subsequence_sum(array))