



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 math import sqrt




def 	UpperCAmelCase   (						_lowerCamelCase = 100_0000    ):
			A							:					int              =						0
			A							:					int              =						0
			A							:					int

			while num_cuboids <= limit:
						max_cuboid_size += 1
						for sum_shortest_sides in range(2 ,    2 * max_cuboid_size + 1    ):
									if sqrt(sum_shortest_sides**2 + max_cuboid_size**2    ).is_integer():
												num_cuboids += (
												    min(_lowerCamelCase ,    sum_shortest_sides // 2    )
												    - max(1 ,    sum_shortest_sides - max_cuboid_size    )
												    + 1
												)

			return max_cuboid_size


if __name__ == "__main__":
							print(F"""{solution() = }""")