

'''simple docstring'''





import copy

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


SCREAMING_SNAKE_CASE_:					Optional[Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Union[str, Any]  ={
    '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 	__A				(			UpperCamelCase__     ):
    a__	:						Union[str, Any]	       =  """dpt"""



    def __init__(self   :   Dict       ,      __a   :   Any=768       ,      __a   :   List[Any]=12       ,      __a   :   Any=12       ,      __a   :   Tuple=3072       ,      __a   :   List[str]="gelu"       ,      __a   :   Any=0.0       ,      __a   :   Any=0.0       ,      __a   :   int=0.02       ,      __a   :   Optional[Any]=1E-12       ,      __a   :   Union[str, Any]=384       ,      __a   :   Optional[Any]=16       ,      __a   :   List[str]=3       ,      __a   :   Optional[int]=False       ,      __a   :   Tuple=True       ,      __a   :   int=[2, 5, 8, 11]       ,      __a   :   str="project"       ,      __a   :   List[str]=[4, 2, 1, 0.5]       ,      __a   :   List[Any]=[96, 192, 384, 768]       ,      __a   :   List[str]=256       ,      __a   :   Union[str, Any]=-1       ,      __a   :   Dict=False       ,      __a   :   str=True       ,      __a   :   Tuple=0.4       ,      __a   :   List[Any]=255       ,      __a   :   List[str]=0.1       ,      __a   :   int=[1, 1024, 24, 24]       ,      __a   :   Any=[0, 1]       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)

         UpperCAmelCase_	   =       hidden_size
         UpperCAmelCase_	   =       is_hybrid

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

              UpperCAmelCase_	   =       backbone_featmap_shape
              UpperCAmelCase_	   =       neck_ignore_stages

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

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






    def  _lowercase						(self   :   Dict			):
         UpperCAmelCase_	   =       copy.deepcopy(self.__dict__			)

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

         UpperCAmelCase_	   =       self.__class__.model_type
         return output





'''simple docstring'''





def      lowerCAmelCase_    ( snake_case_	:			int = 1_00_00_00      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_	   =       limit + 1
     UpperCAmelCase_	   =       [0] * limit
     for first_term in range(1 ,   snake_case_      ):
          for n in range(snake_case_ ,   snake_case_ ,   snake_case_      ):
               UpperCAmelCase_	   =       first_term + n / first_term
               if common_difference % 4:  # d must be divisble by 4
                    continue
               else:
                    common_difference /= 4
                    if (
                        first_term > common_difference
                        and first_term < 4 * common_difference
                    ):  # since x,y,z are positive integers
                         frequency[n] += 1  # so z>0 and a>d ,also 4d<a

     UpperCAmelCase_	   =       sum(1 for x in frequency[1:limit] if x == 10      )

     return count


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




