






"""simple docstring"""





import copy

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


A__					: int=	logging.get_logger(__name__)

A__					: List[Any]=	{
    """ut/deta""": """https://huggingface.co/ut/deta/resolve/main/config.json""",
}
class    __lowerCamelCase    (  _a  ):
   a      :				Union[str, Any] 						="""deta"""
   a      :				Union[str, Any] 						={
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }

   def __init__(					self ,				snake_case_=None ,				snake_case_=900 ,				snake_case_=2048 ,				snake_case_=6 ,				snake_case_=2048 ,				snake_case_=8 ,				snake_case_=6 ,				snake_case_=1024 ,				snake_case_=8 ,				snake_case_=0.0 ,				snake_case_=True ,				snake_case_="relu" ,				snake_case_=256 ,				snake_case_=0.1 ,				snake_case_=0.0 ,				snake_case_=0.0 ,				snake_case_=0.02 ,				snake_case_=1.0 ,				snake_case_=True ,				snake_case_=False ,				snake_case_="sine" ,				snake_case_=5 ,				snake_case_=4 ,				snake_case_=4 ,				snake_case_=True ,				snake_case_=300 ,				snake_case_=True ,				snake_case_=True ,				snake_case_=1 ,				snake_case_=5 ,				snake_case_=2 ,				snake_case_=1 ,				snake_case_=1 ,				snake_case_=5 ,				snake_case_=2 ,				snake_case_=0.1 ,				snake_case_=0.25 ,				**snake_case_ ,				)		->       List[str]:
       if backbone_config is None:
           logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'      )
           UpperCamelCase__         =    CONFIG_MAPPING['resnet'](out_features=['stage2', 'stage3', 'stage4']      )
       else:
           if isinstance(snake_case_ ,				snake_case_      ):
               UpperCamelCase__         =    backbone_config.pop('model_type'      )
               UpperCamelCase__         =    CONFIG_MAPPING[backbone_model_type]
               UpperCamelCase__         =    config_class.from_dict(snake_case_      )

       UpperCamelCase__         =    backbone_config
       UpperCamelCase__         =    num_queries
       UpperCamelCase__         =    max_position_embeddings
       UpperCamelCase__         =    d_model
       UpperCamelCase__         =    encoder_ffn_dim
       UpperCamelCase__         =    encoder_layers
       UpperCamelCase__         =    encoder_attention_heads
       UpperCamelCase__         =    decoder_ffn_dim
       UpperCamelCase__         =    decoder_layers
       UpperCamelCase__         =    decoder_attention_heads
       UpperCamelCase__         =    dropout
       UpperCamelCase__         =    attention_dropout
       UpperCamelCase__         =    activation_dropout
       UpperCamelCase__         =    activation_function
       UpperCamelCase__         =    init_std
       UpperCamelCase__         =    init_xavier_std
       UpperCamelCase__         =    encoder_layerdrop
       UpperCamelCase__         =    auxiliary_loss
       UpperCamelCase__         =    position_embedding_type
       # deformable attributes
       UpperCamelCase__         =    num_feature_levels
       UpperCamelCase__         =    encoder_n_points
       UpperCamelCase__         =    decoder_n_points
       UpperCamelCase__         =    two_stage
       UpperCamelCase__         =    two_stage_num_proposals
       UpperCamelCase__         =    with_box_refine
       UpperCamelCase__         =    assign_first_stage
       if two_stage is True and with_box_refine is False:
           raise ValueError('If two_stage is True, with_box_refine must be True.'      )
       # Hungarian matcher
       UpperCamelCase__         =    class_cost
       UpperCamelCase__         =    bbox_cost
       UpperCamelCase__         =    giou_cost
       # Loss coefficients
       UpperCamelCase__         =    mask_loss_coefficient
       UpperCamelCase__         =    dice_loss_coefficient
       UpperCamelCase__         =    bbox_loss_coefficient
       UpperCamelCase__         =    giou_loss_coefficient
       UpperCamelCase__         =    eos_coefficient
       UpperCamelCase__         =    focal_alpha
       super().__init__(is_encoder_decoder=snake_case_ ,				**snake_case_      )

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

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







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       Any:
       UpperCamelCase__         =    copy.deepcopy(self.__dict__      )
       UpperCamelCase__         =    self.backbone_config.to_dict()
       UpperCamelCase__         =    self.__class__.model_type
       return output












"""simple docstring"""





from collections import defaultdict
from math import ceil, sqrt







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE = 1_00_00_00 ,	SCREAMING_SNAKE_CASE = 10					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    defaultdict(SCREAMING_SNAKE_CASE					)

    for outer_width in range(3 ,	(t_limit // 4) + 2					):
        if outer_width * outer_width > t_limit:
            UpperCamelCase__         =    max(
                ceil(sqrt(outer_width * outer_width - t_limit					)					) ,	1					)
        else:
            UpperCamelCase__         =    1

        hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

        for hole_width in range(SCREAMING_SNAKE_CASE ,	outer_width - 1 ,	2					):
            count[outer_width * outer_width - hole_width * hole_width] += 1

    return sum(1 for n in count.values() if 1 <= n <= 10					)


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




