

"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


lowercase__				:Tuple				   =       logging.get_logger(__name__)

lowercase__				:Optional[Any]				   =       {
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[str]         =       'conditional_detr'
      _A     :       Dict         =       ['past_key_values']
      _A     :       Union[str, Any]         =       {
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }

      def __init__(							self       :      Dict						,				__lowercase       :      Union[str, Any]=True						,				__lowercase       :      List[str]=None						,				__lowercase       :      Tuple=3						,				__lowercase       :      Dict=300						,				__lowercase       :      Tuple=6						,				__lowercase       :      Optional[int]=2_048						,				__lowercase       :      Tuple=8						,				__lowercase       :      Optional[Any]=6						,				__lowercase       :      List[str]=2_048						,				__lowercase       :      Tuple=8						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      Union[str, Any]=0.0						,				__lowercase       :      Dict=True						,				__lowercase       :      Union[str, Any]="relu"						,				__lowercase       :      Union[str, Any]=256						,				__lowercase       :      str=0.1						,				__lowercase       :      Dict=0.0						,				__lowercase       :      List[str]=0.0						,				__lowercase       :      str=0.0_2						,				__lowercase       :      List[str]=1.0						,				__lowercase       :      Union[str, Any]=False						,				__lowercase       :      Tuple="sine"						,				__lowercase       :      List[Any]="resnet50"						,				__lowercase       :      int=True						,				__lowercase       :      str=False						,				__lowercase       :      Any=2						,				__lowercase       :      Optional[Any]=5						,				__lowercase       :      Dict=2						,				__lowercase       :      int=1						,				__lowercase       :      Union[str, Any]=1						,				__lowercase       :      Any=2						,				__lowercase       :      List[Any]=5						,				__lowercase       :      int=2						,				__lowercase       :      int=0.2_5						,				**__lowercase       :      List[Any]						,				):


        '''simple docstring'''




        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.''' )
            __UpperCAmelCase    :						Union[str, Any]       =						CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
          elif isinstance(__lowercase						,				__lowercase ):
            __UpperCAmelCase    :						str       =						backbone_config.get('''model_type''' )
            __UpperCAmelCase    :						Optional[Any]       =						CONFIG_MAPPING[backbone_model_type]
            __UpperCAmelCase    :						Union[str, Any]       =						config_class.from_dict(__lowercase )

        __UpperCAmelCase    :						List[str]       =						use_timm_backbone
        __UpperCAmelCase    :						Any       =						backbone_config
        __UpperCAmelCase    :						Union[str, Any]       =						num_channels
        __UpperCAmelCase    :						List[Any]       =						num_queries
        __UpperCAmelCase    :						Optional[Any]       =						d_model
        __UpperCAmelCase    :						str       =						encoder_ffn_dim
        __UpperCAmelCase    :						Optional[Any]       =						encoder_layers
        __UpperCAmelCase    :						Optional[Any]       =						encoder_attention_heads
        __UpperCAmelCase    :						Any       =						decoder_ffn_dim
        __UpperCAmelCase    :						List[str]       =						decoder_layers
        __UpperCAmelCase    :						Union[str, Any]       =						decoder_attention_heads
        __UpperCAmelCase    :						Optional[Any]       =						dropout
        __UpperCAmelCase    :						Union[str, Any]       =						attention_dropout
        __UpperCAmelCase    :						Union[str, Any]       =						activation_dropout
        __UpperCAmelCase    :						Tuple       =						activation_function
        __UpperCAmelCase    :						List[Any]       =						init_std
        __UpperCAmelCase    :						Union[str, Any]       =						init_xavier_std
        __UpperCAmelCase    :						Tuple       =						encoder_layerdrop
        __UpperCAmelCase    :						List[str]       =						decoder_layerdrop
        __UpperCAmelCase    :						Dict       =						encoder_layers
        __UpperCAmelCase    :						Optional[int]       =						auxiliary_loss
        __UpperCAmelCase    :						Optional[int]       =						position_embedding_type
        __UpperCAmelCase    :						Tuple       =						backbone
        __UpperCAmelCase    :						List[Any]       =						use_pretrained_backbone
        __UpperCAmelCase    :						Optional[int]       =						dilation
        # Hungarian matcher
        __UpperCAmelCase    :						List[Any]       =						class_cost
        __UpperCAmelCase    :						int       =						bbox_cost
        __UpperCAmelCase    :						Optional[int]       =						giou_cost
        # Loss coefficients
        __UpperCAmelCase    :						Dict       =						mask_loss_coefficient
        __UpperCAmelCase    :						Tuple       =						dice_loss_coefficient
        __UpperCAmelCase    :						Tuple       =						cls_loss_coefficient
        __UpperCAmelCase    :						str       =						bbox_loss_coefficient
        __UpperCAmelCase    :						Tuple       =						giou_loss_coefficient
        __UpperCAmelCase    :						List[str]       =						focal_alpha
        super().__init__(is_encoder_decoder=__lowercase						,				**__lowercase )

      @property
      def       A_					(							self       :      Dict ):


        '''simple docstring'''




        return self.encoder_attention_heads

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        return self.d_model







      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						copy.deepcopy(self.__dict__ )
        if self.backbone_config is not None:
          __UpperCAmelCase    :						List[Any]       =						self.backbone_config.to_dict()
        __UpperCAmelCase    :						str       =						self.__class__.model_type
        return output




class 							snake_case							(			__UpperCAmelCase ):

      '''simple docstring'''



      _A     :       List[str]         =       version.parse('1.11' )

      @property
      def       A_					(							self       :      Any ):


        '''simple docstring'''




        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ('''pixel_mask''', {0: '''batch'''}),
            ] )

      @property
      def       A_					(							self       :      str ):


        '''simple docstring'''




        return 1e-5







      @property
      def       A_					(							self       :      Tuple ):


        '''simple docstring'''




        return 12






"""simple docstring"""





def       lowerCamelCase_       (   )						->str:





  """simple docstring"""


  for n in range(1  ,			1_00_00_00  ):
    yield n * (n + 1) // 2


def       lowerCamelCase_       (   UpperCAmelCase_  )						->Union[str, Any]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						1
  __UpperCAmelCase    :						Any       =						2
  while i * i <= n:
    __UpperCAmelCase    :						Tuple       =						0
    while n % i == 0:
      n //= i
      multiplicity += 1
    divisors_count *= multiplicity + 1
    i += 1
  if n > 1:
    divisors_count *= 2
  return divisors_count


def       lowerCamelCase_       (   )						->Optional[int]:





  """simple docstring"""


  return next(i for i in triangle_number_generator() if count_divisors(UpperCAmelCase_  ) > 5_00  )


if __name__ == "__main__":
  print(solution())