

import copy
from typing import Dict, Optional

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


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

lowerCAmelCase       :				Optional[int]         =					{
    'shi-labs/oneformer_ade20k_swin_tiny': (
        'https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json'
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}
class 	_A						(					__magic_name__):
  SCREAMING_SNAKE_CASE      : List[Any]    =					'''oneformer'''
  SCREAMING_SNAKE_CASE      : Union[str, Any]    =					{'''hidden_size''': '''hidden_dim'''}

  def __init__(			self	,   _SCREAMING_SNAKE_CASE = None	,   _SCREAMING_SNAKE_CASE = 255	,   _SCREAMING_SNAKE_CASE = 150	,   _SCREAMING_SNAKE_CASE = 0.1	,   _SCREAMING_SNAKE_CASE = 2.0	,   _SCREAMING_SNAKE_CASE = 5.0	,   _SCREAMING_SNAKE_CASE = 5.0	,   _SCREAMING_SNAKE_CASE = 0.5	,   _SCREAMING_SNAKE_CASE = 0.07	,   _SCREAMING_SNAKE_CASE = 1_2544	,   _SCREAMING_SNAKE_CASE = 3.0	,   _SCREAMING_SNAKE_CASE = 0.75	,   _SCREAMING_SNAKE_CASE = 0.02	,   _SCREAMING_SNAKE_CASE = 1.0	,   _SCREAMING_SNAKE_CASE = 1e-05	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = [4, 8, 16, 32]	,   _SCREAMING_SNAKE_CASE = 77	,   _SCREAMING_SNAKE_CASE = 256	,   _SCREAMING_SNAKE_CASE = 77	,   _SCREAMING_SNAKE_CASE = 6	,   _SCREAMING_SNAKE_CASE = 4_9408	,   _SCREAMING_SNAKE_CASE = 2	,   _SCREAMING_SNAKE_CASE = 16	,   _SCREAMING_SNAKE_CASE = 256	,   _SCREAMING_SNAKE_CASE = 256	,   _SCREAMING_SNAKE_CASE = 256	,   _SCREAMING_SNAKE_CASE = 1024	,   _SCREAMING_SNAKE_CASE = "GN"	,   _SCREAMING_SNAKE_CASE = 6	,   _SCREAMING_SNAKE_CASE = 10	,   _SCREAMING_SNAKE_CASE = True	,   _SCREAMING_SNAKE_CASE = 8	,   _SCREAMING_SNAKE_CASE = 0.1	,   _SCREAMING_SNAKE_CASE = 2048	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = False	,   _SCREAMING_SNAKE_CASE = 2	,   _SCREAMING_SNAKE_CASE = 4	,   **_SCREAMING_SNAKE_CASE	,   ):





        """simple docstring"""







        if backbone_config is None:
              logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'   )
              SCREAMING_SNAKE_CASE_			:					str          =       CONFIG_MAPPING['swin'](
                  image_size=224	,   in_channels=3	,   patch_size=4	,   embed_dim=96	,   depths=[2, 2, 6, 2]	,   num_heads=[3, 6, 12, 24]	,   window_size=7	,   drop_path_rate=0.3	,   use_absolute_embeddings=_SCREAMING_SNAKE_CASE	,   out_features=['stage1', 'stage2', 'stage3', 'stage4']	,   )
        elif isinstance(_SCREAMING_SNAKE_CASE	,   _SCREAMING_SNAKE_CASE   ):
              SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       backbone_config.get('model_type'   )
              SCREAMING_SNAKE_CASE_			:					Tuple          =       CONFIG_MAPPING[backbone_model_type]
              SCREAMING_SNAKE_CASE_			:					int          =       config_class.from_dict(_SCREAMING_SNAKE_CASE   )

        SCREAMING_SNAKE_CASE_			:					str          =       backbone_config

        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       ignore_value
        SCREAMING_SNAKE_CASE_			:					Dict          =       num_queries
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       no_object_weight
        SCREAMING_SNAKE_CASE_			:					List[str]          =       class_weight
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       mask_weight
        SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       dice_weight
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       contrastive_weight
        SCREAMING_SNAKE_CASE_			:					Dict          =       contrastive_temperature
        SCREAMING_SNAKE_CASE_			:					List[str]          =       train_num_points
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       oversample_ratio
        SCREAMING_SNAKE_CASE_			:					Any          =       importance_sample_ratio
        SCREAMING_SNAKE_CASE_			:					List[str]          =       init_std
        SCREAMING_SNAKE_CASE_			:					List[str]          =       init_xavier_std
        SCREAMING_SNAKE_CASE_			:					List[str]          =       layer_norm_eps
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       is_training
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       use_auxiliary_loss
        SCREAMING_SNAKE_CASE_			:					int          =       output_auxiliary_logits
        SCREAMING_SNAKE_CASE_			:					str          =       strides
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       task_seq_len
        SCREAMING_SNAKE_CASE_			:					List[Any]          =       text_encoder_width
        SCREAMING_SNAKE_CASE_			:					Tuple          =       text_encoder_context_length
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       text_encoder_num_layers
        SCREAMING_SNAKE_CASE_			:					Dict          =       text_encoder_vocab_size
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       text_encoder_proj_layers
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       text_encoder_n_ctx
        SCREAMING_SNAKE_CASE_			:					Tuple          =       conv_dim
        SCREAMING_SNAKE_CASE_			:					str          =       mask_dim
        SCREAMING_SNAKE_CASE_			:					Dict          =       hidden_dim
        SCREAMING_SNAKE_CASE_			:					Any          =       encoder_feedforward_dim
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       norm
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       encoder_layers
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       decoder_layers
        SCREAMING_SNAKE_CASE_			:					Tuple          =       use_task_norm
        SCREAMING_SNAKE_CASE_			:					Any          =       num_attention_heads
        SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       dropout
        SCREAMING_SNAKE_CASE_			:					Any          =       dim_feedforward
        SCREAMING_SNAKE_CASE_			:					Dict          =       pre_norm
        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       enforce_input_proj
        SCREAMING_SNAKE_CASE_			:					str          =       query_dec_layers
        SCREAMING_SNAKE_CASE_			:					str          =       common_stride
        SCREAMING_SNAKE_CASE_			:					int          =       decoder_layers

        super().__init__(**_SCREAMING_SNAKE_CASE   )



  def 	UpperCAmelCase      (			self   ):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_			:					Optional[int]          =       copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_			:					int          =       self.backbone_config.to_dict()
        SCREAMING_SNAKE_CASE_			:					str          =       self.__class__.model_type
        return output





from math import factorial

def 				A_					(							a   ,				a					):

      """simple docstring"""



      return factorial(a					) / (factorial(a					) * factorial(n - r					))

def 				A_					(							):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       0

      for i in range(1   ,				1_0_1					):
            for j in range(1   ,				i + 1					):
                  if combinations(a   ,				a					) > 1e6:
                        total += 1
      return total


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






