'''simple docstring'''


import copy
from typing import Dict, Optional

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


snake_case__	:     int      =    logging.get_logger(__name__)

snake_case__	:     Union[str, Any]      =    {
    '''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 	__SCREAMING_SNAKE_CASE		(			lowerCamelCase_			):







  '''simple docstring'''






  lowerCamelCase_						:List[Any]		       =						'''oneformer'''
  lowerCamelCase_						:Optional[Any]		       =						{'''hidden_size''': '''hidden_dim'''}


  def __init__(					self ,						snake_case_ = None ,						snake_case_ = 2_5_5 ,						snake_case_ = 1_5_0 ,						snake_case_ = 0.1 ,						snake_case_ = 2.0 ,						snake_case_ = 5.0 ,						snake_case_ = 5.0 ,						snake_case_ = 0.5 ,						snake_case_ = 0.07 ,						snake_case_ = 1_2_5_4_4 ,						snake_case_ = 3.0 ,						snake_case_ = 0.75 ,						snake_case_ = 0.02 ,						snake_case_ = 1.0 ,						snake_case_ = 1E-05 ,						snake_case_ = False ,						snake_case_ = True ,						snake_case_ = True ,						snake_case_ = [4, 8, 1_6, 3_2] ,						snake_case_ = 7_7 ,						snake_case_ = 2_5_6 ,						snake_case_ = 7_7 ,						snake_case_ = 6 ,						snake_case_ = 4_9_4_0_8 ,						snake_case_ = 2 ,						snake_case_ = 1_6 ,						snake_case_ = 2_5_6 ,						snake_case_ = 2_5_6 ,						snake_case_ = 2_5_6 ,						snake_case_ = 1_0_2_4 ,						snake_case_ = "GN" ,						snake_case_ = 6 ,						snake_case_ = 1_0 ,						snake_case_ = True ,						snake_case_ = 8 ,						snake_case_ = 0.1 ,						snake_case_ = 2_0_4_8 ,						snake_case_ = False ,						snake_case_ = False ,						snake_case_ = 2 ,						snake_case_ = 4 ,						**snake_case_ ,						):




       '''simple docstring'''

       if backbone_config is None:
            logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'		)
            UpperCAmelCase_				:  Any  = CONFIG_MAPPING['swin'](
                image_size=2_2_4 ,						in_channels=3 ,						patch_size=4 ,						embed_dim=9_6 ,						depths=[2, 2, 6, 2] ,						num_heads=[3, 6, 1_2, 2_4] ,						window_size=7 ,						drop_path_rate=0.3 ,						use_absolute_embeddings=snake_case_ ,						out_features=['stage1', 'stage2', 'stage3', 'stage4'] ,						)
       elif isinstance(snake_case_ ,						snake_case_		):
            UpperCAmelCase_				:  int  = backbone_config.get('model_type'		)
            UpperCAmelCase_				:  Tuple  = CONFIG_MAPPING[backbone_model_type]
            UpperCAmelCase_				:  Any  = config_class.from_dict(snake_case_		)

       UpperCAmelCase_				:  List[str]  = backbone_config

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

       super().__init__(**snake_case_		)






  def _UpperCamelCase	(					self		):




       '''simple docstring'''

       UpperCAmelCase_				:  Any  = copy.deepcopy(self.__dict__		)
       UpperCAmelCase_				:  Optional[Any]  = self.backbone_config.to_dict()
       UpperCAmelCase_				:  Tuple  = self.__class__.model_type
       return output







'''simple docstring'''


from itertools import combinations, product


def    _lowerCamelCase       ( lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int      ,	lowerCamelCase_   :				int  ):



     """simple docstring"""





     if xa == ya == 0 or xa == ya == 0:
          return False
     UpperCAmelCase_				:  Union[str, Any]  = xa * xa + ya * ya
     UpperCAmelCase_				:  str  = xa * xa + ya * ya
     UpperCAmelCase_				:  str  = (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
     return (
         a_square + b_square == c_square
         or a_square + c_square == b_square
         or b_square + c_square == a_square
     )


def    _lowerCamelCase       ( lowerCamelCase_   :				int = 50  ):



     """simple docstring"""





     return sum(
         1
         for pta, pta in combinations(product(range(limit + 1  )      ,	repeat=2  )      ,	2  )
         if is_right(*lowerCamelCase_      ,	*lowerCamelCase_  )  )


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






