
import copy
from typing import Dict, Optional

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


_snake_case		  =   logging.get_logger(__name__)

_snake_case		  =   {
    "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 			UpperCamelCase_				(  snake_case__     ):




      '''simple docstring'''







      a		:Tuple						 =      'oneformer'
      a		:Optional[Any]						 =      {'hidden_size': 'hidden_dim'}







      def __init__( self					,					_UpperCAmelCase = None					,					_UpperCAmelCase = 255					,					_UpperCAmelCase = 150					,					_UpperCAmelCase = 0.1					,					_UpperCAmelCase = 2.0					,					_UpperCAmelCase = 5.0					,					_UpperCAmelCase = 5.0					,					_UpperCAmelCase = 0.5					,					_UpperCAmelCase = 0.07					,					_UpperCAmelCase = 12_544					,					_UpperCAmelCase = 3.0					,					_UpperCAmelCase = 0.75					,					_UpperCAmelCase = 0.02					,					_UpperCAmelCase = 1.0					,					_UpperCAmelCase = 1E-05					,					_UpperCAmelCase = False					,					_UpperCAmelCase = True					,					_UpperCAmelCase = True					,					_UpperCAmelCase = [4, 8, 16, 32]					,					_UpperCAmelCase = 77					,					_UpperCAmelCase = 256					,					_UpperCAmelCase = 77					,					_UpperCAmelCase = 6					,					_UpperCAmelCase = 49_408					,					_UpperCAmelCase = 2					,					_UpperCAmelCase = 16					,					_UpperCAmelCase = 256					,					_UpperCAmelCase = 256					,					_UpperCAmelCase = 256					,					_UpperCAmelCase = 1_024					,					_UpperCAmelCase = "GN"					,					_UpperCAmelCase = 6					,					_UpperCAmelCase = 10					,					_UpperCAmelCase = True					,					_UpperCAmelCase = 8					,					_UpperCAmelCase = 0.1					,					_UpperCAmelCase = 2_048					,					_UpperCAmelCase = False					,					_UpperCAmelCase = False					,					_UpperCAmelCase = 2					,					_UpperCAmelCase = 4					,					**_UpperCAmelCase					,					):







            if backbone_config is None:
                  logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.''')
                  lowerCAmelCase_						=      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):
                  lowerCAmelCase_						=      backbone_config.get('''model_type''')
                  lowerCAmelCase_						=      CONFIG_MAPPING[backbone_model_type]
                  lowerCAmelCase_						=      config_class.from_dict(__SCREAMING_SNAKE_CASE)

            lowerCAmelCase_						=      backbone_config

            lowerCAmelCase_						=      ignore_value
            lowerCAmelCase_						=      num_queries
            lowerCAmelCase_						=      no_object_weight
            lowerCAmelCase_						=      class_weight
            lowerCAmelCase_						=      mask_weight
            lowerCAmelCase_						=      dice_weight
            lowerCAmelCase_						=      contrastive_weight
            lowerCAmelCase_						=      contrastive_temperature
            lowerCAmelCase_						=      train_num_points
            lowerCAmelCase_						=      oversample_ratio
            lowerCAmelCase_						=      importance_sample_ratio
            lowerCAmelCase_						=      init_std
            lowerCAmelCase_						=      init_xavier_std
            lowerCAmelCase_						=      layer_norm_eps
            lowerCAmelCase_						=      is_training
            lowerCAmelCase_						=      use_auxiliary_loss
            lowerCAmelCase_						=      output_auxiliary_logits
            lowerCAmelCase_						=      strides
            lowerCAmelCase_						=      task_seq_len
            lowerCAmelCase_						=      text_encoder_width
            lowerCAmelCase_						=      text_encoder_context_length
            lowerCAmelCase_						=      text_encoder_num_layers
            lowerCAmelCase_						=      text_encoder_vocab_size
            lowerCAmelCase_						=      text_encoder_proj_layers
            lowerCAmelCase_						=      text_encoder_n_ctx
            lowerCAmelCase_						=      conv_dim
            lowerCAmelCase_						=      mask_dim
            lowerCAmelCase_						=      hidden_dim
            lowerCAmelCase_						=      encoder_feedforward_dim
            lowerCAmelCase_						=      norm
            lowerCAmelCase_						=      encoder_layers
            lowerCAmelCase_						=      decoder_layers
            lowerCAmelCase_						=      use_task_norm
            lowerCAmelCase_						=      num_attention_heads
            lowerCAmelCase_						=      dropout
            lowerCAmelCase_						=      dim_feedforward
            lowerCAmelCase_						=      pre_norm
            lowerCAmelCase_						=      enforce_input_proj
            lowerCAmelCase_						=      query_dec_layers
            lowerCAmelCase_						=      common_stride
            lowerCAmelCase_						=      decoder_layers

            super().__init__(**__SCREAMING_SNAKE_CASE)







      def lowercase__		( self):
            lowerCAmelCase_						=      copy.deepcopy(self.__dict__)
            lowerCAmelCase_						=      self.backbone_config.to_dict()
            lowerCAmelCase_						=      self.__class__.model_type
            return output







def 	_lowerCamelCase  (		A_  :	float							,  A_  :	float							,  A_  :	int      )			->			float:

   '''simple docstring'''






   UpperCamelCase__ :			Union[str, Any] 						=[1 / first_term]
   UpperCamelCase__ :			Tuple 						=1 / first_term
   for _ in range(number_of_terms - 1      ):
      first_term += common_difference
      arithmetic_progression.append(A_      )
   UpperCamelCase__ :			List[str] 						=[1 / step for step in arithmetic_progression]
   return sum(A_      )


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     print(sum_of_harmonic_progression(1 / 2, 2, 2))

