import copy
from typing import Dict, Optional

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


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    """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(   a_	):
   UpperCamelCase_				:						str 			=   'oneformer'
   UpperCamelCase_				:						Tuple 			=   {'hidden_size': 'hidden_dim'}
   def __init__(    self							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = 2_55							,     lowerCAmelCase_ = 1_50							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 2.0							,     lowerCAmelCase_ = 5.0							,     lowerCAmelCase_ = 5.0							,     lowerCAmelCase_ = 0.5							,     lowerCAmelCase_ = 0.07							,     lowerCAmelCase_ = 1_25_44							,     lowerCAmelCase_ = 3.0							,     lowerCAmelCase_ = 0.75							,     lowerCAmelCase_ = 0.02							,     lowerCAmelCase_ = 1.0							,     lowerCAmelCase_ = 1e-05							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = [4, 8, 16, 32]							,     lowerCAmelCase_ = 77							,     lowerCAmelCase_ = 2_56							,     lowerCAmelCase_ = 77							,     lowerCAmelCase_ = 6							,     lowerCAmelCase_ = 4_94_08							,     lowerCAmelCase_ = 2							,     lowerCAmelCase_ = 16							,     lowerCAmelCase_ = 2_56							,     lowerCAmelCase_ = 2_56							,     lowerCAmelCase_ = 2_56							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "GN"							,     lowerCAmelCase_ = 6							,     lowerCAmelCase_ = 10							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 20_48							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 2							,     lowerCAmelCase_ = 4							,     **lowerCAmelCase_							,     )	->List[Any]:
        if backbone_config is None:
             logger.info('''`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'''    )
             a  :    str				=						CONFIG_MAPPING['swin'](
                 image_size=2_24							,     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=lowerCAmelCase_							,     out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							,     )
        elif isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ):
             a  :    Any				=						backbone_config.get('''model_type'''    )
             a  :    Union[str, Any]				=						CONFIG_MAPPING[backbone_model_type]
             a  :    Optional[Any]				=						config_class.from_dict(lowerCAmelCase_    )

        a  :    Union[str, Any]				=						backbone_config

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

        super().__init__(**lowerCAmelCase_    )


   def _lowercase(    self    )	->Dict[str, any]:
        a  :    List[str]				=						copy.deepcopy(self.__dict__    )
        a  :    Dict				=						self.backbone_config.to_dict()
        a  :    Any				=						self.__class__.model_type
        return output
from __future__ import annotations







def 	UpperCAmelCase       (		lowerCAmelCase_						,      lowerCAmelCase_						,      lowerCAmelCase_						,      )	->	list[float | int]:
     if not all((nth_term, start_term_a, common_ratio_r)       ):
          return []
     a  :    list[float | int]				=						[]
     a  :    str				=						1
     a  :    Dict				=						common_ratio_r
     for _ in range(int(lowerCAmelCase_       )       ):
          if not series:
               series.append(lowerCAmelCase_       )
          else:
               power += 1
               series.append(float(start_term_a * multiple       )       )
               a  :    Dict				=						pow(float(lowerCAmelCase_       )						,      lowerCAmelCase_       )
     return series


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  UpperCAmelCase							   =      float(input("""Enter the last number (n term) of the Geometric Series"""))
  UpperCAmelCase							   =      float(input("""Enter the starting term (a) of the Geometric Series"""))
  UpperCAmelCase							   =      float(
      input("""Enter the common ratio between two terms (r) of the Geometric Series""")
  )
  print("""Formula of Geometric Series => a + ar + ar^2 ... +ar^n""")
  print(geometric_series(nth_term, start_term_a, common_ratio_r))