import copy
from typing import Dict, Optional

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


_SCREAMING_SNAKE_CASE:						int    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						Tuple    	=       {
    '''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							(     _lowercase							):






  """simple docstring"""







  A_						 =      "oneformer"
  A_						 =      {"hidden_size": "hidden_dim"}



  def __init__(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Optional[Dict] = None  ,					__SCREAMING_SNAKE_CASE    :				int = 255  ,					__SCREAMING_SNAKE_CASE    :				int = 150  ,					__SCREAMING_SNAKE_CASE    :				int = 0.1  ,					__SCREAMING_SNAKE_CASE    :				float = 2.0  ,					__SCREAMING_SNAKE_CASE    :				float = 5.0  ,					__SCREAMING_SNAKE_CASE    :				float = 5.0  ,					__SCREAMING_SNAKE_CASE    :				float = 0.5  ,					__SCREAMING_SNAKE_CASE    :				float = 0.0_7  ,					__SCREAMING_SNAKE_CASE    :				int = 12_544  ,					__SCREAMING_SNAKE_CASE    :				float = 3.0  ,					__SCREAMING_SNAKE_CASE    :				float = 0.7_5  ,					__SCREAMING_SNAKE_CASE    :				float = 0.0_2  ,					__SCREAMING_SNAKE_CASE    :				float = 1.0  ,					__SCREAMING_SNAKE_CASE    :				float = 1e-05  ,					__SCREAMING_SNAKE_CASE    :				bool = False  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					__SCREAMING_SNAKE_CASE    :				Optional[list] = [4, 8, 16, 32]  ,					__SCREAMING_SNAKE_CASE    :				int = 77  ,					__SCREAMING_SNAKE_CASE    :				int = 256  ,					__SCREAMING_SNAKE_CASE    :				int = 77  ,					__SCREAMING_SNAKE_CASE    :				int = 6  ,					__SCREAMING_SNAKE_CASE    :				int = 49_408  ,					__SCREAMING_SNAKE_CASE    :				int = 2  ,					__SCREAMING_SNAKE_CASE    :				int = 16  ,					__SCREAMING_SNAKE_CASE    :				int = 256  ,					__SCREAMING_SNAKE_CASE    :				int = 256  ,					__SCREAMING_SNAKE_CASE    :				int = 256  ,					__SCREAMING_SNAKE_CASE    :				int = 1_024  ,					__SCREAMING_SNAKE_CASE    :				str = "GN"  ,					__SCREAMING_SNAKE_CASE    :				int = 6  ,					__SCREAMING_SNAKE_CASE    :				int = 10  ,					__SCREAMING_SNAKE_CASE    :				bool = True  ,					__SCREAMING_SNAKE_CASE    :				int = 8  ,					__SCREAMING_SNAKE_CASE    :				float = 0.1  ,					__SCREAMING_SNAKE_CASE    :				int = 2_048  ,					__SCREAMING_SNAKE_CASE    :				bool = False  ,					__SCREAMING_SNAKE_CASE    :				bool = False  ,					__SCREAMING_SNAKE_CASE    :				int = 2  ,					__SCREAMING_SNAKE_CASE    :				int = 4  ,					**__SCREAMING_SNAKE_CASE    :				List[str]  ,					):






     """simple docstring"""
     if backbone_config is None:
        logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."   )
        _snake_case              =      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=UpperCAmelCase_  ,					out_features=["stage1", "stage2", "stage3", "stage4"]  ,					)
     elif isinstance(UpperCAmelCase_  ,					UpperCAmelCase_   ):
        _snake_case              =      backbone_config.get("model_type"   )
        _snake_case              =      CONFIG_MAPPING[backbone_model_type]
        _snake_case              =      config_class.from_dict(UpperCAmelCase_   )

     _snake_case              =      backbone_config

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

     super().__init__(**UpperCAmelCase_   )







  def 				_a				(			self    :				str   ):






     """simple docstring"""
     _snake_case              =      copy.deepcopy(self.__dict__   )
     _snake_case              =      self.backbone_config.to_dict()
     _snake_case              =      self.__class__.model_type
     return output


from math import gcd






def    UpperCAmelCase__(						lowerCamelCase_ = 12000  ):



			"""simple docstring"""





			_snake_case              =      0
			for d in range(max_d + 1  ):
						for n in range(d // 3 + 1							,	(d + 1) // 2  ):
									if gcd(lowerCamelCase_							,	lowerCamelCase_  ) == 1:
												fractions_number += 1
			return fractions_number


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

