





def  A_(      A ):
	UpperCAmelCase_		    =  [True for i in range(n + 1 )]
	UpperCAmelCase_		    =  [i - 1 for i in range(n + 1 )]
	UpperCAmelCase_		    =  []
	for i in range(2					,   n + 1 ):
		if is_prime[i]:
			primes.append(lowerCAmelCase__ )
		for j in range(0					,   len(lowerCAmelCase__ ) ):
			if i * primes[j] >= n:
				break
			UpperCAmelCase_		    =  False

			if i % primes[j] == 0:
				UpperCAmelCase_		    =  totients[i] * primes[j]
				break

			UpperCAmelCase_		    =  totients[i] * (primes[j] - 1)

	return totients


if __name__ == "__main__":
				import doctest

				doctest.testmod()






'''simple docstring'''






import copy
from typing import Dict, Optional

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


a_       :     List[str] 							=     logging.get_logger(__name__)

a_       :     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 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						"""oneformer"""
 _lowerCAmelCase			       =						{"""hidden_size""": """hidden_dim"""}
 def __init__(			self   ,					__magic_name__ = None   ,					__magic_name__ = 2_55   ,					__magic_name__ = 1_50   ,					__magic_name__ = 0.1   ,					__magic_name__ = 2.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 5.0   ,					__magic_name__ = 0.5   ,					__magic_name__ = 0.0_7   ,					__magic_name__ = 1_25_44   ,					__magic_name__ = 3.0   ,					__magic_name__ = 0.7_5   ,					__magic_name__ = 0.0_2   ,					__magic_name__ = 1.0   ,					__magic_name__ = 1e-05   ,					__magic_name__ = False   ,					__magic_name__ = True   ,					__magic_name__ = True   ,					__magic_name__ = [4, 8, 16, 32]   ,					__magic_name__ = 77   ,					__magic_name__ = 2_56   ,					__magic_name__ = 77   ,					__magic_name__ = 6   ,					__magic_name__ = 4_94_08   ,					__magic_name__ = 2   ,					__magic_name__ = 16   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 2_56   ,					__magic_name__ = 10_24   ,					__magic_name__ = "GN"   ,					__magic_name__ = 6   ,					__magic_name__ = 10   ,					__magic_name__ = True   ,					__magic_name__ = 8   ,					__magic_name__ = 0.1   ,					__magic_name__ = 20_48   ,					__magic_name__ = False   ,					__magic_name__ = False   ,					__magic_name__ = 2   ,					__magic_name__ = 4   ,					**__magic_name__   ,					)       ->				Dict:
      if backbone_config is None:
           logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'    )
           _a         =				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=__magic_name__   ,					out_features=['stage1', 'stage2', 'stage3', 'stage4']   ,					)
      elif isinstance(__magic_name__   ,					__magic_name__    ):
           _a         =				backbone_config.get('model_type'    )
           _a         =				CONFIG_MAPPING[backbone_model_type]
           _a         =				config_class.from_dict(__magic_name__    )

      _a         =				backbone_config

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

      super().__init__(**__magic_name__    )




 def 				__UpperCAmelCase		(			self    )       ->				Dict[str, any]:
      _a         =				copy.deepcopy(self.__dict__    )
      _a         =				self.backbone_config.to_dict()
      _a         =				self.__class__.model_type
      return output

