

import copy
from typing import Dict, Optional

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


__A     						=      logging.get_logger(__name__)

__A     						=      {
    '''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     lowercase_       (    __lowercase	):
      UpperCamelCase_     :						Any		 =							"oneformer"
      UpperCamelCase_     :						List[str]		 =							{"hidden_size": "hidden_dim"}



      def __init__(     self :							str     ,   A__ :							Optional[Dict] = None     ,   A__ :							int = 255     ,   A__ :							int = 150     ,   A__ :							int = 0.1     ,   A__ :							float = 2.0     ,   A__ :							float = 5.0     ,   A__ :							float = 5.0     ,   A__ :							float = 0.5     ,   A__ :							float = 0.07     ,   A__ :							int = 12544     ,   A__ :							float = 3.0     ,   A__ :							float = 0.75     ,   A__ :							float = 0.02     ,   A__ :							float = 1.0     ,   A__ :							float = 1e-05     ,   A__ :							bool = False     ,   A__ :							bool = True     ,   A__ :							bool = True     ,   A__ :							Optional[list] = [4, 8, 16, 32]     ,   A__ :							int = 77     ,   A__ :							int = 256     ,   A__ :							int = 77     ,   A__ :							int = 6     ,   A__ :							int = 49408     ,   A__ :							int = 2     ,   A__ :							int = 16     ,   A__ :							int = 256     ,   A__ :							int = 256     ,   A__ :							int = 256     ,   A__ :							int = 1024     ,   A__ :							str = "GN"     ,   A__ :							int = 6     ,   A__ :							int = 10     ,   A__ :							bool = True     ,   A__ :							int = 8     ,   A__ :							float = 0.1     ,   A__ :							int = 2048     ,   A__ :							bool = False     ,   A__ :							bool = False     ,   A__ :							int = 2     ,   A__ :							int = 4     ,   **A__ :							Union[str, Any]     ,   )       ->	Dict:
        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=A__     ,   out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']     ,   )
        elif isinstance(A__     ,   A__						):
          _snake_case		=				backbone_config.get('''model_type'''						)
          _snake_case		=				CONFIG_MAPPING[backbone_model_type]
          _snake_case		=				config_class.from_dict(A__						)

        _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__(**A__						)







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




from __future__ import annotations

from collections import Counter



def  snake_case_(_UpperCamelCase	)				->      list[int]:



  """simple docstring"""






  _snake_case		=				[True] * n
  _snake_case		=				False
  _snake_case		=				False
  _snake_case		=				True

  for i in range(3					,			int(n**0.5 + 1	)					,			2	):
    _snake_case		=				i * 2
    while index < n:
      _snake_case		=				False
      _snake_case		=				index + i

  _snake_case		=				[2]

  for i in range(3					,			_UpperCamelCase					,			2	):
    if is_prime[i]:
      primes.append(_UpperCamelCase	)

  return primes



def  snake_case_(_UpperCamelCase	)				->      list[list[int]]:



  """simple docstring"""






  _snake_case		=				str(_UpperCamelCase	)
  _snake_case		=				[]
  _snake_case		=				['''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''']

  for duplicate in Counter(_UpperCamelCase	) - Counter(set(_UpperCamelCase	)	):
    _snake_case		=				[int(number_str.replace(_UpperCamelCase					,			_UpperCamelCase	)	) for digit in digits]
    replacements.append(_UpperCamelCase	)

  return replacements



def  snake_case_(_UpperCamelCase = 8	)				->      int:



  """simple docstring"""






  _snake_case		=				set()

  # Filter primes with less than 3 replaceable digits
  _snake_case		=				{
      x for x in set(prime_sieve(1_000_000	)	) if len(str(_UpperCamelCase	)	) - len(set(str(_UpperCamelCase	)	)	) >= 3
  }

  for prime in primes:
    if prime in numbers_checked:
      continue

    _snake_case		=				digit_replacements(_UpperCamelCase	)

    for family in replacements:
      numbers_checked.update(_UpperCamelCase	)
      _snake_case		=				primes.intersection(_UpperCamelCase	)

      if len(_UpperCamelCase	) != family_length:
        continue

      return min(_UpperCamelCase	)

  return -1


if __name__ == "__main__":
 print(solution())

