



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      __lowerCAmelCase       (     A		):
			UpperCamelCase     =	'''oneformer'''
			UpperCamelCase     =	{'''hidden_size''': '''hidden_dim'''}



			def __init__(		self  :    Optional[Any]					,						A  :    Optional[Dict] = None					,						A  :    int = 2_55					,						A  :    int = 1_50					,						A  :    int = 0.1					,						A  :    float = 2.0					,						A  :    float = 5.0					,						A  :    float = 5.0					,						A  :    float = 0.5					,						A  :    float = 0.0_7					,						A  :    int = 1_25_44					,						A  :    float = 3.0					,						A  :    float = 0.7_5					,						A  :    float = 0.0_2					,						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 = 2_56					,						A  :    int = 77					,						A  :    int = 6					,						A  :    int = 4_94_08					,						A  :    int = 2					,						A  :    int = 16					,						A  :    int = 2_56					,						A  :    int = 2_56					,						A  :    int = 2_56					,						A  :    int = 10_24					,						A  :    str = "GN"					,						A  :    int = 6					,						A  :    int = 10					,						A  :    bool = True					,						A  :    int = 8					,						A  :    float = 0.1					,						A  :    int = 20_48					,						A  :    bool = False					,						A  :    bool = False					,						A  :    int = 2					,						A  :    int = 4					,						**A  :    Tuple					,						)  ->       Union[str, Any]:



						"""simple docstring"""






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

						_UpperCAmelCase									=  backbone_config

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

						super().__init__(**A)



			def        _lowerCamelCase			(		self  :    Dict)  ->       Dict[str, any]:



						"""simple docstring"""






						_UpperCAmelCase									=  copy.deepcopy(self.__dict__)
						_UpperCAmelCase									=  self.backbone_config.to_dict()
						_UpperCAmelCase									=  self.__class__.model_type
						return output





import sys

sys.set_int_max_str_digits(0)  # type: ignore





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			_UpperCAmelCase									=  [0] * 11
			_UpperCAmelCase									=  [0] * 11

			# mark last 9 numbers
			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10
			# flag
			_UpperCAmelCase									=  True

			# check last 9 numbers for pandigitality

			for x in range(9       ):
						if not check_last[x + 1]:
									_UpperCAmelCase									=  False
			if not f:
						return f

			# mark first 9 numbers
			_UpperCAmelCase									=  int(str(_UpperCAmelCase       )[:9]       )

			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10

			# check first 9 numbers for pandigitality

			for x in range(9       ):
						if not check_front[x + 1]:
									_UpperCAmelCase									=  False
			return f





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			_UpperCAmelCase									=  [0] * 11

			# mark last 9 numbers
			for _ in range(9       ):
						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  number // 10
			# flag
			_UpperCAmelCase									=  True

			# check last 9 numbers for pandigitality

			for x in range(9       ):
						if not check_last[x + 1]:
									_UpperCAmelCase									=  False
			return f





def 				A      (	)							->					int:







			'''simple docstring'''
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  2
			# temporary Fibonacci numbers

			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  2
			# temporary Fibonacci numbers mod 1e9

			# mod m=1e9, done for fast optimisation
			_UpperCAmelCase									=  [0] * 1_000_000
			_UpperCAmelCase									=  1_000_000_000

			for x in range(1_000_000       ):
						_UpperCAmelCase									=  (aa + ba) % m
						_UpperCAmelCase									=  ba % m
						_UpperCAmelCase									=  ca % m
						if checka(_UpperCAmelCase       ):
									_UpperCAmelCase									=  1

			for x in range(1_000_000       ):
						_UpperCAmelCase									=  a + b
						_UpperCAmelCase									=  b
						_UpperCAmelCase									=  c
						# perform check only if in tocheck
						if tocheck[x + 3] and check(_UpperCAmelCase       ):
									return x + 3  # first 2 already done
			return -1


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