"""simple docstring"""



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 	__UpperCAmelCase						(__A    ):







			'''simple docstring'''
			_UpperCamelCase     :	Union[str, Any]				    =							'oneformer'
			_UpperCamelCase     :	Optional[int]				    =							{'hidden_size': 'hidden_dim'}





			def __init__( self      ,			snake_case_ = None      ,			snake_case_ = 255      ,			snake_case_ = 150      ,			snake_case_ = 0.1      ,			snake_case_ = 2.0      ,			snake_case_ = 5.0      ,			snake_case_ = 5.0      ,			snake_case_ = 0.5      ,			snake_case_ = 0.07      ,			snake_case_ = 12_544      ,			snake_case_ = 3.0      ,			snake_case_ = 0.75      ,			snake_case_ = 0.02      ,			snake_case_ = 1.0      ,			snake_case_ = 1E-05      ,			snake_case_ = False      ,			snake_case_ = True      ,			snake_case_ = True      ,			snake_case_ = [4, 8, 16, 32]      ,			snake_case_ = 77      ,			snake_case_ = 256      ,			snake_case_ = 77      ,			snake_case_ = 6      ,			snake_case_ = 49_408      ,			snake_case_ = 2      ,			snake_case_ = 16      ,			snake_case_ = 256      ,			snake_case_ = 256      ,			snake_case_ = 256      ,			snake_case_ = 1_024      ,			snake_case_ = "GN"      ,			snake_case_ = 6      ,			snake_case_ = 10      ,			snake_case_ = True      ,			snake_case_ = 8      ,			snake_case_ = 0.1      ,			snake_case_ = 2_048      ,			snake_case_ = False      ,			snake_case_ = False      ,			snake_case_ = 2      ,			snake_case_ = 4      ,			**snake_case_      ,			):







										'''simple docstring'''


										if backbone_config is None:
																	logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""       )
																	A__					:    int														= 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=snake_case_      ,			out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]      ,			)
										elif isinstance(snake_case_      ,			snake_case_       ):
																	A__					:    List[str]														= backbone_config.get("""model_type"""       )
																	A__					:    str														= CONFIG_MAPPING[backbone_model_type]
																	A__					:    Optional[int]														= config_class.from_dict(snake_case_       )

										A__					:    Any														= backbone_config

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

										super().__init__(**snake_case_       )





			def 		lowerCamelCase				( self       ):







										'''simple docstring'''


										A__					:    Optional[int]														= copy.deepcopy(self.__dict__       )
										A__					:    Union[str, Any]														= self.backbone_config.to_dict()
										A__					:    Tuple														= self.__class__.model_type
										return output






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							if not isinstance(lowerCAmelCase ,     lowerCAmelCase       ):
														A__					:    int														= F'''Input value of [number={number}] must be an integer'''
														raise TypeError(lowerCAmelCase       )
							if number < 0 or number % 2 == 1:
														return False
							A__					:    List[Any]														= int(number**0.5       )
							return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





