





import copy
from typing import Dict, Optional

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


_lowerCamelCase       =	logging.get_logger(__name__)

_lowerCamelCase       =	{
    '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_				(   UpperCamelCase__			):
							lowerCamelCase_							=    "oneformer"
							lowerCamelCase_							=    {"hidden_size": "hidden_dim"}


							def __init__(							self			:List[Any]							, __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 = 0.0_7							, __A			:int = 1_2544							, __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 = 256							, __A			:int = 77							, __A			:int = 6							, __A			:int = 4_9408							, __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]							, )      ->      Optional[Any]:



									"""simple docstring"""



									if backbone_config is None:
											logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""						)
											SCREAMING_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						):
											SCREAMING_SNAKE_CASE__								=					backbone_config.get("""model_type"""						)
											SCREAMING_SNAKE_CASE__								=					CONFIG_MAPPING[backbone_model_type]
											SCREAMING_SNAKE_CASE__								=					config_class.from_dict(__A						)

									SCREAMING_SNAKE_CASE__								=					backbone_config

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

									super().__init__(**__A						)


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



									"""simple docstring"""



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






def  SCREAMING_SNAKE_CASE__							(				UpperCamelCase__:							str			):
		SCREAMING_SNAKE_CASE__								=					SCREAMING_SNAKE_CASE__								=					0
		for letter in data:
				SCREAMING_SNAKE_CASE__								=					(hash_ << 4) + ord(UpperCamelCase__			)
				SCREAMING_SNAKE_CASE__								=					hash_ & 0XF_0_0_0_0_0_0_0
				if x != 0:
						hash_ ^= x >> 24
				hash_ &= ~x
		return hash_


if __name__ == "__main__":
	import doctest

	doctest.testmod()