






import copy
from typing import Dict, Optional

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


A_			:Tuple	    =		logging.get_logger(__name__)

A_			:Optional[int]	    =		{
    '''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	(     _UpperCamelCase					):





				"""simple docstring"""







				UpperCamelCase__					: Optional[int]     					="""oneformer"""
				UpperCamelCase__					: Optional[int]     					={"""hidden_size""": """hidden_dim"""}


				def __init__(					self				,    lowerCamelCase__ = None				,    lowerCamelCase__ = 255				,    lowerCamelCase__ = 150				,    lowerCamelCase__ = 0.1				,    lowerCamelCase__ = 2.0				,    lowerCamelCase__ = 5.0				,    lowerCamelCase__ = 5.0				,    lowerCamelCase__ = 0.5				,    lowerCamelCase__ = 0.07				,    lowerCamelCase__ = 12544				,    lowerCamelCase__ = 3.0				,    lowerCamelCase__ = 0.75				,    lowerCamelCase__ = 0.02				,    lowerCamelCase__ = 1.0				,    lowerCamelCase__ = 1E-05				,    lowerCamelCase__ = False				,    lowerCamelCase__ = True				,    lowerCamelCase__ = True				,    lowerCamelCase__ = [4, 8, 16, 32]				,    lowerCamelCase__ = 77				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 77				,    lowerCamelCase__ = 6				,    lowerCamelCase__ = 49408				,    lowerCamelCase__ = 2				,    lowerCamelCase__ = 16				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 256				,    lowerCamelCase__ = 1024				,    lowerCamelCase__ = "GN"				,    lowerCamelCase__ = 6				,    lowerCamelCase__ = 10				,    lowerCamelCase__ = True				,    lowerCamelCase__ = 8				,    lowerCamelCase__ = 0.1				,    lowerCamelCase__ = 2048				,    lowerCamelCase__ = False				,    lowerCamelCase__ = False				,    lowerCamelCase__ = 2				,    lowerCamelCase__ = 4				,    **lowerCamelCase__				,    ):




									"""simple docstring"""

									if backbone_config is None:
														logger.info('`backbone_config` is unset. Initializing the config with the default `Swin` backbone.'       )
														__UpperCamelCase :   List[str]									=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__       ):
														__UpperCamelCase :   List[Any]									=backbone_config.get('model_type'       )
														__UpperCamelCase :   List[str]									=CONFIG_MAPPING[backbone_model_type]
														__UpperCamelCase :   Optional[Any]									=config_class.from_dict(a__       )

									__UpperCamelCase :   Union[str, Any]									=backbone_config

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

									super().__init__(**a__       )







				def  __lowercase				(					self       ):




									"""simple docstring"""

									__UpperCamelCase :   int									=copy.deepcopy(self.__dict__       )
									__UpperCamelCase :   List[str]									=self.backbone_config.to_dict()
									__UpperCamelCase :   List[Any]									=self.__class__.model_type
									return output





"""simple docstring"""


from .hash_table import HashTable
class snake_case   (	_UpperCamelCase):







		def __init__(       self   :							Any     , *a__   :							Any     , **a__   :							Tuple		)							->    Tuple:





						'''simple docstring'''







						super().__init__(*a__     , **a__		)







		def 			a_			(       self   :							Tuple     , a__   :							int     , a__   :							str=None		)							->    Optional[int]:





						'''simple docstring'''







						_A       	=      1
						_A       	=      self.hash_function(key + i * i		)

						while self.values[new_key] is not None and self.values[new_key] != key:
										i += 1
										_A       	=      (
										    self.hash_function(key + i * i		)
										    if not self.balanced_factor() >= self.lim_charge
										    else None
										)

										if new_key is None:
														break

						return new_key