
"""simple docstring"""
import copy
from typing import Dict, Optional

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


lowercase		:    str			=       logging.get_logger(__name__)

lowercase		:    Dict			=       {
    '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  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''oneformer'''
							SCREAMING_SNAKE_CASE		  =    {'''hidden_size''': '''hidden_dim'''}






							def __init__(       self    :      Optional[int]    ,			snake_case__    :      Optional[Dict] = None    ,			snake_case__    :      int = 2_5_5    ,			snake_case__    :      int = 1_5_0    ,			snake_case__    :      int = 0.1    ,			snake_case__    :      float = 2.0    ,			snake_case__    :      float = 5.0    ,			snake_case__    :      float = 5.0    ,			snake_case__    :      float = 0.5    ,			snake_case__    :      float = 0.07    ,			snake_case__    :      int = 1_2_5_4_4    ,			snake_case__    :      float = 3.0    ,			snake_case__    :      float = 0.75    ,			snake_case__    :      float = 0.02    ,			snake_case__    :      float = 1.0    ,			snake_case__    :      float = 1e-05    ,			snake_case__    :      bool = False    ,			snake_case__    :      bool = True    ,			snake_case__    :      bool = True    ,			snake_case__    :      Optional[list] = [4, 8, 1_6, 3_2]    ,			snake_case__    :      int = 7_7    ,			snake_case__    :      int = 2_5_6    ,			snake_case__    :      int = 7_7    ,			snake_case__    :      int = 6    ,			snake_case__    :      int = 4_9_4_0_8    ,			snake_case__    :      int = 2    ,			snake_case__    :      int = 1_6    ,			snake_case__    :      int = 2_5_6    ,			snake_case__    :      int = 2_5_6    ,			snake_case__    :      int = 2_5_6    ,			snake_case__    :      int = 1_0_2_4    ,			snake_case__    :      str = "GN"    ,			snake_case__    :      int = 6    ,			snake_case__    :      int = 1_0    ,			snake_case__    :      bool = True    ,			snake_case__    :      int = 8    ,			snake_case__    :      float = 0.1    ,			snake_case__    :      int = 2_0_4_8    ,			snake_case__    :      bool = False    ,			snake_case__    :      bool = False    ,			snake_case__    :      int = 2    ,			snake_case__    :      int = 4    ,			**snake_case__    :      Tuple    ,			):






												"""simple docstring"""



												if backbone_config is None:
																	logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""     )
																	__a:    Union[str, Any]		     =       CONFIG_MAPPING["""swin"""](
																	    image_size=2_2_4    ,			in_channels=3    ,			patch_size=4    ,			embed_dim=9_6    ,			depths=[2, 2, 6, 2]    ,			num_heads=[3, 6, 1_2, 2_4]    ,			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:    Dict		     =       backbone_config.get("""model_type"""     )
																	__a:    Optional[int]		     =       CONFIG_MAPPING[backbone_model_type]
																	__a:    Optional[int]		     =       config_class.from_dict(snake_case__     )

												__a:    Dict		     =       backbone_config

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

												super().__init__(**snake_case__     )






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



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


"""simple docstring"""
from transformers import ProcessorMixin




class  _snake_case	(    _lowercase							):
							SCREAMING_SNAKE_CASE		  =    '''AutoFeatureExtractor'''
							SCREAMING_SNAKE_CASE		  =    '''AutoTokenizer'''
