



'''simple docstring'''



import copy
from typing import Dict, Optional

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


lowerCAmelCase						:		List[str]						      = logging.get_logger(__name__)

lowerCAmelCase						:		Union[str, Any]						      = {
    "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__						(		_SCREAMING_SNAKE_CASE			):
					a   :  Optional[int]   =				"""oneformer"""
					a   :  int   =				{"""hidden_size""": """hidden_dim"""}



					def __init__(     self      ,					UpperCamelCase = None      ,					UpperCamelCase = 255      ,					UpperCamelCase = 150      ,					UpperCamelCase = 0.1      ,					UpperCamelCase = 2.0      ,					UpperCamelCase = 5.0      ,					UpperCamelCase = 5.0      ,					UpperCamelCase = 0.5      ,					UpperCamelCase = 0.07      ,					UpperCamelCase = 1_2544      ,					UpperCamelCase = 3.0      ,					UpperCamelCase = 0.75      ,					UpperCamelCase = 0.02      ,					UpperCamelCase = 1.0      ,					UpperCamelCase = 1E-05      ,					UpperCamelCase = False      ,					UpperCamelCase = True      ,					UpperCamelCase = True      ,					UpperCamelCase = [4, 8, 16, 32]      ,					UpperCamelCase = 77      ,					UpperCamelCase = 256      ,					UpperCamelCase = 77      ,					UpperCamelCase = 6      ,					UpperCamelCase = 4_9408      ,					UpperCamelCase = 2      ,					UpperCamelCase = 16      ,					UpperCamelCase = 256      ,					UpperCamelCase = 256      ,					UpperCamelCase = 256      ,					UpperCamelCase = 1024      ,					UpperCamelCase = "GN"      ,					UpperCamelCase = 6      ,					UpperCamelCase = 10      ,					UpperCamelCase = True      ,					UpperCamelCase = 8      ,					UpperCamelCase = 0.1      ,					UpperCamelCase = 2048      ,					UpperCamelCase = False      ,					UpperCamelCase = False      ,					UpperCamelCase = 2      ,					UpperCamelCase = 4      ,					**UpperCamelCase      ,					)  ->      Dict:
						if backbone_config is None:
							logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."  )
							__lowerCAmelCase       				=	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=UpperCamelCase      ,					out_features=["stage1", "stage2", "stage3", "stage4"]      ,					)
						elif isinstance(UpperCamelCase      ,					UpperCamelCase  ):
							__lowerCAmelCase       				=	backbone_config.get("model_type"  )
							__lowerCAmelCase       				=	CONFIG_MAPPING[backbone_model_type]
							__lowerCAmelCase       				=	config_class.from_dict(UpperCamelCase  )

						__lowerCAmelCase       				=	backbone_config

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

						super().__init__(**UpperCamelCase  )



					def 							UpperCAmelCase_			(     self  )  ->      Dict[str, any]:
						__lowerCAmelCase       				=	copy.deepcopy(self.__dict__  )
						__lowerCAmelCase       				=	self.backbone_config.to_dict()
						__lowerCAmelCase       				=	self.__class__.model_type
						return output



'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_       :     Any 							=     {"configuration_bit": ["BIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BitConfig", "BitOnnxConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     int 							=     [
     "BIT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "BitForImageClassification",
     "BitModel",
     "BitPreTrainedModel",
     "BitBackbone",
 ]


try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_       :     Tuple 							=     ["BitImageProcessor"]


if TYPE_CHECKING:
 from .configuration_bit import BIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BitConfig, BitOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_bit import (
      BIT_PRETRAINED_MODEL_ARCHIVE_LIST,
      BitBackbone,
      BitForImageClassification,
      BitModel,
      BitPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_bit import BitImageProcessor

else:
 import sys

 a_       :     Union[str, Any] 							=     _LazyModule(__name__, globals()["__file__"], _import_structure)

