
"""simple docstring"""







import copy
from typing import Dict, Optional

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


lowercase_						    =					logging.get_logger(__name__)

lowercase_						    =					{
    "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_	(lowerCamelCase_						):
 """simple docstring"""




 UpperCamelCase_						:     int				   =    """oneformer"""
 UpperCamelCase_						:     Tuple				   =    {"""hidden_size""": """hidden_dim"""}




 def __init__( self       : str     ,   a_       : Optional[Dict] = None     ,   a_       : int = 2_55     ,   a_       : int = 1_50     ,   a_       : int = 0.1     ,   a_       : float = 2.0     ,   a_       : float = 5.0     ,   a_       : float = 5.0     ,   a_       : float = 0.5     ,   a_       : float = 0.07     ,   a_       : int = 1_25_44     ,   a_       : float = 3.0     ,   a_       : float = 0.75     ,   a_       : float = 0.02     ,   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 = 2_56     ,   a_       : int = 77     ,   a_       : int = 6     ,   a_       : int = 4_94_08     ,   a_       : int = 2     ,   a_       : int = 16     ,   a_       : int = 2_56     ,   a_       : int = 2_56     ,   a_       : int = 2_56     ,   a_       : int = 10_24     ,   a_       : str = "GN"     ,   a_       : int = 6     ,   a_       : int = 10     ,   a_       : bool = True     ,   a_       : int = 8     ,   a_       : float = 0.1     ,   a_       : int = 20_48     ,   a_       : bool = False     ,   a_       : bool = False     ,   a_       : int = 2     ,   a_       : int = 4     ,   **a_       : Any     ,   )->					Any:



      """simple docstring"""

      if backbone_config is None:
           logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone.""" )
           UpperCAmelCase_						:    Union[str, Any]    					=  CONFIG_MAPPING["""swin"""](
               image_size=2_24     ,   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_						:    Any    					=  backbone_config.get("""model_type""" )
           UpperCAmelCase_						:    Dict    					=  CONFIG_MAPPING[backbone_model_type]
           UpperCAmelCase_						:    int    					=  config_class.from_dict(a_ )

      UpperCAmelCase_						:    Tuple    					=  backbone_config

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

      super().__init__(**a_ )






 def   a      ( self       : Optional[Any] )->					Dict[str, any]:



      """simple docstring"""

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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_						    =					{"configuration_visual_bert": ["VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VisualBertConfig"]}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_						    =					[
           "VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VisualBertForMultipleChoice",
           "VisualBertForPreTraining",
           "VisualBertForQuestionAnswering",
           "VisualBertForRegionToPhraseAlignment",
           "VisualBertForVisualReasoning",
           "VisualBertLayer",
           "VisualBertModel",
           "VisualBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_visual_bert import VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, VisualBertConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_visual_bert import (
                  VISUAL_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VisualBertForMultipleChoice,
                  VisualBertForPreTraining,
                  VisualBertForQuestionAnswering,
                  VisualBertForRegionToPhraseAlignment,
                  VisualBertForVisualReasoning,
                  VisualBertLayer,
                  VisualBertModel,
                  VisualBertPreTrainedModel,
              )


else:
       import sys

       lowercase_						    =					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

