import copy
from typing import Dict, Optional

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


UpperCAmelCase      		=					logging.get_logger(__name__)

UpperCAmelCase      		=					{
    '''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  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Tuple					     =			'oneformer'
 _lowerCamelCase    :						Optional[int]					     =			{'hidden_size': 'hidden_dim'}

 def __init__(      self							:		str							,      lowerCamelCase							:		Optional[Dict] = None							,      lowerCamelCase							:		int = 2_5_5							,      lowerCamelCase							:		int = 1_5_0							,      lowerCamelCase							:		int = 0.1							,      lowerCamelCase							:		float = 2.0							,      lowerCamelCase							:		float = 5.0							,      lowerCamelCase							:		float = 5.0							,      lowerCamelCase							:		float = 0.5							,      lowerCamelCase							:		float = 0.0_7							,      lowerCamelCase							:		int = 1_2_5_4_4							,      lowerCamelCase							:		float = 3.0							,      lowerCamelCase							:		float = 0.7_5							,      lowerCamelCase							:		float = 0.0_2							,      lowerCamelCase							:		float = 1.0							,      lowerCamelCase							:		float = 1e-05							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		Optional[list] = [4, 8, 1_6, 3_2]							,      lowerCamelCase							:		int = 7_7							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 7_7							,      lowerCamelCase							:		int = 6							,      lowerCamelCase							:		int = 4_9_4_0_8							,      lowerCamelCase							:		int = 2							,      lowerCamelCase							:		int = 1_6							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 2_5_6							,      lowerCamelCase							:		int = 1_0_2_4							,      lowerCamelCase							:		str = "GN"							,      lowerCamelCase							:		int = 6							,      lowerCamelCase							:		int = 1_0							,      lowerCamelCase							:		bool = True							,      lowerCamelCase							:		int = 8							,      lowerCamelCase							:		float = 0.1							,      lowerCamelCase							:		int = 2_0_4_8							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		bool = False							,      lowerCamelCase							:		int = 2							,      lowerCamelCase							:		int = 4							,      **lowerCamelCase							:		Union[str, Any]							,      )			->				Optional[Any]:
        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_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=lowerCamelCase							,      out_features=['stage1', 'stage2', 'stage3', 'stage4']							,      )
        elif isinstance(lowerCamelCase							,      lowerCamelCase	):
               _UpperCamelCase  :							int	    =	backbone_config.get('model_type'	)
               _UpperCamelCase  :							Optional[int]	    =	CONFIG_MAPPING[backbone_model_type]
               _UpperCamelCase  :							List[str]	    =	config_class.from_dict(lowerCamelCase	)

        _UpperCamelCase  :							Optional[int]	    =	backbone_config

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

        super().__init__(**lowerCamelCase	)

 def        __snake_case       (      self							:		Union[str, Any]	)			->				Dict[str, any]:
        _UpperCamelCase  :							Any	    =	copy.deepcopy(self.__dict__	)
        _UpperCamelCase  :							int	    =	self.backbone_config.to_dict()
        _UpperCamelCase  :							Optional[Any]	    =	self.__class__.model_type
        return output











'''simple docstring'''







from typing import TYPE_CHECKING

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


UpperCamelCase_					=				{
    "configuration_flava": [
        "FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "FlavaConfig",
        "FlavaImageCodebookConfig",
        "FlavaImageConfig",
        "FlavaMultimodalConfig",
        "FlavaTextConfig",
    ],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				["FlavaFeatureExtractor"]
  UpperCamelCase_					=				["FlavaImageProcessor"]
  UpperCamelCase_					=				["FlavaProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCamelCase_					=				[
      "FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlavaForPreTraining",
      "FlavaImageCodebook",
      "FlavaImageModel",
      "FlavaModel",
      "FlavaMultimodalModel",
      "FlavaPreTrainedModel",
      "FlavaTextModel",
  ]

if TYPE_CHECKING:
  from .configuration_flava import (
      FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
      FlavaConfig,
      FlavaImageCodebookConfig,
      FlavaImageConfig,
      FlavaMultimodalConfig,
      FlavaTextConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_flava import FlavaFeatureExtractor
    from .image_processing_flava import FlavaImageProcessor
    from .processing_flava import FlavaProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flava import (
        FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlavaForPreTraining,
        FlavaImageCodebook,
        FlavaImageModel,
        FlavaModel,
        FlavaMultimodalModel,
        FlavaPreTrainedModel,
        FlavaTextModel,
    )

else:
  import sys

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