



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

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


A:   Tuple								=		{
    "facebook/mask2former-swin-small-coco-instance": (
        "https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json"
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

A:   int								=		logging.get_logger(__name__)



class       SCREAMING_SNAKE_CASE__  (					UpperCAmelCase__ ):
   __lowerCAmelCase  :   Dict						=						'mask2former'
   __lowerCAmelCase  :   int						=						['swin']
   __lowerCAmelCase  :   Tuple						=						{'hidden_size': 'hidden_dim'}

   def __init__(   self      ,     _SCREAMING_SNAKE_CASE = None      ,     _SCREAMING_SNAKE_CASE = 256      ,     _SCREAMING_SNAKE_CASE = 256      ,     _SCREAMING_SNAKE_CASE = 256      ,     _SCREAMING_SNAKE_CASE = 1024      ,     _SCREAMING_SNAKE_CASE = "relu"      ,     _SCREAMING_SNAKE_CASE = 6      ,     _SCREAMING_SNAKE_CASE = 10      ,     _SCREAMING_SNAKE_CASE = 8      ,     _SCREAMING_SNAKE_CASE = 0.0      ,     _SCREAMING_SNAKE_CASE = 2048      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = False      ,     _SCREAMING_SNAKE_CASE = 4      ,     _SCREAMING_SNAKE_CASE = 255      ,     _SCREAMING_SNAKE_CASE = 100      ,     _SCREAMING_SNAKE_CASE = 0.1      ,     _SCREAMING_SNAKE_CASE = 2.0      ,     _SCREAMING_SNAKE_CASE = 5.0      ,     _SCREAMING_SNAKE_CASE = 5.0      ,     _SCREAMING_SNAKE_CASE = 12544      ,     _SCREAMING_SNAKE_CASE = 3.0      ,     _SCREAMING_SNAKE_CASE = 0.75      ,     _SCREAMING_SNAKE_CASE = 0.02      ,     _SCREAMING_SNAKE_CASE = 1.0      ,     _SCREAMING_SNAKE_CASE = True      ,     _SCREAMING_SNAKE_CASE = [4, 8, 16, 32]      ,     _SCREAMING_SNAKE_CASE = None      ,     **_SCREAMING_SNAKE_CASE      ,     )  ->      List[str]:





    '''simple docstring'''




    if backbone_config is None:
     logger.info("""`backbone_config` is `None`. Initializing the config with the default `Swin` backbone."""						)
     UpperCAmelCase	:					Optional[Any]	=	CONFIG_MAPPING["""swin"""](
         image_size=224      ,     in_channels=3      ,     patch_size=4      ,     embed_dim=96      ,     depths=[2, 2, 18, 2]      ,     num_heads=[3, 6, 12, 24]      ,     window_size=7      ,     drop_path_rate=0.3      ,     use_absolute_embeddings=_SCREAMING_SNAKE_CASE      ,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]      ,     )

    if isinstance(_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE						):
     UpperCAmelCase	:					Optional[int]	=	backbone_config.pop("""model_type"""						)
     UpperCAmelCase	:					int	=	CONFIG_MAPPING[backbone_model_type]
     UpperCAmelCase	:					Dict	=	config_class.from_dict(_SCREAMING_SNAKE_CASE						)

    # verify that the backbone is supported
    if backbone_config.model_type not in self.backbones_supported:
     logger.warning_once(
         F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. "
         F"Supported model types: {','.join(self.backbones_supported						)}"						)

    UpperCAmelCase	:					Any	=	backbone_config
    UpperCAmelCase	:					List[Any]	=	feature_size
    UpperCAmelCase	:					Optional[int]	=	mask_feature_size
    UpperCAmelCase	:					Any	=	hidden_dim
    UpperCAmelCase	:					Optional[int]	=	encoder_feedforward_dim
    UpperCAmelCase	:					Optional[Any]	=	activation_function
    UpperCAmelCase	:					Optional[int]	=	encoder_layers
    UpperCAmelCase	:					Dict	=	decoder_layers
    UpperCAmelCase	:					Optional[Any]	=	num_attention_heads
    UpperCAmelCase	:					List[Any]	=	dropout
    UpperCAmelCase	:					Optional[Any]	=	dim_feedforward
    UpperCAmelCase	:					Optional[Any]	=	pre_norm
    UpperCAmelCase	:					List[str]	=	enforce_input_projection
    UpperCAmelCase	:					List[Any]	=	common_stride
    UpperCAmelCase	:					Tuple	=	ignore_value
    UpperCAmelCase	:					List[Any]	=	num_queries
    UpperCAmelCase	:					Dict	=	no_object_weight
    UpperCAmelCase	:					int	=	class_weight
    UpperCAmelCase	:					List[str]	=	mask_weight
    UpperCAmelCase	:					str	=	dice_weight
    UpperCAmelCase	:					Optional[int]	=	train_num_points
    UpperCAmelCase	:					Union[str, Any]	=	oversample_ratio
    UpperCAmelCase	:					str	=	importance_sample_ratio
    UpperCAmelCase	:					Tuple	=	init_std
    UpperCAmelCase	:					Any	=	init_xavier_std
    UpperCAmelCase	:					Optional[int]	=	use_auxiliary_loss
    UpperCAmelCase	:					Any	=	feature_strides
    UpperCAmelCase	:					Any	=	output_auxiliary_logits
    UpperCAmelCase	:					str	=	decoder_layers

    super().__init__(**_SCREAMING_SNAKE_CASE						)

   @classmethod
   def 	SCREAMING_SNAKE_CASE     (   cls      ,     _SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE						)  ->      List[str]:





    '''simple docstring'''




    return cls(
        backbone_config=_SCREAMING_SNAKE_CASE      ,     **_SCREAMING_SNAKE_CASE      ,     )




   def 	SCREAMING_SNAKE_CASE     (   self						)  ->      Dict[str, any]:





    '''simple docstring'''




    UpperCAmelCase	:					int	=	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_tf_available, is_torch_available


A:   Optional[Any]								=		{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   Optional[int]								=		[
          "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TapasForMaskedLM",
          "TapasForQuestionAnswering",
          "TapasForSequenceClassification",
          "TapasModel",
          "TapasPreTrainedModel",
          "load_tf_weights_in_tapas",
      ]
try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A:   List[str]								=		[
          "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFTapasForMaskedLM",
          "TFTapasForQuestionAnswering",
          "TFTapasForSequenceClassification",
          "TFTapasModel",
          "TFTapasPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
      from .tokenization_tapas import TapasTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tapas import (
                TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TapasForMaskedLM,
                TapasForQuestionAnswering,
                TapasForSequenceClassification,
                TapasModel,
                TapasPreTrainedModel,
                load_tf_weights_in_tapas,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_tapas import (
                TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFTapasForMaskedLM,
                TFTapasForQuestionAnswering,
                TFTapasForSequenceClassification,
                TFTapasModel,
                TFTapasPreTrainedModel,
            )


else:
      import sys

      A:   Optional[Any]								=		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
