


import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


__lowerCamelCase :  List[Any]										=  {
    "facebook/maskformer-swin-base-ade": (
        "https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json"
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

__lowerCamelCase :  str										=  logging.get_logger(__name__)



class       A__     ( _UpperCAmelCase    ):
      _UpperCAmelCase			:Tuple							=     '''maskformer'''
      _UpperCAmelCase			:Optional[int]							=     {'''hidden_size''': '''mask_feature_size'''}
      _UpperCAmelCase			:List[str]							=     ['''resnet''', '''swin''']
      _UpperCAmelCase			:Dict							=     ['''detr''']





      def __init__(				self ,							A_ = 256 ,							A_ = 256 ,							A_ = 0.1 ,							A_ = False ,							A_ = None ,							A_ = None ,							A_ = 0.02 ,							A_ = 1.0 ,							A_ = 1.0 ,							A_ = 1.0 ,							A_ = 20.0 ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       if backbone_config is None:
        # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
        UpperCamelCase			:      Dict 			=     SwinConfig(
            image_size=384 ,							in_channels=3 ,							patch_size=4 ,							embed_dim=128 ,							depths=[2, 2, 18, 2] ,							num_heads=[4, 8, 16, 32] ,							window_size=12 ,							drop_path_rate=0.3 ,							out_features=["stage1", "stage2", "stage3", "stage4"] ,							)

       if isinstance(A_ ,							A_     ):
        UpperCamelCase			:      str 			=     backbone_config.pop("model_type"     )
        UpperCamelCase			:      Dict 			=     CONFIG_MAPPING[backbone_model_type]
        UpperCamelCase			:      List[str] 			=     config_class.from_dict(A_     )

       # 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 MaskFormer. """
            F"""Supported model types: {",".join(self.backbones_supported     )}"""     )

       if decoder_config is None:
        # fall back to https://huggingface.co/facebook/detr-resnet-50
        UpperCamelCase			:      List[str] 			=     DetrConfig()
       else:
        # verify that the decoder is supported
        UpperCamelCase			:      Dict 			=     (
            decoder_config.pop("model_type"     ) if isinstance(A_ ,							A_     ) else decoder_config.model_type
        )
        if decoder_type not in self.decoders_supported:
         raise ValueError(
             F"""Transformer Decoder {decoder_type} not supported, please use one of"""
             F""" {",".join(self.decoders_supported     )}"""     )
        if isinstance(A_ ,							A_     ):
         UpperCamelCase			:      Optional[Any] 			=     CONFIG_MAPPING[decoder_type]
         UpperCamelCase			:      Optional[Any] 			=     config_class.from_dict(A_     )

       UpperCamelCase			:      str 			=     backbone_config
       UpperCamelCase			:      Optional[Any] 			=     decoder_config
       # main feature dimension for the model
       UpperCamelCase			:      Optional[Any] 			=     fpn_feature_size
       UpperCamelCase			:      Tuple 			=     mask_feature_size
       # initializer
       UpperCamelCase			:      List[str] 			=     init_std
       UpperCamelCase			:      List[Any] 			=     init_xavier_std
       # Hungarian matcher && loss
       UpperCamelCase			:      Optional[Any] 			=     cross_entropy_weight
       UpperCamelCase			:      List[str] 			=     dice_weight
       UpperCamelCase			:      Optional[Any] 			=     mask_weight
       UpperCamelCase			:      Optional[Any] 			=     use_auxiliary_loss
       UpperCamelCase			:      Dict 			=     no_object_weight
       UpperCamelCase			:      Tuple 			=     output_auxiliary_logits

       UpperCamelCase			:      Union[str, Any] 			=     self.decoder_config.encoder_attention_heads
       UpperCamelCase			:      List[Any] 			=     self.decoder_config.num_hidden_layers
       super().__init__(**A_     )





      @classmethod
      def  __UpperCamelCase(				cls ,							A_ ,							A_ ,							**A_     ):
       '''simple docstring'''




       return cls(
           backbone_config=A_ ,							decoder_config=A_ ,							**A_ ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     copy.deepcopy(self.__dict__     )
       UpperCamelCase			:      str 			=     self.backbone_config.to_dict()
       UpperCamelCase			:      str 			=     self.decoder_config.to_dict()
       UpperCamelCase			:      Tuple 			=     self.__class__.model_type
       return output







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCamelCase :  int										=  {
    """configuration_convbert""": ["""CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ConvBertConfig""", """ConvBertOnnxConfig"""],
    """tokenization_convbert""": ["""ConvBertTokenizer"""],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  Dict										=  ["""ConvBertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  int										=  [
        """CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """ConvBertForMaskedLM""",
        """ConvBertForMultipleChoice""",
        """ConvBertForQuestionAnswering""",
        """ConvBertForSequenceClassification""",
        """ConvBertForTokenClassification""",
        """ConvBertLayer""",
        """ConvBertModel""",
        """ConvBertPreTrainedModel""",
        """load_tf_weights_in_convbert""",
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowerCamelCase :  str										=  [
        """TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFConvBertForMaskedLM""",
        """TFConvBertForMultipleChoice""",
        """TFConvBertForQuestionAnswering""",
        """TFConvBertForSequenceClassification""",
        """TFConvBertForTokenClassification""",
        """TFConvBertLayer""",
        """TFConvBertModel""",
        """TFConvBertPreTrainedModel""",
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

    __lowerCamelCase :  List[str]										=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
