

'''simple docstring'''

from typing import TYPE_CHECKING

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


lowercase__         =      {
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RobertaPreLayerNormForCausalLM",
     "RobertaPreLayerNormForMaskedLM",
     "RobertaPreLayerNormForMultipleChoice",
     "RobertaPreLayerNormForQuestionAnswering",
     "RobertaPreLayerNormForSequenceClassification",
     "RobertaPreLayerNormForTokenClassification",
     "RobertaPreLayerNormModel",
     "RobertaPreLayerNormPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRobertaPreLayerNormForCausalLM",
     "TFRobertaPreLayerNormForMaskedLM",
     "TFRobertaPreLayerNormForMultipleChoice",
     "TFRobertaPreLayerNormForQuestionAnswering",
     "TFRobertaPreLayerNormForSequenceClassification",
     "TFRobertaPreLayerNormForTokenClassification",
     "TFRobertaPreLayerNormMainLayer",
     "TFRobertaPreLayerNormModel",
     "TFRobertaPreLayerNormPreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowercase__         =      [
     "FlaxRobertaPreLayerNormForCausalLM",
     "FlaxRobertaPreLayerNormForMaskedLM",
     "FlaxRobertaPreLayerNormForMultipleChoice",
     "FlaxRobertaPreLayerNormForQuestionAnswering",
     "FlaxRobertaPreLayerNormForSequenceClassification",
     "FlaxRobertaPreLayerNormForTokenClassification",
     "FlaxRobertaPreLayerNormModel",
     "FlaxRobertaPreLayerNormPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roberta_prelayernorm import (
     ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
     RobertaPreLayerNormConfig,
     RobertaPreLayerNormOnnxConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roberta_prelayernorm import (
      ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      RobertaPreLayerNormForCausalLM,
      RobertaPreLayerNormForMaskedLM,
      RobertaPreLayerNormForMultipleChoice,
      RobertaPreLayerNormForQuestionAnswering,
      RobertaPreLayerNormForSequenceClassification,
      RobertaPreLayerNormForTokenClassification,
      RobertaPreLayerNormModel,
      RobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roberta_prelayernorm import (
      TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRobertaPreLayerNormForCausalLM,
      TFRobertaPreLayerNormForMaskedLM,
      TFRobertaPreLayerNormForMultipleChoice,
      TFRobertaPreLayerNormForQuestionAnswering,
      TFRobertaPreLayerNormForSequenceClassification,
      TFRobertaPreLayerNormForTokenClassification,
      TFRobertaPreLayerNormMainLayer,
      TFRobertaPreLayerNormModel,
      TFRobertaPreLayerNormPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
      FlaxRobertaPreLayerNormPreTrainedModel,
  )

else:
 import sys

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







'''simple docstring'''

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


lowercase__         =      {
    "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
}

lowercase__         =      logging.get_logger(__name__)




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Any  		=  """maskformer"""
     UpperCAmelCase_						:     Union[str, Any]  		=  {"""hidden_size""": """mask_feature_size"""}
     UpperCAmelCase_						:     Union[str, Any]  		=  ["""resnet""", """swin"""]
     UpperCAmelCase_						:     Optional[int]  		=  ["""detr"""]


     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					int = 256					,      lowercase_			:					int = 256					,      lowercase_			:					float = 0.1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					Optional[Dict] = None					,      lowercase_			:					float = 0.02					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 1.0					,      lowercase_			:					float = 20.0					,      lowercase_			:					Optional[bool] = None					,      **lowercase_			:					int					,      )						->     Optional[Any]:
      if backbone_config is None:
       # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
       UpperCAmelCase	:     List[Any]						 =			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(lowercase_					,      lowercase_	):
       UpperCAmelCase	:     List[Any]						 =			backbone_config.pop('model_type'	)
       UpperCAmelCase	:     Optional[Any]						 =			CONFIG_MAPPING[backbone_model_type]
       UpperCAmelCase	:     List[Any]						 =			config_class.from_dict(lowercase_	)

      # 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	:     Dict						 =			DetrConfig()
      else:
       # verify that the decoder is supported
       UpperCAmelCase	:     Tuple						 =			(
           decoder_config.pop('model_type'	) if isinstance(lowercase_					,      lowercase_	) 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(lowercase_					,      lowercase_	):
        UpperCAmelCase	:     Union[str, Any]						 =			CONFIG_MAPPING[decoder_type]
        UpperCAmelCase	:     str						 =			config_class.from_dict(lowercase_	)

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

      UpperCAmelCase	:     Dict						 =			self.decoder_config.encoder_attention_heads
      UpperCAmelCase	:     Union[str, Any]						 =			self.decoder_config.num_hidden_layers
      super().__init__(**lowercase_	)


     @classmethod
     def        UpperCAmelCase_	(		cls			:					Any					,      lowercase_			:					PretrainedConfig					,      lowercase_			:					PretrainedConfig					,      **lowercase_			:					Optional[Any]	)						->     str:
      return cls(
          backbone_config=lowercase_					,      decoder_config=lowercase_					,      **lowercase_					,      )




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




