"""simple docstring"""






from typing import TYPE_CHECKING

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


__A			   = {
    "configuration_squeezebert": [
        "SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SqueezeBertConfig",
        "SqueezeBertOnnxConfig",
    ],
    "tokenization_squeezebert": ["SqueezeBertTokenizer"],
}

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["SqueezeBertTokenizerFast"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
           "SqueezeBertForMaskedLM",
           "SqueezeBertForMultipleChoice",
           "SqueezeBertForQuestionAnswering",
           "SqueezeBertForSequenceClassification",
           "SqueezeBertForTokenClassification",
           "SqueezeBertModel",
           "SqueezeBertModule",
           "SqueezeBertPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_squeezebert import (
           SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SqueezeBertConfig,
           SqueezeBertOnnxConfig,
       )
       from .tokenization_squeezebert import SqueezeBertTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_squeezebert import (
                  SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SqueezeBertForMaskedLM,
                  SqueezeBertForMultipleChoice,
                  SqueezeBertForQuestionAnswering,
                  SqueezeBertForSequenceClassification,
                  SqueezeBertForTokenClassification,
                  SqueezeBertModel,
                  SqueezeBertModule,
                  SqueezeBertPreTrainedModel,
              )

else:
       import sys

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

"""simple docstring"""






import copy

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


__A			   = logging.get_logger(__name__)

__A			   = {
    "ut/deta": "https://huggingface.co/ut/deta/resolve/main/config.json",
}


class     UpperCAmelCase						(_UpperCAmelCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = "deta"
    _UpperCAmelCase	:Any						    = {
        "hidden_size": "d_model",
        "num_attention_heads": "encoder_attention_heads",
    }






    def __init__(   self    ,			_UpperCAmelCase=None    ,			_UpperCAmelCase=900    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=2048    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=6    ,			_UpperCAmelCase=1024    ,			_UpperCAmelCase=8    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase="relu"    ,			_UpperCAmelCase=256    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.0    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=1.0    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase="sine"    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=300    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.25    ,			**_UpperCAmelCase    ,			):
       if backbone_config is None:
          logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
          lowercase__:   str					=      CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']							)
       else:
          if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
             lowercase__:   Tuple					=      backbone_config.pop('''model_type'''							)
             lowercase__:   str					=      CONFIG_MAPPING[backbone_model_type]
             lowercase__:   Optional[int]					=      config_class.from_dict(_UpperCAmelCase							)

       lowercase__:   int					=      backbone_config
       lowercase__:   Any					=      num_queries
       lowercase__:   List[str]					=      max_position_embeddings
       lowercase__:   Optional[Any]					=      d_model
       lowercase__:   List[Any]					=      encoder_ffn_dim
       lowercase__:   Tuple					=      encoder_layers
       lowercase__:   Dict					=      encoder_attention_heads
       lowercase__:   Any					=      decoder_ffn_dim
       lowercase__:   Union[str, Any]					=      decoder_layers
       lowercase__:   List[Any]					=      decoder_attention_heads
       lowercase__:   int					=      dropout
       lowercase__:   List[str]					=      attention_dropout
       lowercase__:   Tuple					=      activation_dropout
       lowercase__:   Tuple					=      activation_function
       lowercase__:   int					=      init_std
       lowercase__:   Optional[Any]					=      init_xavier_std
       lowercase__:   Optional[Any]					=      encoder_layerdrop
       lowercase__:   Optional[int]					=      auxiliary_loss
       lowercase__:   Union[str, Any]					=      position_embedding_type
       # deformable attributes
       lowercase__:   List[str]					=      num_feature_levels
       lowercase__:   Optional[Any]					=      encoder_n_points
       lowercase__:   int					=      decoder_n_points
       lowercase__:   str					=      two_stage
       lowercase__:   Optional[int]					=      two_stage_num_proposals
       lowercase__:   Tuple					=      with_box_refine
       lowercase__:   str					=      assign_first_stage
       if two_stage is True and with_box_refine is False:
          raise ValueError('''If two_stage is True, with_box_refine must be True.'''							)
       # Hungarian matcher
       lowercase__:   Union[str, Any]					=      class_cost
       lowercase__:   Optional[int]					=      bbox_cost
       lowercase__:   int					=      giou_cost
       # Loss coefficients
       lowercase__:   Optional[int]					=      mask_loss_coefficient
       lowercase__:   List[str]					=      dice_loss_coefficient
       lowercase__:   str					=      bbox_loss_coefficient
       lowercase__:   Union[str, Any]					=      giou_loss_coefficient
       lowercase__:   Optional[int]					=      eos_coefficient
       lowercase__:   str					=      focal_alpha
       super().__init__(is_encoder_decoder=_UpperCAmelCase    ,			**_UpperCAmelCase							)






    @property
    def _snake_case			(   self							):
       return self.encoder_attention_heads






    @property
    def _snake_case			(   self							):
       return self.d_model






    def _snake_case			(   self							):
       lowercase__:   Union[str, Any]					=      copy.deepcopy(self.__dict__							)
       lowercase__:   Dict					=      self.backbone_config.to_dict()
       lowercase__:   Union[str, Any]					=      self.__class__.model_type
       return output
