
import copy

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


lowercase__       =logging.get_logger(__name__)

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



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							int	       =      "deta"
   _SCREAMING_SNAKE_CASE      :							Any	       =      {
       "hidden_size": "d_model",
       "num_attention_heads": "encoder_attention_heads",
   }
   def __init__(self :					List[str]					,				snake_case_ :					List[Any]=None					,				snake_case_ :					str=9_0_0					,				snake_case_ :					int=2_0_4_8					,				snake_case_ :					int=6					,				snake_case_ :					Optional[Any]=2_0_4_8					,				snake_case_ :					List[Any]=8					,				snake_case_ :					int=6					,				snake_case_ :					Optional[Any]=1_0_2_4					,				snake_case_ :					Optional[int]=8					,				snake_case_ :					Any=0.0					,				snake_case_ :					Union[str, Any]=True					,				snake_case_ :					Any="relu"					,				snake_case_ :					int=2_5_6					,				snake_case_ :					Dict=0.1					,				snake_case_ :					Union[str, Any]=0.0					,				snake_case_ :					List[str]=0.0					,				snake_case_ :					int=0.02					,				snake_case_ :					Optional[int]=1.0					,				snake_case_ :					Any=True					,				snake_case_ :					Any=False					,				snake_case_ :					Optional[int]="sine"					,				snake_case_ :					str=5					,				snake_case_ :					Dict=4					,				snake_case_ :					Tuple=4					,				snake_case_ :					str=True					,				snake_case_ :					List[Any]=3_0_0					,				snake_case_ :					Union[str, Any]=True					,				snake_case_ :					Tuple=True					,				snake_case_ :					Tuple=1					,				snake_case_ :					str=5					,				snake_case_ :					Optional[Any]=2					,				snake_case_ :					Union[str, Any]=1					,				snake_case_ :					Optional[Any]=1					,				snake_case_ :					List[Any]=5					,				snake_case_ :					Optional[int]=2					,				snake_case_ :					List[str]=0.1					,				snake_case_ :					str=0.25					,				**snake_case_ :					List[Any]					,				):
    if backbone_config is None:
     logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''   )
     __a :  Optional[int]						=  CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']   )
    else:
     if isinstance(snake_case_					,				snake_case_   ):
      __a :  Optional[Any]						=  backbone_config.pop('''model_type'''   )
      __a :  Dict						=  CONFIG_MAPPING[backbone_model_type]
      __a :  List[str]						=  config_class.from_dict(snake_case_   )

    __a :  List[str]						=  backbone_config
    __a :  Tuple						=  num_queries
    __a :  Tuple						=  max_position_embeddings
    __a :  List[str]						=  d_model
    __a :  Optional[int]						=  encoder_ffn_dim
    __a :  Dict						=  encoder_layers
    __a :  Dict						=  encoder_attention_heads
    __a :  Tuple						=  decoder_ffn_dim
    __a :  Dict						=  decoder_layers
    __a :  int						=  decoder_attention_heads
    __a :  List[Any]						=  dropout
    __a :  int						=  attention_dropout
    __a :  List[Any]						=  activation_dropout
    __a :  List[Any]						=  activation_function
    __a :  List[str]						=  init_std
    __a :  str						=  init_xavier_std
    __a :  str						=  encoder_layerdrop
    __a :  Any						=  auxiliary_loss
    __a :  Optional[int]						=  position_embedding_type
    # deformable attributes
    __a :  List[Any]						=  num_feature_levels
    __a :  Tuple						=  encoder_n_points
    __a :  Optional[Any]						=  decoder_n_points
    __a :  int						=  two_stage
    __a :  List[str]						=  two_stage_num_proposals
    __a :  Union[str, Any]						=  with_box_refine
    __a :  Tuple						=  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
    __a :  List[str]						=  class_cost
    __a :  str						=  bbox_cost
    __a :  Any						=  giou_cost
    # Loss coefficients
    __a :  Any						=  mask_loss_coefficient
    __a :  Optional[Any]						=  dice_loss_coefficient
    __a :  Optional[int]						=  bbox_loss_coefficient
    __a :  List[Any]						=  giou_loss_coefficient
    __a :  Optional[Any]						=  eos_coefficient
    __a :  Union[str, Any]						=  focal_alpha
    super().__init__(is_encoder_decoder=snake_case_					,				**snake_case_   )
   @property
   def       lowerCAmelCase			(self :					Tuple   ):
    return self.encoder_attention_heads
   @property
   def       lowerCAmelCase			(self :					Tuple   ):
    return self.d_model







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[Any]						=  copy.deepcopy(self.__dict__   )
    __a :  int						=  self.backbone_config.to_dict()
    __a :  Dict						=  self.__class__.model_type
    return output


from typing import TYPE_CHECKING

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


lowercase__       ={
    'configuration_roformer': ['ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoFormerConfig', 'RoFormerOnnxConfig'],
    'tokenization_roformer': ['RoFormerTokenizer'],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =['RoFormerTokenizerFast']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'RoFormerForCausalLM',
          'RoFormerForMaskedLM',
          'RoFormerForMultipleChoice',
          'RoFormerForQuestionAnswering',
          'RoFormerForSequenceClassification',
          'RoFormerForTokenClassification',
          'RoFormerLayer',
          'RoFormerModel',
          'RoFormerPreTrainedModel',
          'load_tf_weights_in_roformer',
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFRoFormerForCausalLM',
          'TFRoFormerForMaskedLM',
          'TFRoFormerForMultipleChoice',
          'TFRoFormerForQuestionAnswering',
          'TFRoFormerForSequenceClassification',
          'TFRoFormerForTokenClassification',
          'TFRoFormerLayer',
          'TFRoFormerModel',
          'TFRoFormerPreTrainedModel',
      ]


try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase__       =[
          'FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'FlaxRoFormerForMaskedLM',
          'FlaxRoFormerForMultipleChoice',
          'FlaxRoFormerForQuestionAnswering',
          'FlaxRoFormerForSequenceClassification',
          'FlaxRoFormerForTokenClassification',
          'FlaxRoFormerModel',
          'FlaxRoFormerPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
      from .tokenization_roformer import RoFormerTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_roformer_fast import RoFormerTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_roformer import (
                ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                RoFormerForCausalLM,
                RoFormerForMaskedLM,
                RoFormerForMultipleChoice,
                RoFormerForQuestionAnswering,
                RoFormerForSequenceClassification,
                RoFormerForTokenClassification,
                RoFormerLayer,
                RoFormerModel,
                RoFormerPreTrainedModel,
                load_tf_weights_in_roformer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_roformer import (
                TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRoFormerForCausalLM,
                TFRoFormerForMaskedLM,
                TFRoFormerForMultipleChoice,
                TFRoFormerForQuestionAnswering,
                TFRoFormerForSequenceClassification,
                TFRoFormerForTokenClassification,
                TFRoFormerLayer,
                TFRoFormerModel,
                TFRoFormerPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_roformer import (
                FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlaxRoFormerForMaskedLM,
                FlaxRoFormerForMultipleChoice,
                FlaxRoFormerForQuestionAnswering,
                FlaxRoFormerForSequenceClassification,
                FlaxRoFormerForTokenClassification,
                FlaxRoFormerModel,
                FlaxRoFormerPreTrainedModel,
            )


else:
      import sys

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