


'''simple docstring'''
import copy

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


UpperCamelCase__          = logging.get_logger(__name__)

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




class      lowerCamelCase_  (      __a ):
      lowerCAmelCase__							=	'deta'
      lowerCAmelCase__							=	{
          'hidden_size': 'd_model',
          'num_attention_heads': 'encoder_attention_heads',
      }






      def __init__(   self							:       Dict		,		_A							:       str=None		,		_A							:       Any=900		,		_A							:       str=2_048		,		_A							:       str=6		,		_A							:       Optional[Any]=2_048		,		_A							:       int=8		,		_A							:       List[Any]=6		,		_A							:       Optional[Any]=1_024		,		_A							:       Tuple=8		,		_A							:       Optional[int]=0.0		,		_A							:       Optional[Any]=True		,		_A							:       Union[str, Any]="relu"		,		_A							:       List[str]=256		,		_A							:       str=0.1		,		_A							:       Union[str, Any]=0.0		,		_A							:       List[str]=0.0		,		_A							:       int=0.0_2		,		_A							:       Union[str, Any]=1.0		,		_A							:       Dict=True		,		_A							:       Union[str, Any]=False		,		_A							:       Dict="sine"		,		_A							:       Dict=5		,		_A							:       Optional[Any]=4		,		_A							:       Tuple=4		,		_A							:       Optional[Any]=True		,		_A							:       Dict=300		,		_A							:       str=True		,		_A							:       Union[str, Any]=True		,		_A							:       Tuple=1		,		_A							:       List[str]=5		,		_A							:       List[str]=2		,		_A							:       List[Any]=1		,		_A							:       List[Any]=1		,		_A							:       Union[str, Any]=5		,		_A							:       str=2		,		_A							:       Optional[Any]=0.1		,		_A							:       Tuple=0.2_5		,		**_A							:       int		,		):


          '''simple docstring'''


          if backbone_config is None:
              logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''					)
              UpperCAmelCase__     :						Optional[int]						=							CONFIG_MAPPING['''resnet'''](out_features=['''stage2''', '''stage3''', '''stage4''']					)
          else:
              if isinstance(_A		,		_A					):
                  UpperCAmelCase__     :						str						=							backbone_config.pop('''model_type'''					)
                  UpperCAmelCase__     :						str						=							CONFIG_MAPPING[backbone_model_type]
                  UpperCAmelCase__     :						Tuple						=							config_class.from_dict(_A					)

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






      @property
      def lowercase_				(   self							:       Any					):


          '''simple docstring'''


          return self.encoder_attention_heads






      @property
      def lowercase_				(   self							:       str					):


          '''simple docstring'''


          return self.d_model






      def lowercase_				(   self							:       Dict					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							copy.deepcopy(self.__dict__					)
          UpperCAmelCase__     :						Tuple						=							self.backbone_config.to_dict()
          UpperCAmelCase__     :						str						=							self.__class__.model_type
          return output





"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowerCAmelCase							:   Optional[int]				=				logging.get_logger(__name__)

_lowerCAmelCase							:   Any				=				{
    "roberta-base": "https://huggingface.co/roberta-base/resolve/main/config.json",
    "roberta-large": "https://huggingface.co/roberta-large/resolve/main/config.json",
    "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/config.json",
    "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/config.json",
    "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json",
    "roberta-large-openai-detector": "https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json",
}






class       UpperCAmelCase_    (							_UpperCamelCase		):
      __SCREAMING_SNAKE_CASE		:  Optional[int]						=					'roberta'



      def __init__(	self	:      str	,      A	:      Optional[int]=5_0_2_6_5	,      A	:      Optional[int]=7_6_8	,      A	:      Tuple=1_2	,      A	:      Optional[Any]=1_2	,      A	:      Optional[int]=3_0_7_2	,      A	:      str="gelu"	,      A	:      List[Any]=0.1	,      A	:      Optional[Any]=0.1	,      A	:      Any=5_1_2	,      A	:      Dict=2	,      A	:      Union[str, Any]=0.02	,      A	:      str=1e-12	,      A	:      Optional[int]=1	,      A	:      List[str]=0	,      A	:      int=2	,      A	:      Any="absolute"	,      A	:      Optional[int]=True	,      A	:      int=None	,      **A	:      str	,      ):
           super().__init__(pad_token_id=A	,      bos_token_id=A	,      eos_token_id=A	,      **A		)

           _UpperCAmelCase     :      str														=       vocab_size
           _UpperCAmelCase     :      Dict														=       hidden_size
           _UpperCAmelCase     :      List[str]														=       num_hidden_layers
           _UpperCAmelCase     :      Any														=       num_attention_heads
           _UpperCAmelCase     :      List[str]														=       hidden_act
           _UpperCAmelCase     :      Any														=       intermediate_size
           _UpperCAmelCase     :      Union[str, Any]														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Tuple														=       max_position_embeddings
           _UpperCAmelCase     :      Union[str, Any]														=       type_vocab_size
           _UpperCAmelCase     :      str														=       initializer_range
           _UpperCAmelCase     :      int														=       layer_norm_eps
           _UpperCAmelCase     :      Tuple														=       position_embedding_type
           _UpperCAmelCase     :      int														=       use_cache
           _UpperCAmelCase     :      Dict														=       classifier_dropout







class       UpperCAmelCase_    (							_UpperCamelCase		):



      @property
      def 		snake_case_   (	self	:      Tuple		):
           if self.task == "multiple-choice":
                _UpperCAmelCase     :      Optional[Any]														=       {0: "batch", 1: "choice", 2: "sequence"}
           else:
                _UpperCAmelCase     :      List[Any]														=       {0: "batch", 1: "sequence"}
           return OrderedDict(
               [
                   ("input_ids", dynamic_axis),
                   ("attention_mask", dynamic_axis),
               ]		)
