





import copy

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


_UpperCAmelCase				:       int	   =				logging.get_logger(__name__)

_UpperCAmelCase				:       str	   =				{
    """SenseTime/deformable-detr""": """https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json""",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}

class    lowerCAmelCase							(					__UpperCamelCase			):
 UpperCAmelCase__													=		"""deformable_detr"""
 UpperCAmelCase__													=		{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(      self  :      str			,     UpperCAmelCase  :      Any=True			,     UpperCAmelCase  :      str=None			,     UpperCAmelCase  :      Dict=3			,     UpperCAmelCase  :      Union[str, Any]=300			,     UpperCAmelCase  :      Dict=1024			,     UpperCAmelCase  :      Optional[int]=6			,     UpperCAmelCase  :      Optional[int]=1024			,     UpperCAmelCase  :      Optional[int]=8			,     UpperCAmelCase  :      List[str]=6			,     UpperCAmelCase  :      int=1024			,     UpperCAmelCase  :      str=8			,     UpperCAmelCase  :      Optional[int]=0.0			,     UpperCAmelCase  :      str=True			,     UpperCAmelCase  :      List[Any]="relu"			,     UpperCAmelCase  :      str=256			,     UpperCAmelCase  :      Dict=0.1			,     UpperCAmelCase  :      List[Any]=0.0			,     UpperCAmelCase  :      Optional[Any]=0.0			,     UpperCAmelCase  :      List[str]=0.0_2			,     UpperCAmelCase  :      int=1.0			,     UpperCAmelCase  :      int=True			,     UpperCAmelCase  :      List[str]=False			,     UpperCAmelCase  :      str="sine"			,     UpperCAmelCase  :      int="resnet50"			,     UpperCAmelCase  :      Any=True			,     UpperCAmelCase  :      int=False			,     UpperCAmelCase  :      Union[str, Any]=4			,     UpperCAmelCase  :      Union[str, Any]=4			,     UpperCAmelCase  :      Tuple=4			,     UpperCAmelCase  :      Any=False			,     UpperCAmelCase  :      Optional[int]=300			,     UpperCAmelCase  :      List[str]=False			,     UpperCAmelCase  :      int=1			,     UpperCAmelCase  :      Optional[Any]=5			,     UpperCAmelCase  :      Optional[int]=2			,     UpperCAmelCase  :      Union[str, Any]=1			,     UpperCAmelCase  :      Optional[Any]=1			,     UpperCAmelCase  :      List[str]=5			,     UpperCAmelCase  :      Dict=2			,     UpperCAmelCase  :      Any=0.1			,     UpperCAmelCase  :      List[Any]=0.2_5			,     UpperCAmelCase  :      Dict=False			,     **UpperCAmelCase  :      Optional[Any]			,     )			->	int:
     if backbone_config is not None and use_timm_backbone:
         raise ValueError('You can\'t specify both `backbone_config` and `use_timm_backbone`.'	)

     if not use_timm_backbone:
         if backbone_config is None:
             logger.info('`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'	)
             lowerCamelCase__    :      Optional[Any]					 =		CONFIG_MAPPING['resnet'](out_features=['stage4']	)
         elif isinstance(UpperCAmelCase			,     UpperCAmelCase	):
             lowerCamelCase__    :      Optional[int]					 =		backbone_config.get('model_type'	)
             lowerCamelCase__    :      int					 =		CONFIG_MAPPING[backbone_model_type]
             lowerCamelCase__    :      Union[str, Any]					 =		config_class.from_dict(UpperCAmelCase	)
     lowerCamelCase__    :      Optional[Any]					 =		use_timm_backbone
     lowerCamelCase__    :      List[Any]					 =		backbone_config
     lowerCamelCase__    :      Union[str, Any]					 =		num_channels
     lowerCamelCase__    :      str					 =		num_queries
     lowerCamelCase__    :      Any					 =		max_position_embeddings
     lowerCamelCase__    :      Optional[Any]					 =		d_model
     lowerCamelCase__    :      List[str]					 =		encoder_ffn_dim
     lowerCamelCase__    :      Optional[Any]					 =		encoder_layers
     lowerCamelCase__    :      Tuple					 =		encoder_attention_heads
     lowerCamelCase__    :      Optional[int]					 =		decoder_ffn_dim
     lowerCamelCase__    :      List[Any]					 =		decoder_layers
     lowerCamelCase__    :      Optional[Any]					 =		decoder_attention_heads
     lowerCamelCase__    :      str					 =		dropout
     lowerCamelCase__    :      Any					 =		attention_dropout
     lowerCamelCase__    :      Any					 =		activation_dropout
     lowerCamelCase__    :      Tuple					 =		activation_function
     lowerCamelCase__    :      Union[str, Any]					 =		init_std
     lowerCamelCase__    :      str					 =		init_xavier_std
     lowerCamelCase__    :      Optional[int]					 =		encoder_layerdrop
     lowerCamelCase__    :      Union[str, Any]					 =		auxiliary_loss
     lowerCamelCase__    :      List[Any]					 =		position_embedding_type
     lowerCamelCase__    :      List[Any]					 =		backbone
     lowerCamelCase__    :      int					 =		use_pretrained_backbone
     lowerCamelCase__    :      List[str]					 =		dilation
     # deformable attributes
     lowerCamelCase__    :      Any					 =		num_feature_levels
     lowerCamelCase__    :      Optional[int]					 =		encoder_n_points
     lowerCamelCase__    :      Union[str, Any]					 =		decoder_n_points
     lowerCamelCase__    :      Union[str, Any]					 =		two_stage
     lowerCamelCase__    :      str					 =		two_stage_num_proposals
     lowerCamelCase__    :      Union[str, Any]					 =		with_box_refine
     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
     lowerCamelCase__    :      int					 =		class_cost
     lowerCamelCase__    :      str					 =		bbox_cost
     lowerCamelCase__    :      Optional[int]					 =		giou_cost
     # Loss coefficients
     lowerCamelCase__    :      int					 =		mask_loss_coefficient
     lowerCamelCase__    :      int					 =		dice_loss_coefficient
     lowerCamelCase__    :      Tuple					 =		bbox_loss_coefficient
     lowerCamelCase__    :      Union[str, Any]					 =		giou_loss_coefficient
     lowerCamelCase__    :      Any					 =		eos_coefficient
     lowerCamelCase__    :      Any					 =		focal_alpha
     lowerCamelCase__    :      Tuple					 =		disable_custom_kernels
     super().__init__(is_encoder_decoder=UpperCAmelCase			,     **UpperCAmelCase	)
 @property
 def      A_	(      self  :      str	)			->	int:
     return self.encoder_attention_heads
 @property
 def      A_	(      self  :      str	)			->	int:
     return self.d_model







 def      A_	(      self  :      Optional[Any]	)			->	List[Any]:
     lowerCamelCase__    :      int					 =		copy.deepcopy(self.__dict__	)
     if self.backbone_config is not None:
         lowerCamelCase__    :      Union[str, Any]					 =		self.backbone_config.to_dict()
     lowerCamelCase__    :      Dict					 =		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


a_ =			logging.get_logger(__name__)

a_ =			{
    """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 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """roberta"""

 def __init__(    self					,						__lowerCamelCase=5_0265					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=1					,						__lowerCamelCase=0					,						__lowerCamelCase=2					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						bos_token_id=__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

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






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						List[Any]						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Optional[Any]						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]  )




