


from ....configuration_utils import PretrainedConfig
from ....utils import logging


A				:       List[Any]      =  logging.get_logger(__name__)

# TODO: upload to AWS
A				:       Optional[Any]      =  {
    'yjernite/retribert-base-uncased': (
        'https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json'
    ),
}






class      UpperCamelCase(	UpperCamelCase__ ):
					snake_case_		:					List[str]    					=						"""retribert"""


					def __init__(    self						:		Dict  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=3_0_5_2_2  ,     SCREAMING_SNAKE_CASE						:		Dict=7_6_8  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=8  ,     SCREAMING_SNAKE_CASE						:		int=1_2  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=3_0_7_2  ,     SCREAMING_SNAKE_CASE						:		Tuple="gelu"  ,     SCREAMING_SNAKE_CASE						:		List[Any]=0.1  ,     SCREAMING_SNAKE_CASE						:		Tuple=0.1  ,     SCREAMING_SNAKE_CASE						:		Optional[int]=5_1_2  ,     SCREAMING_SNAKE_CASE						:		str=2  ,     SCREAMING_SNAKE_CASE						:		Dict=0.02  ,     SCREAMING_SNAKE_CASE						:		Union[str, Any]=1e-1_2  ,     SCREAMING_SNAKE_CASE						:		Optional[Any]=True  ,     SCREAMING_SNAKE_CASE						:		str=1_2_8  ,     SCREAMING_SNAKE_CASE						:		str=0  ,     **SCREAMING_SNAKE_CASE						:		Optional[int]  ,     )      ->						Optional[Any]:




											'''simple docstring'''

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

											__snake_case				=		vocab_size
											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		hidden_act
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		max_position_embeddings
											__snake_case				=		type_vocab_size
											__snake_case				=		initializer_range
											__snake_case				=		layer_norm_eps
											__snake_case				=		share_encoders
											__snake_case				=		projection_dim












import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

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


a__: Optional[Any]							   =						logging.get_logger(__name__)

a__: Any							   =						{
    'microsoft/conditional-detr-resnet-50': (
        'https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json'
    ),
}




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      '''conditional_detr'''
 __SCREAMING_SNAKE_CASE							=      ['''past_key_values''']
 __SCREAMING_SNAKE_CASE							=      {
     '''hidden_size''': '''d_model''',
     '''num_attention_heads''': '''encoder_attention_heads''',
 }



 def __init__(				self,__lowerCamelCase=True,__lowerCamelCase=None,__lowerCamelCase=3,__lowerCamelCase=300,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=6,__lowerCamelCase=2048,__lowerCamelCase=8,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=True,__lowerCamelCase="relu",__lowerCamelCase=256,__lowerCamelCase=0.1,__lowerCamelCase=0.0,__lowerCamelCase=0.0,__lowerCamelCase=0.02,__lowerCamelCase=1.0,__lowerCamelCase=False,__lowerCamelCase="sine",__lowerCamelCase="resnet50",__lowerCamelCase=True,__lowerCamelCase=False,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=1,__lowerCamelCase=1,__lowerCamelCase=2,__lowerCamelCase=5,__lowerCamelCase=2,__lowerCamelCase=0.25,**__lowerCamelCase,):
        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.'''					)
                      A__		   =		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']					)
               elif isinstance(__lowerCamelCase,__lowerCamelCase					):
                      A__		   =		backbone_config.get('''model_type'''					)
                      A__		   =		CONFIG_MAPPING[backbone_model_type]
                      A__		   =		config_class.from_dict(__lowerCamelCase					)

        A__		   =		use_timm_backbone
        A__		   =		backbone_config
        A__		   =		num_channels
        A__		   =		num_queries
        A__		   =		d_model
        A__		   =		encoder_ffn_dim
        A__		   =		encoder_layers
        A__		   =		encoder_attention_heads
        A__		   =		decoder_ffn_dim
        A__		   =		decoder_layers
        A__		   =		decoder_attention_heads
        A__		   =		dropout
        A__		   =		attention_dropout
        A__		   =		activation_dropout
        A__		   =		activation_function
        A__		   =		init_std
        A__		   =		init_xavier_std
        A__		   =		encoder_layerdrop
        A__		   =		decoder_layerdrop
        A__		   =		encoder_layers
        A__		   =		auxiliary_loss
        A__		   =		position_embedding_type
        A__		   =		backbone
        A__		   =		use_pretrained_backbone
        A__		   =		dilation
        # Hungarian matcher
        A__		   =		class_cost
        A__		   =		bbox_cost
        A__		   =		giou_cost
        # Loss coefficients
        A__		   =		mask_loss_coefficient
        A__		   =		dice_loss_coefficient
        A__		   =		cls_loss_coefficient
        A__		   =		bbox_loss_coefficient
        A__		   =		giou_loss_coefficient
        A__		   =		focal_alpha
        super().__init__(is_encoder_decoder=__lowerCamelCase,**__lowerCamelCase					)



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



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




 def 							UpperCamelCase     (				self					):
        A__		   =		copy.deepcopy(self.__dict__					)
        if self.backbone_config is not None:
               A__		   =		self.backbone_config.to_dict()
        A__		   =		self.__class__.model_type
        return output




class        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):
 __SCREAMING_SNAKE_CASE							=      version.parse('''1.11''' )



 @property
 def 							UpperCamelCase     (				self					):
        return OrderedDict(
            [
                ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ('''pixel_mask''', {0: '''batch'''}),
            ]					)



 @property
 def 							UpperCamelCase     (				self					):
        return 1E-5




 @property
 def 							UpperCamelCase     (				self					):
        return 12





