"""simple docstring"""




import copy
from collections import OrderedDict
from typing import Dict, 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[int]	   =       logging.get_logger(__name__)

_a				:		Dict	   =       {
    'facebook/detr-resnet-50': 'https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json',
    # See all DETR models at https://huggingface.co/models?filter=detr
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[Any]		 =		"detr"
     _UpperCamelCase							:      Union[str, Any]		 =		["past_key_values"]
     _UpperCamelCase							:      Any		 =		{
         "hidden_size": "d_model",
         "num_attention_heads": "encoder_attention_heads",
     }






     def __init__(		self    ,					a__=True    ,					a__=None    ,					a__=3    ,					a__=100    ,					a__=6    ,					a__=2048    ,					a__=8    ,					a__=6    ,					a__=2048    ,					a__=8    ,					a__=0.0    ,					a__=0.0    ,					a__=True    ,					a__="relu"    ,					a__=256    ,					a__=0.1    ,					a__=0.0    ,					a__=0.0    ,					a__=0.0_2    ,					a__=1.0    ,					a__=False    ,					a__="sine"    ,					a__="resnet50"    ,					a__=True    ,					a__=False    ,					a__=1    ,					a__=5    ,					a__=2    ,					a__=1    ,					a__=1    ,					a__=5    ,					a__=2    ,					a__=0.1    ,					**a__    ,					):
       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						:	Dict        =					CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""]  )
         elif isinstance(a__    ,					a__  ):
           _lowerCAmelCase						:	str        =					backbone_config.get("""model_type"""  )
           _lowerCAmelCase						:	Any        =					CONFIG_MAPPING[backbone_model_type]
           _lowerCAmelCase						:	Union[str, Any]        =					config_class.from_dict(a__  )
         # set timm attributes to None
         _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Dict        =					None, None, None

       _lowerCAmelCase						:	Optional[int]        =					use_timm_backbone
       _lowerCAmelCase						:	Union[str, Any]        =					backbone_config
       _lowerCAmelCase						:	Tuple        =					num_channels
       _lowerCAmelCase						:	List[str]        =					num_queries
       _lowerCAmelCase						:	str        =					d_model
       _lowerCAmelCase						:	int        =					encoder_ffn_dim
       _lowerCAmelCase						:	Union[str, Any]        =					encoder_layers
       _lowerCAmelCase						:	List[Any]        =					encoder_attention_heads
       _lowerCAmelCase						:	Optional[Any]        =					decoder_ffn_dim
       _lowerCAmelCase						:	Tuple        =					decoder_layers
       _lowerCAmelCase						:	Dict        =					decoder_attention_heads
       _lowerCAmelCase						:	Optional[Any]        =					dropout
       _lowerCAmelCase						:	str        =					attention_dropout
       _lowerCAmelCase						:	Optional[int]        =					activation_dropout
       _lowerCAmelCase						:	Dict        =					activation_function
       _lowerCAmelCase						:	Any        =					init_std
       _lowerCAmelCase						:	Dict        =					init_xavier_std
       _lowerCAmelCase						:	int        =					encoder_layerdrop
       _lowerCAmelCase						:	int        =					decoder_layerdrop
       _lowerCAmelCase						:	Tuple        =					encoder_layers
       _lowerCAmelCase						:	List[Any]        =					auxiliary_loss
       _lowerCAmelCase						:	Optional[int]        =					position_embedding_type
       _lowerCAmelCase						:	List[str]        =					backbone
       _lowerCAmelCase						:	str        =					use_pretrained_backbone
       _lowerCAmelCase						:	Any        =					dilation
       # Hungarian matcher
       _lowerCAmelCase						:	List[str]        =					class_cost
       _lowerCAmelCase						:	Union[str, Any]        =					bbox_cost
       _lowerCAmelCase						:	List[str]        =					giou_cost
       # Loss coefficients
       _lowerCAmelCase						:	List[Any]        =					mask_loss_coefficient
       _lowerCAmelCase						:	Optional[int]        =					dice_loss_coefficient
       _lowerCAmelCase						:	int        =					bbox_loss_coefficient
       _lowerCAmelCase						:	Dict        =					giou_loss_coefficient
       _lowerCAmelCase						:	str        =					eos_coefficient
       super().__init__(is_encoder_decoder=a__    ,					**a__  )






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






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






     @classmethod
     def    __A  (		cls    ,					a__    ,					**a__  ):
       return cls(backbone_config=a__    ,					**a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					copy.deepcopy(self.__dict__  )
       if output["backbone_config"] is not None:
         _lowerCAmelCase						:	Dict        =					self.backbone_config.to_dict()
       _lowerCAmelCase						:	Any        =					self.__class__.model_type
       return output







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		version.parse("1.11"		)






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






     @property
     def    __A  (		self  ):
       return 1e-5






     @property
     def    __A  (		self  ):
       return 12





"""simple docstring"""




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


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"visual_bert"






     def __init__(		self    ,					a__=30522    ,					a__=768    ,					a__=512    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=2    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=False    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )

       _lowerCAmelCase						:	Optional[Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	Optional[int]        =					visual_embedding_dim
       _lowerCAmelCase						:	Optional[int]        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					intermediate_size
       _lowerCAmelCase						:	List[Any]        =					hidden_act
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Optional[Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					type_vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Optional[Any]        =					bypass_transformer
       _lowerCAmelCase						:	List[Any]        =					special_visual_initialize




