'''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


SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Any  ={
    '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      ( UpperCamelCase__	):
    a__       :						int          =  """detr"""
    a__       :						List[Any]          =  ["""past_key_values"""]
    a__       :						Tuple          =  {
        """hidden_size""": """d_model""",
        """num_attention_heads""": """encoder_attention_heads""",
    }



    def __init__(self    :    Dict   ,			__a    :    List[str]=True   ,			__a    :    str=None   ,			__a    :    Tuple=3   ,			__a    :    List[Any]=100   ,			__a    :    List[Any]=6   ,			__a    :    int=2048   ,			__a    :    Dict=8   ,			__a    :    List[Any]=6   ,			__a    :    Optional[int]=2048   ,			__a    :    Tuple=8   ,			__a    :    int=0.0   ,			__a    :    Union[str, Any]=0.0   ,			__a    :    str=True   ,			__a    :    Optional[int]="relu"   ,			__a    :    Optional[int]=256   ,			__a    :    int=0.1   ,			__a    :    Union[str, Any]=0.0   ,			__a    :    Optional[Any]=0.0   ,			__a    :    List[Any]=0.02   ,			__a    :    Union[str, Any]=1.0   ,			__a    :    Optional[int]=False   ,			__a    :    str="sine"   ,			__a    :    Any="resnet50"   ,			__a    :    Optional[int]=True   ,			__a    :    List[str]=False   ,			__a    :    Dict=1   ,			__a    :    List[Any]=5   ,			__a    :    Union[str, Any]=2   ,			__a    :    int=1   ,			__a    :    Optional[int]=1   ,			__a    :    Optional[int]=5   ,			__a    :    Optional[int]=2   ,			__a    :    List[str]=0.1   ,			**__a    :    List[str]   ,			):
         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." )
                   UpperCAmelCase_           =   CONFIG_MAPPING["resnet"](out_features=["stage4"] )
              elif isinstance(__a   ,			__a ):
                   UpperCAmelCase_           =   backbone_config.get("model_type" )
                   UpperCAmelCase_           =   CONFIG_MAPPING[backbone_model_type]
                   UpperCAmelCase_           =   config_class.from_dict(__a )
              # set timm attributes to None
              UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   None, None, None

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



    @property
    def 					_lowercase						(self    :    Tuple ):
         return self.encoder_attention_heads



    @property
    def 					_lowercase						(self    :    Optional[int] ):
         return self.d_model



    @classmethod
    def 					_lowercase						(cls    :    Optional[int]   ,			__a    :    PretrainedConfig   ,			**__a    :    Tuple ):
         return cls(backbone_config=__a   ,			**__a )






    def 					_lowercase						(self    :    Optional[Any] ):
         UpperCAmelCase_           =   copy.deepcopy(self.__dict__ )
         if output["backbone_config"] is not None:
              UpperCAmelCase_           =   self.backbone_config.to_dict()
         UpperCAmelCase_           =   self.__class__.model_type
         return output




class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  version.parse("""1.11"""	)



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



    @property
    def 					_lowercase						(self    :    List[str] ):
         return 1E-5






    @property
    def 					_lowercase						(self    :    Dict ):
         return 12







from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






