





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


__lowerCamelCase :  Any										=  logging.get_logger(__name__)

__lowerCamelCase :  int										=  {
    """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__     ( __snake_case    ):
      _UpperCAmelCase			:int							=     'detr'
      _UpperCAmelCase			:List[Any]							=     ['past_key_values']
      _UpperCAmelCase			:Optional[int]							=     {
          '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_=0.02 ,							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_ ,							):
       '''simple docstring'''




       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			:      Optional[int] 			=     CONFIG_MAPPING["resnet"](out_features=["stage4"]     )
        elif isinstance(A_ ,							A_     ):
         UpperCamelCase			:      Tuple 			=     backbone_config.get("model_type"     )
         UpperCamelCase			:      Optional[int] 			=     CONFIG_MAPPING[backbone_model_type]
         UpperCamelCase			:      List[str] 			=     config_class.from_dict(A_     )
        # set timm attributes to None
        UpperCamelCase			:      Optional[Any] 			=     None, None, None

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





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.encoder_attention_heads





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.d_model





      @classmethod
      def  __UpperCamelCase(				cls ,							A_ ,							**A_     ):
       '''simple docstring'''




       return cls(backbone_config=A_ ,							**A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     copy.deepcopy(self.__dict__     )
       if output["backbone_config"] is not None:
        UpperCamelCase			:      Tuple 			=     self.backbone_config.to_dict()
       UpperCamelCase			:      int 			=     self.__class__.model_type
       return output



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:List[Any]							=     version.parse('1.11'    )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 1e-5





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return 12









from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase :  Tuple										=  logging.get_logger(__name__)

__lowerCamelCase :  str										=  {
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     'camembert'





      def __init__(				self ,							A_=3_0522 ,							A_=768 ,							A_=12 ,							A_=12 ,							A_=3072 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=512 ,							A_=2 ,							A_=0.02 ,							A_=1e-12 ,							A_=1 ,							A_=0 ,							A_=2 ,							A_="absolute" ,							A_=True ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




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

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



class       A__     ( __snake_case    ):





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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