'''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





'''simple docstring'''





from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest




@require_tokenizers
class        __A      ( UpperCamelCase__	):
    a__       :						Optional[Any]          =  DistilBertTokenizer
    a__       :						Any          =  DistilBertTokenizerFast
    a__       :						str          =  True



    @slow
    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   DistilBertTokenizer.from_pretrained("distilbert-base-uncased" )

         UpperCAmelCase_           =   tokenizer.encode("sequence builders"   ,			add_special_tokens=__a )
         UpperCAmelCase_           =   tokenizer.encode("multi-sequence build"   ,			add_special_tokens=__a )

         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a )
         UpperCAmelCase_           =   tokenizer.build_inputs_with_special_tokens(__a   ,			__a )

         assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
         assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
             tokenizer.sep_token_id
         ]




