





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


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

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





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = """conditional_detr"""
   lowerCAmelCase__        = ["""past_key_values"""]
   lowerCAmelCase__        = {
       """hidden_size""": """d_model""",
       """num_attention_heads""": """encoder_attention_heads""",
   }




   def __init__(self	:Any    ,   _UpperCamelCase	:List[Any]=True    ,   _UpperCamelCase	:List[Any]=None    ,   _UpperCamelCase	:Tuple=3    ,   _UpperCamelCase	:int=300    ,   _UpperCamelCase	:Union[str, Any]=6    ,   _UpperCamelCase	:Optional[int]=2048    ,   _UpperCamelCase	:List[Any]=8    ,   _UpperCamelCase	:Union[str, Any]=6    ,   _UpperCamelCase	:str=2048    ,   _UpperCamelCase	:Optional[Any]=8    ,   _UpperCamelCase	:Tuple=0.0    ,   _UpperCamelCase	:int=0.0    ,   _UpperCamelCase	:int=True    ,   _UpperCamelCase	:Union[str, Any]="relu"    ,   _UpperCamelCase	:str=256    ,   _UpperCamelCase	:Optional[int]=0.1    ,   _UpperCamelCase	:Tuple=0.0    ,   _UpperCamelCase	:List[Any]=0.0    ,   _UpperCamelCase	:int=0.0_2    ,   _UpperCamelCase	:Any=1.0    ,   _UpperCamelCase	:Dict=False    ,   _UpperCamelCase	:Optional[Any]="sine"    ,   _UpperCamelCase	:List[str]="resnet50"    ,   _UpperCamelCase	:Tuple=True    ,   _UpperCamelCase	:Tuple=False    ,   _UpperCamelCase	:List[str]=2    ,   _UpperCamelCase	:Optional[Any]=5    ,   _UpperCamelCase	:Union[str, Any]=2    ,   _UpperCamelCase	:Optional[int]=1    ,   _UpperCamelCase	:List[str]=1    ,   _UpperCamelCase	:int=2    ,   _UpperCamelCase	:Union[str, Any]=5    ,   _UpperCamelCase	:Optional[int]=2    ,   _UpperCamelCase	:Optional[int]=0.2_5    ,   **_UpperCamelCase	:Tuple    ,   )->					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.''' )
                     __A          =				CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
               elif isinstance(_UpperCamelCase    ,   _UpperCamelCase ):
                     __A          =				backbone_config.get('''model_type''' )
                     __A          =				CONFIG_MAPPING[backbone_model_type]
                     __A          =				config_class.from_dict(_UpperCamelCase )

         __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=_UpperCamelCase    ,   **_UpperCamelCase )




   @property
   def _lowerCAmelCase      (self	:Optional[int] )->					int:
         return self.encoder_attention_heads




   @property
   def _lowerCAmelCase      (self	:int )->					int:
         return self.d_model






   def _lowerCAmelCase      (self	:List[Any] )->					Union[str, Any]:
         __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 A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = version.parse("""1.11"""   )




   @property
   def _lowerCAmelCase      (self	:int )->					Mapping[str, Mapping[int, str]]:
         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                 ('''pixel_mask''', {0: '''batch'''}),
             ] )




   @property
   def _lowerCAmelCase      (self	:Optional[Any] )->					float:
         return 1e-5






   @property
   def _lowerCAmelCase      (self	:List[str] )->					int:
         return 12












import requests

snake_case__					:							int													=     'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='



def _a				(	lowerCamelCase:   str )	->						None:







      '''simple docstring'''






      __A          =				requests.get(_NEWS_API + bbc_news_api_key ).json()
      # each article in the list is a dict
      for i, article in enumerate(bbc_news_page['''articles''']					,				1 ):
            print(F"""{i}.) {article['title']}""" )


if __name__ == "__main__":
       fetch_bbc_news(bbc_news_api_key='<Your BBC News API key goes here>')





