




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


SCREAMING_SNAKE_CASE_	     =       {
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       ['MaskFormerFeatureExtractor']
  SCREAMING_SNAKE_CASE_	     =       ['MaskFormerImageProcessor']


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE_	     =       [
      'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
      'MaskFormerForInstanceSegmentation',
      'MaskFormerModel',
      'MaskFormerPreTrainedModel',
  ]
  SCREAMING_SNAKE_CASE_	     =       [
      'MaskFormerSwinBackbone',
      'MaskFormerSwinModel',
      'MaskFormerSwinPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
  from .configuration_maskformer_swin import MaskFormerSwinConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_maskformer import MaskFormerFeatureExtractor
    from .image_processing_maskformer import MaskFormerImageProcessor
  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_maskformer import (
        MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        MaskFormerForInstanceSegmentation,
        MaskFormerModel,
        MaskFormerPreTrainedModel,
    )
    from .modeling_maskformer_swin import (
        MaskFormerSwinBackbone,
        MaskFormerSwinModel,
        MaskFormerSwinPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE_	     =       _LazyModule(__name__, globals()['__file__'], _import_structure)




"""simple docstring"""




import requests

_lowerCAmelCase							:   List[Any]				=				"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  None:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       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>")
