


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase      :Dict			    =       {
    "configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Optional[int]			    =       ["AlbertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       ["AlbertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Any			    =       [
          "ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "AlbertForMaskedLM",
          "AlbertForMultipleChoice",
          "AlbertForPreTraining",
          "AlbertForQuestionAnswering",
          "AlbertForSequenceClassification",
          "AlbertForTokenClassification",
          "AlbertModel",
          "AlbertPreTrainedModel",
          "load_tf_weights_in_albert",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       [
          "TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFAlbertForMaskedLM",
          "TFAlbertForMultipleChoice",
          "TFAlbertForPreTraining",
          "TFAlbertForQuestionAnswering",
          "TFAlbertForSequenceClassification",
          "TFAlbertForTokenClassification",
          "TFAlbertMainLayer",
          "TFAlbertModel",
          "TFAlbertPreTrainedModel",
      ]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :int			    =       [
          "FlaxAlbertForMaskedLM",
          "FlaxAlbertForMultipleChoice",
          "FlaxAlbertForPreTraining",
          "FlaxAlbertForQuestionAnswering",
          "FlaxAlbertForSequenceClassification",
          "FlaxAlbertForTokenClassification",
          "FlaxAlbertModel",
          "FlaxAlbertPreTrainedModel",
      ]

if TYPE_CHECKING:
      from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert import AlbertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_albert_fast import AlbertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_albert import (
                ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                AlbertForMaskedLM,
                AlbertForMultipleChoice,
                AlbertForPreTraining,
                AlbertForQuestionAnswering,
                AlbertForSequenceClassification,
                AlbertForTokenClassification,
                AlbertModel,
                AlbertPreTrainedModel,
                load_tf_weights_in_albert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_albert import (
                TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFAlbertForMaskedLM,
                TFAlbertForMultipleChoice,
                TFAlbertForPreTraining,
                TFAlbertForQuestionAnswering,
                TFAlbertForSequenceClassification,
                TFAlbertForTokenClassification,
                TFAlbertMainLayer,
                TFAlbertModel,
                TFAlbertPreTrainedModel,
            )

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_albert import (
                FlaxAlbertForMaskedLM,
                FlaxAlbertForMultipleChoice,
                FlaxAlbertForPreTraining,
                FlaxAlbertForQuestionAnswering,
                FlaxAlbertForSequenceClassification,
                FlaxAlbertForTokenClassification,
                FlaxAlbertModel,
                FlaxAlbertPreTrainedModel,
            )
else:
      import sys

      __UpperCAmelCase      :Tuple			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



'''simple docstring'''



import requests

__UpperCAmelCase      :Union[str, Any]			    =       "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="




def      _a     (						_lowercase :     str  ):





     '''simple docstring'''






     __UpperCAmelCase     :			Union[str, Any]  							=      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>")