
from typing import TYPE_CHECKING

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


_A   =     {
    "configuration_speech_to_text": ["SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP", "Speech2TextConfig"],
    "processing_speech_to_text": ["Speech2TextProcessor"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextTokenizer"]

try:
			if not is_speech_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     ["Speech2TextFeatureExtractor"]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFSpeech2TextForConditionalGeneration",
			    "TFSpeech2TextModel",
			    "TFSpeech2TextPreTrainedModel",
			]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A   =     [
			    "SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Speech2TextForConditionalGeneration",
			    "Speech2TextModel",
			    "Speech2TextPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
			from .processing_speech_to_text import SpeechaTextProcessor

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_speech_to_text import SpeechaTextTokenizer

			try:
						if not is_speech_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_speech_to_text import (
						    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFSpeechaTextForConditionalGeneration,
						    TFSpeechaTextModel,
						    TFSpeechaTextPreTrainedModel,
						)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_speech_to_text import (
						    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SpeechaTextForConditionalGeneration,
						    SpeechaTextModel,
						    SpeechaTextPreTrainedModel,
						)

else:
			import sys

			_A   =     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





import requests

_A   =     "https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="






def    lowerCamelCase__				(				__lowerCAmelCase  :  str				):




			"""simple docstring"""


			lowerCAmelCase_						=	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>")



