




"""simple docstring"""







from __future__ import annotations

import requests





def 							lowercase					(snake_case__   : str )     ->  dict:


				'''simple docstring'''






				lowerCAmelCase  =				f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
				return requests.get(snake_case__ ).json()





def 							lowercase					(snake_case__   : int = 10 )     ->  list[dict]:


				'''simple docstring'''






				lowerCAmelCase  =				"""https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"""
				lowerCAmelCase  =				requests.get(snake_case__ ).json()[:max_stories]
				return [get_hackernews_story(snake_case__ ) for story_id in story_ids]





def 							lowercase					(snake_case__   : int = 10 )     ->  str:


				'''simple docstring'''






				lowerCAmelCase  =				hackernews_top_stories(snake_case__ )
				return "\n".join("""* [{title}]({url})""".format(**snake_case__ ) for story in stories )


if __name__ == "__main__":
		print(hackernews_top_stories_as_markdown())











"""simple docstring"""







from typing import TYPE_CHECKING

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


a      =							{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a      =							[
		    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'BioGptForCausalLM',
		    'BioGptForTokenClassification',
		    'BioGptForSequenceClassification',
		    'BioGptModel',
		    'BioGptPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a      =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





