


"""simple docstring"""


from __future__ import annotations

import requests






def      a__ (				__lowercase   )	->	dict:
				_A					  =			f"""https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"""
				return requests.get(__lowercase   ).json()






def      a__ (				__lowercase = 10   )	->	list[dict]:
				_A					  =			"https://hacker-news.firebaseio.com/v0/topstories.json?print=pretty"
				_A					  =			requests.get(__lowercase   ).json()[:max_stories]
				return [get_hackernews_story(__lowercase   ) for story_id in story_ids]






def      a__ (				__lowercase = 10   )	->	str:
				_A					  =			hackernews_top_stories(__lowercase   )
				return "\n".join("* [{title}]({url})".format(**__lowercase   ) 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_torch_available


a_       							=    {
    "configuration_upernet": ["UperNetConfig"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a_       							=    [
		    "UperNetForSemanticSegmentation",
		    "UperNetPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_upernet import UperNetConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel


else:
		import sys

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