from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
	from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline












from __future__ import annotations

import requests

def      A      (      _SCREAMING_SNAKE_CASE    )	-> dict:
       lowerCamelCase   :      Tuple			 =					f'''https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty'''
       return requests.get(_SCREAMING_SNAKE_CASE    ).json()

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

def      A      (      _SCREAMING_SNAKE_CASE = 10    )	-> str:
       lowerCamelCase   :      str			 =					hackernews_top_stories(_SCREAMING_SNAKE_CASE    )
       return "\n".join("* [{title}]({url})".format(**_SCREAMING_SNAKE_CASE    ) for story in stories    )


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






