


import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel






class       _UpperCamelCase    (					__snake_case				):

    """simple docstring"""







    lowerCAmelCase 		= 'M-CLIP'

    def __init__(						self				,   a__=1024				,   a__=768				,   **a__   )	->				List[Any]:
     A								=				transformerDimSize
     A								=				imageDimSize
     super().__init__(**a__   )






class       _UpperCamelCase    (					__snake_case				):

    """simple docstring"""







    lowerCAmelCase 		= MCLIPConfig

    def __init__(						self				,   a__				,   *a__				,   **a__   )	->				str:
     super().__init__(a__				,   *a__				,   **a__   )
     A								=				XLMRobertaModel(a__   )
     A								=				torch.nn.Linear(
         in_features=config.transformerDimensions				,   out_features=config.numDims   )

    def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				List[Any]:
     A								=				self.transformer(input_ids=a__				,   attention_mask=a__   )[0]
     A								=				(embs * attention_mask.unsqueeze(2   )).sum(dim=1   ) / attention_mask.sum(dim=1   )[:, None]
     return self.LinearTransformation(a__   ), embs







from __future__ import annotations

import requests





def   _lowerCAmelCase				(   UpperCamelCase__:     str						) ->   dict:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   list[dict]:





	"""simple docstring"""







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





def   _lowerCAmelCase				(   UpperCamelCase__:     int = 10						) ->   str:





	"""simple docstring"""







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


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



