



import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
     import torch
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    str      						=					'''char'''
 _lowercase   :    Tuple      						=					'''bpe'''
 _lowercase   :    Optional[int]      						=					'''wp'''





a_      :      Union[str, Any]    =     (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class    __UpperCamelCase			(       _lowercase				):

 """simple docstring"""



 _lowercase   :    Union[str, Any]      						=					['''image_processor''', '''char_tokenizer''']
 _lowercase   :    List[Any]      						=					'''ViTImageProcessor'''
 _lowercase   :    str      						=					'''MgpstrTokenizer'''

 def __init__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= None
  if "feature_extractor" in kwargs:
   warnings.warn(
       '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
       ''' instead.'''		,				SCREAMING_SNAKE_CASE		,				)
   a__												= kwargs.pop('''feature_extractor'''      )

  a__												= image_processor if image_processor is not None else feature_extractor
  if image_processor is None:
   raise ValueError('''You need to specify an `image_processor`.'''      )
  if tokenizer is None:
   raise ValueError('''You need to specify a `tokenizer`.'''      )

  a__												= tokenizer
  a__												= AutoTokenizer.from_pretrained('''gpt2'''      )
  a__												= AutoTokenizer.from_pretrained('''bert-base-uncased'''      )

  super().__init__(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

 def __call__(	self		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=None		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  if images is None and text is None:
   raise ValueError('''You need to specify either an `images` or `text` input to process.'''      )

  if images is not None:
   a__												= self.image_processor(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
  if text is not None:
   a__												= self.char_tokenizer(SCREAMING_SNAKE_CASE		,				return_tensors=SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )

  if text is None:
   return inputs
  elif images is None:
   return encodings
  else:
   a__												= encodings['''input_ids''']
   return inputs

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  a__							,			a__							,			a__												= sequences
  a__												= char_preds.size(0      )

  a__							,			a__												= self._decode_helper(SCREAMING_SNAKE_CASE		,				'''char'''      )
  a__							,			a__												= self._decode_helper(SCREAMING_SNAKE_CASE		,				'''bpe'''      )
  a__							,			a__												= self._decode_helper(SCREAMING_SNAKE_CASE		,				'''wp'''      )

  a__												= []
  a__												= []
  for i in range(SCREAMING_SNAKE_CASE      ):
   a__												= [char_scores[i], bpe_scores[i], wp_scores[i]]
   a__												= [char_strs[i], bpe_strs[i], wp_strs[i]]
   a__												= scores.index(max(SCREAMING_SNAKE_CASE      )      )
   final_strs.append(strs[max_score_index]      )
   final_scores.append(scores[max_score_index]      )

  a__												= {}
  a__												= final_strs
  a__												= final_scores
  a__												= char_strs
  a__												= bpe_strs
  a__												= wp_strs
  return out

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  if format == DecodeType.CHARACTER:
   a__												= self.char_decode
   a__												= 1
   a__												= '''[s]'''
  elif format == DecodeType.BPE:
   a__												= self.bpe_decode
   a__												= 2
   a__												= '''#'''
  elif format == DecodeType.WORDPIECE:
   a__												= self.wp_decode
   a__												= 1_0_2
   a__												= '''[SEP]'''
  else:
   raise ValueError(f"Format {format} is not supported."      )

  a__							,			a__												= [], []
  a__												= pred_logits.size(0      )
  a__												= pred_logits.size(1      )
  a__							,			a__												= pred_logits.topk(1		,				dim=-1		,				largest=SCREAMING_SNAKE_CASE		,				sorted=SCREAMING_SNAKE_CASE      )
  a__												= preds_index.view(-1		,				SCREAMING_SNAKE_CASE      )[:, 1:]
  a__												= decoder(SCREAMING_SNAKE_CASE      )
  a__							,			a__												= torch.nn.functional.softmax(SCREAMING_SNAKE_CASE		,				dim=2      ).max(dim=2      )
  a__												= preds_max_prob[:, 1:]

  for index in range(SCREAMING_SNAKE_CASE      ):
   a__												= preds_str[index].find(SCREAMING_SNAKE_CASE      )
   a__												= preds_str[index][:pred_eos]
   a__												= preds_index[index].cpu().tolist()
   a__												= pred_index.index(SCREAMING_SNAKE_CASE      ) if eos_token in pred_index else -1
   a__												= preds_max_prob[index][: pred_eos_index + 1]
   a__												= pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
   dec_strs.append(SCREAMING_SNAKE_CASE      )
   conf_scores.append(SCREAMING_SNAKE_CASE      )

  return dec_strs, conf_scores

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  a__												= [seq.replace(''' '''		,				''''''      ) for seq in self.char_tokenizer.batch_decode(SCREAMING_SNAKE_CASE      )]
  return decode_strs

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Optional[Any]:
  return self.bpe_tokenizer.batch_decode(SCREAMING_SNAKE_CASE      )



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			str:
  a__												= [seq.replace(''' '''		,				''''''      ) for seq in self.wp_tokenizer.batch_decode(SCREAMING_SNAKE_CASE      )]
  return decode_strs










from __future__ import annotations

import requests






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






def     __a					(		__UpperCAmelCase = 10       ):
 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     __a					(		__UpperCAmelCase = 10       ):
 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())





