



import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class    __UpperCamelCase			(       _lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    Tuple      						=					MvpTokenizer
 _lowercase   :    List[Any]      						=					MvpTokenizerFast
 _lowercase   :    str      						=					True
 _lowercase   :    List[str]      						=					filter_roberta_detectors

 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  super().setUp()
  a__												= [
      '''l''',
      '''o''',
      '''w''',
      '''e''',
      '''r''',
      '''s''',
      '''t''',
      '''i''',
      '''d''',
      '''n''',
      '''\u0120''',
      '''\u0120l''',
      '''\u0120n''',
      '''\u0120lo''',
      '''\u0120low''',
      '''er''',
      '''\u0120lowest''',
      '''\u0120newer''',
      '''\u0120wider''',
      '''<unk>''',
  ]
  a__												= dict(zip(SCREAMING_SNAKE_CASE		,				range(len(SCREAMING_SNAKE_CASE      )      )      )      )
  a__												= ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
  a__												= {'''unk_token''': '''<unk>'''}

  a__												= os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''vocab_file''']      )
  a__												= os.path.join(self.tmpdirname		,				VOCAB_FILES_NAMES['''merges_file''']      )
  with open(self.vocab_file		,				'''w'''		,				encoding='''utf-8'''      ) as fp:
   fp.write(json.dumps(SCREAMING_SNAKE_CASE      ) + '''\n'''      )
  with open(self.merges_file		,				'''w'''		,				encoding='''utf-8'''      ) as fp:
   fp.write('''\n'''.join(SCREAMING_SNAKE_CASE      )      )

 def 						_UpperCAmelCase       (	self		,				**SCREAMING_SNAKE_CASE      ) ->			Optional[int]:
  kwargs.update(self.special_tokens_map      )
  return self.tokenizer_class.from_pretrained(self.tmpdirname		,				**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				**SCREAMING_SNAKE_CASE      ) ->			Tuple:
  kwargs.update(self.special_tokens_map      )
  return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,				**SCREAMING_SNAKE_CASE      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  return "lower newer", "lower newer"

 @cached_property
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''      )

 @cached_property
 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''      )

 @require_torch
 def 						_UpperCAmelCase       (	self      ) ->			Dict:
  a__												= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  a__												= [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   a__												= tokenizer(SCREAMING_SNAKE_CASE		,				max_length=len(SCREAMING_SNAKE_CASE      )		,				padding=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      )
   self.assertIsInstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

   self.assertEqual((2, 9)		,				batch.input_ids.shape      )
   self.assertEqual((2, 9)		,				batch.attention_mask.shape      )
   a__												= batch.input_ids.tolist()[0]
   self.assertListEqual(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   # Test that special tokens are reset

 @require_torch
 def 						_UpperCAmelCase       (	self      ) ->			Optional[int]:
  a__												= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   a__												= tokenizer(SCREAMING_SNAKE_CASE		,				padding=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      )
   # check if input_ids are returned and no labels
   self.assertIn('''input_ids'''		,				SCREAMING_SNAKE_CASE      )
   self.assertIn('''attention_mask'''		,				SCREAMING_SNAKE_CASE      )
   self.assertNotIn('''labels'''		,				SCREAMING_SNAKE_CASE      )
   self.assertNotIn('''decoder_attention_mask'''		,				SCREAMING_SNAKE_CASE      )

 @require_torch
 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= [
      '''Summary of the text.''',
      '''Another summary.''',
  ]
  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   a__												= tokenizer(text_target=SCREAMING_SNAKE_CASE		,				max_length=3_2		,				padding='''max_length'''		,				return_tensors='''pt'''      )
   self.assertEqual(3_2		,				targets['''input_ids'''].shape[1]      )

 @require_torch
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   a__												= tokenizer(
       ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']		,				padding=SCREAMING_SNAKE_CASE		,				truncation=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      )
   self.assertIsInstance(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   self.assertEqual(batch.input_ids.shape		,				(2, 1_0_2_4)      )

 @require_torch
 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= ['''A long paragraph for summarization.''']
  a__												= [
      '''Summary of the text.''',
  ]
  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   a__												= tokenizer(SCREAMING_SNAKE_CASE		,				text_target=SCREAMING_SNAKE_CASE		,				return_tensors='''pt'''      )
   a__												= inputs['''input_ids''']
   a__												= inputs['''labels''']
   self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
   self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
   self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
   self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )

 def 						_UpperCAmelCase       (	self      ) ->			int:
  pass



 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
   with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
    a__												= self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
    a__												= self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE		,				**SCREAMING_SNAKE_CASE      )
    a__												= '''A, <mask> AllenNLP sentence.'''
    a__												= tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE		,				add_special_tokens=SCREAMING_SNAKE_CASE		,				return_token_type_ids=SCREAMING_SNAKE_CASE      )
    a__												= tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE		,				add_special_tokens=SCREAMING_SNAKE_CASE		,				return_token_type_ids=SCREAMING_SNAKE_CASE      )

    # token_type_ids should put 0 everywhere
    self.assertEqual(sum(tokens_r['''token_type_ids''']      )		,				sum(tokens_p['''token_type_ids''']      )      )

    # attention_mask should put 1 everywhere, so sum over length should be 1
    self.assertEqual(
        sum(tokens_r['''attention_mask''']      ) / len(tokens_r['''attention_mask''']      )		,				sum(tokens_p['''attention_mask''']      ) / len(tokens_p['''attention_mask''']      )		,				)

    a__												= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
    a__												= tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']      )

    # Rust correctly handles the space before the mask while python doesnt
    self.assertSequenceEqual(tokens_p['''input_ids''']		,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )
    self.assertSequenceEqual(tokens_r['''input_ids''']		,				[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]      )

    self.assertSequenceEqual(
        SCREAMING_SNAKE_CASE		,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )
    self.assertSequenceEqual(
        SCREAMING_SNAKE_CASE		,				['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )










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())





