





"""simple docstring"""
import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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 			a       (      a__      ,   unittest.TestCase     ):
 snake_case__								=					BartTokenizer
 snake_case__								=					BartTokenizerFast
 snake_case__								=					True
 snake_case__								=					filter_roberta_detectors



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






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

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



 def       UpperCamelCase__     (    self							,					**_snake_case    ):







  """simple docstring"""






  kwargs.update(self.special_tokens_map    )
  return self.tokenizer_class.from_pretrained(self.tmpdirname							,					**_snake_case    )



 def       UpperCamelCase__     (    self							,					**_snake_case    ):







  """simple docstring"""






  kwargs.update(self.special_tokens_map    )
  return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,					**_snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  return "lower newer", "lower newer"



 @cached_property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return BartTokenizer.from_pretrained('facebook/bart-large'    )



 @cached_property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return BartTokenizerFast.from_pretrained('facebook/bart-large'    )



 @require_torch
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
  lowerCAmelCase													=   [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

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

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



 @require_torch
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   ['A long paragraph for summarization.', 'Another paragraph for summarization.']
  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   lowerCAmelCase													=   tokenizer(_snake_case							,					padding=_snake_case							,					return_tensors='pt'    )
   # check if input_ids are returned and no labels
   self.assertIn('input_ids'							,					_snake_case    )
   self.assertIn('attention_mask'							,					_snake_case    )
   self.assertNotIn('labels'							,					_snake_case    )
   self.assertNotIn('decoder_attention_mask'							,					_snake_case    )



 @require_torch
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   [
      'Summary of the text.',
      'Another summary.',
  ]
  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   lowerCAmelCase													=   tokenizer(text_target=_snake_case							,					max_length=32							,					padding='max_length'							,					return_tensors='pt'    )
   self.assertEqual(32							,					targets['input_ids'].shape[1]    )



 @require_torch
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   lowerCAmelCase													=   tokenizer(
       ['I am a small frog' * 10_24, 'I am a small frog']							,					padding=_snake_case							,					truncation=_snake_case							,					return_tensors='pt'    )
   self.assertIsInstance(_snake_case							,					_snake_case    )
   self.assertEqual(batch.input_ids.shape							,					(2, 10_24)    )



 @require_torch
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   ['A long paragraph for summarization.']
  lowerCAmelCase													=   [
      'Summary of the text.',
  ]
  for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
   lowerCAmelCase													=   tokenizer(_snake_case							,					return_tensors='pt'    )
   lowerCAmelCase													=   tokenizer(text_target=_snake_case							,					return_tensors='pt'    )
   lowerCAmelCase													=   inputs['input_ids']
   lowerCAmelCase													=   targets['input_ids']
   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    ):







  """simple docstring"""






  pass



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
   with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
    lowerCAmelCase													=   self.rust_tokenizer_class.from_pretrained(_snake_case							,					**_snake_case    )
    lowerCAmelCase													=   self.tokenizer_class.from_pretrained(_snake_case							,					**_snake_case    )
    lowerCAmelCase													=   'A, <mask> AllenNLP sentence.'
    lowerCAmelCase													=   tokenizer_r.encode_plus(_snake_case							,					add_special_tokens=_snake_case							,					return_token_type_ids=_snake_case    )
    lowerCAmelCase													=   tokenizer_p.encode_plus(_snake_case							,					add_special_tokens=_snake_case							,					return_token_type_ids=_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']    )							,					)

    lowerCAmelCase													=   tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']    )
    lowerCAmelCase													=   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_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )
    self.assertSequenceEqual(tokens_r['input_ids']							,					[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]    )

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












"""simple docstring"""
import math

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int ):

 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5     ,    int(math.sqrt(_UpperCAmelCase ) + 1 )     ,    6 ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True

def 		_SCREAMING_SNAKE_CASE (_UpperCAmelCase				:						int = 200_0000 ):
 return sum(num for num in range(3     ,    _UpperCAmelCase     ,    2 ) if is_prime(_UpperCAmelCase ) ) + 2 if n > 2 else 0


if __name__ == "__main__":
       print(f'''{solution() = }''')





