
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 				UpperCamelCase__		(	__SCREAMING_SNAKE_CASE       ,    unittest.TestCase	):






       """simple docstring"""


       UpperCAmelCase_				       =BartTokenizer
       UpperCAmelCase_				       =BartTokenizerFast
       UpperCAmelCase_				       =True
       UpperCAmelCase_				       =filter_roberta_detectors



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

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



       def     _UpperCamelCase	(							self ,     **_A	)				->				Any:
          kwargs.update(self.special_tokens_map	)
          return self.tokenizer_class.from_pretrained(self.tmpdirname ,     **_A	)



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



       def     _UpperCamelCase	(							self ,     _A	)				->				List[Any]:
          return "lower newer", "lower newer"



       @cached_property
       def     _UpperCamelCase	(							self	)				->				Dict:
          return BartTokenizer.from_pretrained('''facebook/bart-large'''	)



       @cached_property
       def     _UpperCamelCase	(							self	)				->				Any:
          return BartTokenizerFast.from_pretrained('''facebook/bart-large'''	)



       @require_torch
       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          SCREAMING_SNAKE_CASE_	=      [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

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

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



       @require_torch
       def     _UpperCamelCase	(							self	)				->				Union[str, Any]:
          SCREAMING_SNAKE_CASE_	=      ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE_	=      tokenizer(_A ,     padding=_A ,     return_tensors='''pt'''	)
             # check if input_ids are returned and no labels
             self.assertIn('''input_ids''' ,     _A	)
             self.assertIn('''attention_mask''' ,     _A	)
             self.assertNotIn('''labels''' ,     _A	)
             self.assertNotIn('''decoder_attention_mask''' ,     _A	)



       @require_torch
       def     _UpperCamelCase	(							self	)				->				Dict:
          SCREAMING_SNAKE_CASE_	=      [
              '''Summary of the text.''',
              '''Another summary.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE_	=      tokenizer(text_target=_A ,     max_length=32 ,     padding='''max_length''' ,     return_tensors='''pt'''	)
             self.assertEqual(32 ,     targets['''input_ids'''].shape[1]	)



       @require_torch
       def     _UpperCamelCase	(							self	)				->				List[str]:
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE_	=      tokenizer(
                 ['''I am a small frog''' * 1024, '''I am a small frog'''] ,     padding=_A ,     truncation=_A ,     return_tensors='''pt'''	)
             self.assertIsInstance(_A ,     _A	)
             self.assertEqual(batch.input_ids.shape ,     (2, 1024)	)



       @require_torch
       def     _UpperCamelCase	(							self	)				->				Any:
          SCREAMING_SNAKE_CASE_	=      ['''A long paragraph for summarization.''']
          SCREAMING_SNAKE_CASE_	=      [
              '''Summary of the text.''',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
             SCREAMING_SNAKE_CASE_	=      tokenizer(_A ,     return_tensors='''pt'''	)
             SCREAMING_SNAKE_CASE_	=      tokenizer(text_target=_A ,     return_tensors='''pt'''	)
             SCREAMING_SNAKE_CASE_	=      inputs['''input_ids''']
             SCREAMING_SNAKE_CASE_	=      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	)				->				Optional[Any]:
          pass




       def     _UpperCamelCase	(							self	)				->				Any:
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
             with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                SCREAMING_SNAKE_CASE_	=      self.rust_tokenizer_class.from_pretrained(_A ,     **_A	)
                SCREAMING_SNAKE_CASE_	=      self.tokenizer_class.from_pretrained(_A ,     **_A	)
                SCREAMING_SNAKE_CASE_	=      '''A, <mask> AllenNLP sentence.'''
                SCREAMING_SNAKE_CASE_	=      tokenizer_r.encode_plus(_A ,     add_special_tokens=_A ,     return_token_type_ids=_A	)
                SCREAMING_SNAKE_CASE_	=      tokenizer_p.encode_plus(_A ,     add_special_tokens=_A ,     return_token_type_ids=_A	)

                # 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''']	) ,     )

                SCREAMING_SNAKE_CASE_	=      tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']	)
                SCREAMING_SNAKE_CASE_	=      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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)
                self.assertSequenceEqual(tokens_r['''input_ids'''] ,     [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]	)

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




from math import sqrt


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      [1]
   for i in range(2,  int(sqrt(__lowerCamelCase						)						) + 1,  1						):
      if number % i == 0:
         values.append(__lowerCamelCase						)
         if int(number // i						) != i:
            values.append(int(number // i						)						)
   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   return sum(factors(__lowerCamelCase						)						) > n


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      factors(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      [[0 for i in range(number + 1						)] for j in range(r + 1						)]
   for i in range(r + 1						):
      SCREAMING_SNAKE_CASE_	=      True

   for i in range(1,  number + 1						):
      SCREAMING_SNAKE_CASE_	=      False

   for i in range(1,  r + 1						):
      for j in range(1,  number + 1						):
         if j < values[i - 1]:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j]
         else:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0


def  A__ (       __lowerCamelCase						):
   return abundant(__lowerCamelCase						) and not semi_perfect(__lowerCamelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F"""{number} is {'' if weird(number) else 'not '}weird.""")


