



'''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						):



 '''simple docstring'''







 __lowerCamelCase   :	Tuple										=	BartTokenizer
 __lowerCamelCase   :	Any										=	BartTokenizerFast
 __lowerCamelCase   :	str										=	True
 __lowerCamelCase   :	List[str]										=	filter_roberta_detectors



 def  a__  (self	)   ->					Any:


      """simple docstring"""



      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(A			,       range(len(A	)	)	)	)
      _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(A	) + '''\n'''	)
      with open(self.merges_file			,       '''w'''			,       encoding='''utf-8'''	) as fp:
           fp.write('''\n'''.join(A	)	)



 def  a__  (self			,       **A	)   ->					Tuple:


      """simple docstring"""



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



 def  a__  (self			,       **A	)   ->					str:


      """simple docstring"""



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



 def  a__  (self			,       A	)   ->					int:


      """simple docstring"""



      return "lower newer", "lower newer"



 @cached_property
 def  a__  (self	)   ->					Any:


      """simple docstring"""



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



 @cached_property
 def  a__  (self	)   ->					Optional[int]:


      """simple docstring"""



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



 @require_torch
 def  a__  (self	)   ->					List[str]:


      """simple docstring"""



      _a     =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      _a     =  [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _a     =  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	)
           _a     =  batch.input_ids.tolist()[0]
           self.assertListEqual(A			,       A	)
           # Test that special tokens are reset



 @require_torch
 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      _a     =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _a     =  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  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



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



 @require_torch
 def  a__  (self	)   ->					Optional[Any]:


      """simple docstring"""



      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _a     =  tokenizer(
               ['''I am a small frog''' * 1_024, '''I am a small frog''']			,       padding=A			,       truncation=A			,       return_tensors='''pt'''	)
           self.assertIsInstance(A			,       A	)
           self.assertEqual(batch.input_ids.shape			,       (2, 1_024)	)



 @require_torch
 def  a__  (self	)   ->					List[Any]:


      """simple docstring"""



      _a     =  ['''A long paragraph for summarization.''']
      _a     =  [
          '''Summary of the text.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
           _a     =  tokenizer(A			,       return_tensors='''pt'''	)
           _a     =  tokenizer(text_target=A			,       return_tensors='''pt'''	)
           _a     =  inputs['''input_ids''']
           _a     =  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  a__  (self	)   ->					Dict:


      """simple docstring"""



      pass



 def  a__  (self	)   ->					str:


      """simple docstring"""



      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(A			,       **A	)
                _a     =  self.tokenizer_class.from_pretrained(A			,       **A	)
                _a     =  '''A, <mask> AllenNLP sentence.'''
                _a     =  tokenizer_r.encode_plus(A			,       add_special_tokens=A			,       return_token_type_ids=A	)
                _a     =  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''']	)			,       )

                _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, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)
                self.assertSequenceEqual(tokens_r['''input_ids''']			,       [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]	)

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








'''simple docstring'''





import os
from string import ascii_letters

lowercase_			=  ascii_letters + " \t\n"




def lowerCAmelCase  ():



     """simple docstring"""



     _a     =  os.path.split(os.path.realpath(__A))
     _a     =  {}
     with open(path[0] + '''/dictionary.txt''') as dictionary_file:
          for word in dictionary_file.read().split('''\n'''):
               _a     =  None
     return english_words


lowercase_			=  load_dictionary()




def lowerCAmelCase  (__A):



     """simple docstring"""



     _a     =  message.upper()
     _a     =  remove_non_letters(__A)
     _a     =  message.split()
     _a     =  len([word for word in possible_words if word in ENGLISH_WORDS])
     return float(__A) / len(__A)




def lowerCAmelCase  (__A):



     """simple docstring"""



     return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE)




def lowerCAmelCase  (__A  ,     __A = 20  ,     __A = 85):



     """simple docstring"""



     _a     =  get_english_count(__A) * 100 >= word_percentage
     _a     =  len(remove_non_letters(__A))
     _a     =  (float(__A) / len(__A)) * 100
     _a     =  message_letters_percentage >= letter_percentage
     return words_match and letters_match


if __name__ == "__main__":
  import doctest

  doctest.testmod()



