


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    __magic_name__						(  __lowerCAmelCase       ,					unittest.TestCase):
     A:		List[str]								=   BartTokenizer
     A:		Optional[int]								=   BartTokenizerFast
     A:		Optional[int]								=   True
     A:		List[Any]								=   filter_roberta_detectors


     def   UpperCAmelCase__						(   self				:  Union[str, Any]      )	->   Optional[int]:





      '''simple docstring'''






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

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


     def   UpperCAmelCase__						(   self				:  List[str]				,						**lowerCamelCase__				:  Optional[Any]      )	->   List[str]:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  Optional[int]				,						**lowerCamelCase__				:  List[Any]      )	->   Optional[int]:





      '''simple docstring'''






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


     def   UpperCAmelCase__						(   self				:  int				,						lowerCamelCase__				:  Dict      )	->   Union[str, Any]:





      '''simple docstring'''






      return "lower newer", "lower newer"


     @cached_property
     def   UpperCAmelCase__						(   self				:  List[Any]      )	->   Dict:





      '''simple docstring'''






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


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





      '''simple docstring'''






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


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





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      UpperCamelCase__       :     Union[str, Any] 							= [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__       :     List[Any] 							= tokenizer(lowerCamelCase__				,						max_length=len(lowerCamelCase__      )				,						padding=lowerCamelCase__				,						return_tensors='''pt'''      )
       self.assertIsInstance(lowerCamelCase__				,						lowerCamelCase__      )

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


     @require_torch
     def   UpperCAmelCase__						(   self				:  Optional[Any]      )	->   List[str]:





      '''simple docstring'''






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


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





      '''simple docstring'''






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


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





      '''simple docstring'''






      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__       :     Optional[Any] 							= tokenizer(
           ['''I am a small frog''' * 1024, '''I am a small frog''']				,						padding=lowerCamelCase__				,						truncation=lowerCamelCase__				,						return_tensors='''pt'''      )
       self.assertIsInstance(lowerCamelCase__				,						lowerCamelCase__      )
       self.assertEqual(batch.input_ids.shape				,						(2, 1024)      )


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





      '''simple docstring'''






      UpperCamelCase__       :     Tuple 							= ['''A long paragraph for summarization.''']
      UpperCamelCase__       :     List[str] 							= [
          '''Summary of the text.''',
      ]
      for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
       UpperCamelCase__       :     Optional[int] 							= tokenizer(lowerCamelCase__				,						return_tensors='''pt'''      )
       UpperCamelCase__       :     Dict 							= tokenizer(text_target=lowerCamelCase__				,						return_tensors='''pt'''      )
       UpperCamelCase__       :     Optional[Any] 							= inputs['''input_ids''']
       UpperCamelCase__       :     Optional[Any] 							= 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[int]      )	->   List[str]:





      '''simple docstring'''






      pass


     def   UpperCAmelCase__						(   self				:  List[str]      )	->   Any:





      '''simple docstring'''






      for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
        UpperCamelCase__       :     Optional[int] 							= self.rust_tokenizer_class.from_pretrained(lowerCamelCase__				,						**lowerCamelCase__      )
        UpperCamelCase__       :     List[Any] 							= self.tokenizer_class.from_pretrained(lowerCamelCase__				,						**lowerCamelCase__      )
        UpperCamelCase__       :     Dict 							= '''A, <mask> AllenNLP sentence.'''
        UpperCamelCase__       :     Union[str, Any] 							= tokenizer_r.encode_plus(lowerCamelCase__				,						add_special_tokens=lowerCamelCase__				,						return_token_type_ids=lowerCamelCase__      )
        UpperCamelCase__       :     Any 							= tokenizer_p.encode_plus(lowerCamelCase__				,						add_special_tokens=lowerCamelCase__				,						return_token_type_ids=lowerCamelCase__      )

        # 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''']      )				,						)

        UpperCamelCase__       :     Optional[int] 							= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
        UpperCamelCase__       :     Dict 							= 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(
            lowerCamelCase__				,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )
        self.assertSequenceEqual(
            lowerCamelCase__				,						['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']      )






import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir

@dataclass
class    __magic_name__						:
     A:		str



class    __magic_name__						(  unittest.TestCase):


     def   UpperCAmelCase__						(   self				:  Any      )	->   List[Any]:





      '''simple docstring'''






      UpperCamelCase__       :     Optional[Any] 							= get_tests_dir('''fixtures/test_sentencepiece.model'''      )
      UpperCamelCase__       :     Any 							= get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model'''      )

      UpperCamelCase__       :     List[str] 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     Optional[int] 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						0      )

      UpperCamelCase__       :     Tuple 							= FakeOriginalTokenizer(vocab_file=lowerCamelCase__      )

      with warnings.catch_warnings(record=lowerCamelCase__      ) as w:
       UpperCamelCase__       :     str 							= SpmConverter(lowerCamelCase__      )
      self.assertEqual(len(lowerCamelCase__      )				,						1      )

      self.assertIn(
          '''The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option'''
          ''' which is not implemented in the fast tokenizers.'''				,						str(w[0].message      )				,						)


