



"""simple docstring"""







import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case	:				Optional[Any]  					=			get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Dict				   =ErnieMTokenizer
     _lowerCamelCase					: Optional[int]				   =False
     _lowerCamelCase					: Optional[int]				   =True
     _lowerCamelCase					: int				   =False
     _lowerCamelCase					: str				   =False





     def 						A__  (					self :							Optional[int] ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          A__				=   ErnieMTokenizer(_lowerCamelCase	,      unk_token='''<unk>'''	,      pad_token='''<pad>''' )
          tokenizer.save_pretrained(self.tmpdirname )





     def 						A__  (					self :							Tuple	,      _lowerCamelCase :							Optional[int] ):
          A__				=   '''this is a test'''
          A__				=   '''this is a test'''
          return input_text, output_text





     def 						A__  (					self :							Any ):
          A__				=   '''<pad>'''
          A__				=   0

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowerCamelCase )	,      _lowerCamelCase )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowerCamelCase )	,      _lowerCamelCase )





     def 						A__  (					self :							Tuple ):
          A__				=   list(self.get_tokenizer().get_vocab().keys() )

          self.assertEqual(vocab_keys[0]	,      '''<pad>''' )
          self.assertEqual(vocab_keys[1]	,      '''<unk>''' )
          self.assertEqual(vocab_keys[-1]	,      '''▁eloquent''' )
          self.assertEqual(len(_lowerCamelCase )	,      3_0_0_0_0 )





     def 						A__  (					self :							str ):
          self.assertEqual(self.get_tokenizer().vocab_size	,      3_0_0_0_0 )





     def 						A__  (					self :							int ):
          if not self.test_rust_tokenizer:
               return

          A__				=   self.get_tokenizer()
          A__				=   self.get_rust_tokenizer()

          A__				=   '''I was born in 92000, and this is falsé.'''

          A__				=   tokenizer.tokenize(_lowerCamelCase )
          A__				=   rust_tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   self.get_rust_tokenizer()
          A__				=   tokenizer.encode(_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )





     def 						A__  (					self :							Optional[Any] ):
          A__				=   ErnieMTokenizer(_lowerCamelCase	,      do_lower_case=_lowerCamelCase	,      unk_token='''<unk>'''	,      pad_token='''<pad>''' )

          A__				=   tokenizer.tokenize('''This is a test''' )
          self.assertListEqual(_lowerCamelCase	,      ['''▁this''', '''▁is''', '''▁a''', '''▁test'''] )

          self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )	,      [4_8, 2_5, 2_1, 1_2_8_9] )

          A__				=   tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
          # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
          self.assertListEqual(
              _lowerCamelCase	,      ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''] )
          A__				=   tokenizer.convert_tokens_to_ids(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      [3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9] )

          A__				=   tokenizer.convert_ids_to_tokens(_lowerCamelCase )
          self.assertListEqual(
              _lowerCamelCase	,      ['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']	,      )





     def 						A__  (					self :							Any ):
          A__				=   ErnieMTokenizer(_lowerCamelCase	,      unk_token='''<unk>'''	,      pad_token='''<pad>''' )

          A__				=   tokenizer.encode('''sequence builders''' )
          A__				=   tokenizer.encode('''multi-sequence build''' )

          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase	,      _lowerCamelCase )

          assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
          assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
              tokenizer.sep_token_id
          ] + text_a + [tokenizer.sep_token_id]





     @slow
     def 						A__  (					self :							List[str] ):
          # fmt: off
          A__				=   {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
          # fmt: on

          self.tokenizer_integration_test_util(
              expected_encoding=_lowerCamelCase	,      model_name='''susnato/ernie-m-base_pytorch'''	,      sequences=[
                  '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
                  '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
                  '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
                  '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
                  '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
                  '''conditioning on both left and right context in all layers.''',
                  '''The quick brown fox jumps over the lazy dog.''',
              ]	,      )








"""simple docstring"""







import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class       UpperCamelCase  (					a      , unittest.TestCase				):





     """simple docstring"""






     _lowerCamelCase					: Dict				   =HerbertTokenizer
     _lowerCamelCase					: Optional[int]				   =HerbertTokenizerFast
     _lowerCamelCase					: Optional[Any]				   =True





     def 						A__  (					self :							Dict ):
          super().setUp()

          # Use a simpler test file without japanese/chinese characters
          with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''	,      encoding='''utf-8''' ) as f_data:
               A__				=   f_data.read().replace('''\n\n'''	,      '''\n''' ).strip()

          A__				=   [
              '''<s>''',
              '''</s>''',
              '''l''',
              '''o''',
              '''w''',
              '''e''',
              '''r''',
              '''s''',
              '''t''',
              '''i''',
              '''d''',
              '''n''',
              '''w</w>''',
              '''r</w>''',
              '''t</w>''',
              '''lo''',
              '''low''',
              '''er</w>''',
              '''low</w>''',
              '''lowest</w>''',
              '''newer</w>''',
              '''wider</w>''',
              ''',</w>''',
              '''<unk>''',
          ]
          A__				=   dict(zip(_lowerCamelCase	,      range(len(_lowerCamelCase ) ) ) )
          A__				=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

          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''' ) as fp:
               fp.write(json.dumps(_lowerCamelCase ) )
          with open(self.merges_file	,      '''w''' ) as fp:
               fp.write('''\n'''.join(_lowerCamelCase ) )





     def 						A__  (					self :							List[Any]	,      _lowerCamelCase :							Union[str, Any] ):
          A__				=   '''lower newer'''
          A__				=   '''lower newer'''
          return input_text, output_text





     def 						A__  (					self :							int ):
          A__				=   self.tokenizer_class(vocab_file=self.vocab_file	,      merges_file=self.merges_file )

          A__				=   '''lower'''
          A__				=   ['''low''', '''er</w>''']
          A__				=   tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokens + ['''<unk>''']
          A__				=   [1_6, 1_7, 2_3]
          self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase )	,      _lowerCamelCase )





     def 						A__  (					self :							Optional[int] ):
          if not self.test_rust_tokenizer:
               return

          A__				=   self.get_tokenizer()
          A__				=   self.get_rust_tokenizer()

          A__				=   '''lower,newer'''

          A__				=   tokenizer.tokenize(_lowerCamelCase )
          A__				=   rust_tokenizer.tokenize(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase	,      add_special_tokens=_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )

          A__				=   self.get_rust_tokenizer()
          A__				=   tokenizer.encode(_lowerCamelCase )
          A__				=   rust_tokenizer.encode(_lowerCamelCase )
          self.assertListEqual(_lowerCamelCase	,      _lowerCamelCase )





     @slow
     def 						A__  (					self :							Any ):
          A__				=   self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased''' )

          A__				=   tokenizer.encode('''konstruowanie sekwencji'''	,      add_special_tokens=_lowerCamelCase )
          A__				=   tokenizer.encode('''konstruowanie wielu sekwencji'''	,      add_special_tokens=_lowerCamelCase )

          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase )
          A__				=   tokenizer.build_inputs_with_special_tokens(_lowerCamelCase	,      _lowerCamelCase )

          assert encoded_sentence == [0] + text + [2]
          assert encoded_pair == [0] + text + [2] + text_a + [2]



