



from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy

class       UpperCamelCase  (					lowercase__				):





     """simple docstring"""






     _lowerCamelCase					: List[str]				   ="""MarkupLMFeatureExtractor"""
     _lowerCamelCase					: Tuple				   =("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
     _lowerCamelCase					: Optional[int]				   =True





     def __call__(					self :							Any	,      _lowerCamelCase :							str=None	,      _lowerCamelCase :							List[Any]=None	,      _lowerCamelCase :							str=None	,      _lowerCamelCase :							List[str]=None	,      _lowerCamelCase :							Tuple=None	,      _lowerCamelCase :							bool = True	,      _lowerCamelCase :							Union[bool, str, PaddingStrategy] = False	,      _lowerCamelCase :							Union[bool, str, TruncationStrategy] = None	,      _lowerCamelCase :							Optional[int] = None	,      _lowerCamelCase :							int = 0	,      _lowerCamelCase :							Optional[int] = None	,      _lowerCamelCase :							Optional[bool] = None	,      _lowerCamelCase :							Optional[bool] = None	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							bool = False	,      _lowerCamelCase :							bool = True	,      _lowerCamelCase :							Optional[Union[str, TensorType]] = None	,      **_lowerCamelCase :							Any	,      ):
          # first, create nodes and xpaths
          if self.parse_html:
               if html_strings is None:
                    raise ValueError('''Make sure to pass HTML strings in case `parse_html` is set to `True`''' )

               if nodes is not None or xpaths is not None or node_labels is not None:
                    raise ValueError(
                        '''Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`''' )

               A__				=   self.feature_extractor(_A )
               A__				=   features['nodes']
               A__				=   features['xpaths']
          else:
               if html_strings is not None:
                    raise ValueError('''You have passed HTML strings but `parse_html` is set to `False`.''' )
               if nodes is None or xpaths is None:
                    raise ValueError('''Make sure to pass nodes and xpaths in case `parse_html` is set to `False`''' )

        # # second, apply the tokenizer
          if questions is not None and self.parse_html:
               if isinstance(_A	,      _A ):
                    A__				=   [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

          A__				=   self.tokenizer(
              text=questions if questions is not None else nodes	,      text_pair=nodes if questions is not None else None	,      xpaths=_A	,      node_labels=_A	,      add_special_tokens=_A	,      padding=_A	,      truncation=_A	,      max_length=_A	,      stride=_A	,      pad_to_multiple_of=_A	,      return_token_type_ids=_A	,      return_attention_mask=_A	,      return_overflowing_tokens=_A	,      return_special_tokens_mask=_A	,      return_offsets_mapping=_A	,      return_length=_A	,      verbose=_A	,      return_tensors=_A	,      **_A	,      )

          return encoded_inputs





     def 						A__  (					self :							Dict	,      *_lowerCamelCase :							Optional[Any]	,      **_lowerCamelCase :							Optional[int] ):
          return self.tokenizer.batch_decode(*_A	,      **_A )





     def 						A__  (					self :							Optional[Any]	,      *_lowerCamelCase :							Tuple	,      **_lowerCamelCase :							List[str] ):
          return self.tokenizer.decode(*_A	,      **_A )





     @property
     def 						A__  (					self :							int ):
          A__				=   self.tokenizer.model_input_names
          return tokenizer_input_names








"""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]



