




'''simple docstring'''




import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin


class   __UpperCamelCase       ( a_			,					unittest.TestCase				):
       lowercase	:    List[str]      						=TransfoXLTokenizer
       lowercase	:    Optional[int]      						=False
       lowercase	:    Optional[int]      						=False






       def      lowercase__     (							self  ):



              """simple docstring"""







              super().setUp()

              lowerCamelCase_							=[
                  '''<unk>''',
                  '''[CLS]''',
                  '''[SEP]''',
                  '''want''',
                  '''unwanted''',
                  '''wa''',
                  '''un''',
                  '''running''',
                  ''',''',
                  '''low''',
                  '''l''',
              ]
              lowerCamelCase_							=os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES['''vocab_file''']  )
              with open(self.vocab_file,		'''w''',		encoding='''utf-8'''  ) as vocab_writer:
                     vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )






       def      lowercase__     (							self,		**lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							=True
              return TransfoXLTokenizer.from_pretrained(self.tmpdirname,		**_lowerCamelCase  )






       def      lowercase__     (							self,		lowerCAmelCase  ):



              """simple docstring"""







              lowerCamelCase_							='''<unk> UNwanted , running'''
              lowerCamelCase_							='''<unk> unwanted, running'''
              return input_text, output_text






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=TransfoXLTokenizer(vocab_file=self.vocab_file,		lower_case=_lowerCamelCase  )

              lowerCamelCase_							=tokenizer.tokenize('''<unk> UNwanted , running'''  )
              self.assertListEqual(_lowerCamelCase,		['''<unk>''', '''unwanted''', ''',''', '''running''']  )

              self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase  ),		[0, 4, 8, 7]  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=TransfoXLTokenizer(lower_case=_lowerCamelCase  )

              self.assertListEqual(
                  tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''  ),		['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=TransfoXLTokenizer(lower_case=_lowerCamelCase  )

              self.assertListEqual(
                  tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''  ),		['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=TransfoXLTokenizer(lower_case=_lowerCamelCase  )
              lowerCamelCase_							='''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
              lowerCamelCase_							=[
                  '''Hello''',
                  '''(''',
                  '''bracket''',
                  ''')''',
                  '''and''',
                  '''side''',
                  '''@-@''',
                  '''scrolled''',
                  '''[''',
                  '''and''',
                  ''']''',
                  '''Henry''',
                  '''\'s''',
                  '''$''',
                  '''5''',
                  '''@,@''',
                  '''000''',
                  '''with''',
                  '''3''',
                  '''@.@''',
                  '''34''',
                  '''m''',
                  '''.''',
                  '''What''',
                  '''\'s''',
                  '''up''',
                  '''!''',
                  '''?''',
              ]

              self.assertListEqual(tokenizer.tokenize(_lowerCamelCase  ),		_lowerCamelCase  )

              self.assertEqual(tokenizer.convert_tokens_to_string(_lowerCamelCase  ),		_lowerCamelCase  )






       def      lowercase__     (							self  ):



              """simple docstring"""







              lowerCamelCase_							=self.get_tokenizer()
              lowerCamelCase_							=len(_lowerCamelCase  )

              tokenizer.add_tokens(['''new1''', '''new2''']  )
              tokenizer.move_added_token('''new1''',		1  )

              # Check that moved token is not copied (duplicate)
              self.assertEqual(len(_lowerCamelCase  ),		original_len + 2  )
              # Check that token is moved to specified id
              self.assertEqual(tokenizer.encode('''new1'''  ),		[1]  )
              self.assertEqual(tokenizer.decode([1]  ),		'''new1'''  )












"""simple docstring"""



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class        a     (   a_	):
    UpperCAmelCase_ :     List[Any]	     =["image_processor", "tokenizer"]
    UpperCAmelCase_ :     str	     ="AutoImageProcessor"
    UpperCAmelCase_ :     Any	     ="AutoTokenizer"







    def __init__(			self							,		_lowerCamelCase=None							,		_lowerCamelCase=None							,		**_lowerCamelCase       ):
      lowercase							    =       None
      if "feature_extractor" in kwargs:
        warnings.warn(
            'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
            ' instead.'							,		_lowerCamelCase							,		)
        lowercase							    =       kwargs.pop('feature_extractor'       )

      lowercase							    =       image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
        raise ValueError('You need to specify an `image_processor`.'       )
      if tokenizer is None:
        raise ValueError('You need to specify a `tokenizer`.'       )

      super().__init__(_lowerCamelCase							,		_lowerCamelCase       )
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def __call__(			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      # For backward compatibility
      if self._in_target_context_manager:
        return self.current_processor(*_lowerCamelCase							,		**_lowerCamelCase       )

      lowercase							    =       kwargs.pop('images'							,		_lowerCamelCase       )
      lowercase							    =       kwargs.pop('text'							,		_lowerCamelCase       )
      if len(_lowerCamelCase       ) > 0:
        lowercase							    =       args[0]
        lowercase							    =       args[1:]

      if images is None and text is None:
        raise ValueError('You need to specify either an `images` or `text` input to process.'       )

      if images is not None:
        lowercase							    =       self.image_processor(_lowerCamelCase							,		*_lowerCamelCase							,		**_lowerCamelCase       )
      if text is not None:
        lowercase							    =       self.tokenizer(_lowerCamelCase							,		**_lowerCamelCase       )

      if text is None:
        return inputs
      elif images is None:
        return encodings
      else:
        lowercase							    =       encodings['input_ids']
        return inputs







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.batch_decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    def 			UpperCamelCase_      (			self							,		*_lowerCamelCase							,		**_lowerCamelCase       ):
      return self.tokenizer.decode(*_lowerCamelCase							,		**_lowerCamelCase       )







    @contextmanager
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
          'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
          'your images inputs, or in a separate call.'       )
      lowercase							    =       True
      lowercase							    =       self.tokenizer
      yield
      lowercase							    =       self.image_processor
      lowercase							    =       False







    def 			UpperCamelCase_      (			self							,		_lowerCamelCase							,		_lowerCamelCase=False							,		_lowerCamelCase=None       ):
      if added_vocab is None:
        lowercase							    =       self.tokenizer.get_added_vocab()

      lowercase							    =       {}

      while tokens:
        lowercase							    =       re.search(R'<s_(.*?)>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        if start_token is None:
          break
        lowercase							    =       start_token.group(1       )
        lowercase							    =       re.search(RF'</s_{key}>'							,		_lowerCamelCase							,		re.IGNORECASE       )
        lowercase							    =       start_token.group()
        if end_token is None:
          lowercase							    =       tokens.replace(_lowerCamelCase							,		''       )
        else:
          lowercase							    =       end_token.group()
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.escape(_lowerCamelCase       )
          lowercase							    =       re.search(F'{start_token_escaped}(.*?){end_token_escaped}'							,		_lowerCamelCase							,		re.IGNORECASE       )
          if content is not None:
            lowercase							    =       content.group(1       ).strip()
            if r"<s_" in content and r"</s_" in content:  # non-leaf node
              lowercase							    =       self.tokenajson(_lowerCamelCase							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )
              if value:
                if len(_lowerCamelCase       ) == 1:
                  lowercase							    =       value[0]
                lowercase							    =       value
            else:  # leaf nodes
              lowercase							    =       []
              for leaf in content.split(R'<sep/>'       ):
                lowercase							    =       leaf.strip()
                if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
                  lowercase							    =       leaf[1:-2]  # for categorical special tokens
                output[key].append(_lowerCamelCase       )
              if len(output[key]       ) == 1:
                lowercase							    =       output[key][0]

          lowercase							    =       tokens[tokens.find(_lowerCamelCase       ) + len(_lowerCamelCase       ) :].strip()
          if tokens[:6] == r"<sep/>":  # non-leaf nodes
            return [output] + self.tokenajson(tokens[6:]							,		is_inner_value=_lowerCamelCase							,		added_vocab=_lowerCamelCase       )

      if len(_lowerCamelCase       ):
        return [output] if is_inner_value else output
      else:
        return [] if is_inner_value else {"text_sequence": tokens}







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor_class







    @property
    def 			UpperCamelCase_      (			self       ):
      warnings.warn(
          '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'							,		_lowerCamelCase							,		)
      return self.image_processor


