

import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class   UpperCamelCase			(  UpperCAmelCase_						,      unittest.TestCase ):
    __UpperCamelCase    =RobertaTokenizer
    __UpperCamelCase    =RobertaTokenizerFast
    __UpperCamelCase    =True
    __UpperCamelCase    ={'cls_token': '<s>'}

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     super().setUp()

     # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
     SCREAMING_SNAKE_CASE		    =      [
         """l""",
         """o""",
         """w""",
         """e""",
         """r""",
         """s""",
         """t""",
         """i""",
         """d""",
         """n""",
         """\u0120""",
         """\u0120l""",
         """\u0120n""",
         """\u0120lo""",
         """\u0120low""",
         """er""",
         """\u0120lowest""",
         """\u0120newer""",
         """\u0120wider""",
         """<unk>""",
     ]
     SCREAMING_SNAKE_CASE		    =      dict(zip(SCREAMING_SNAKE_CASE_				,     range(len(SCREAMING_SNAKE_CASE_     )     )     )     )
     SCREAMING_SNAKE_CASE		    =      ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
     SCREAMING_SNAKE_CASE		    =      {"""unk_token""": """<unk>"""}

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

    def UpperCamelCase      (      self			:   Tuple				,     **snake_case__			:   List[Any]     ):

     """simple docstring"""






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

    def UpperCamelCase      (      self			:   List[Any]				,     **snake_case__			:   List[Any]     ):

     """simple docstring"""






     kwargs.update(self.special_tokens_map     )
     return RobertaTokenizerFast.from_pretrained(self.tmpdirname				,     **SCREAMING_SNAKE_CASE_     )

    def UpperCamelCase      (      self			:   Any				,     snake_case__			:   Dict     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      """lower newer"""
     SCREAMING_SNAKE_CASE		    =      """lower newer"""
     return input_text, output_text

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer_class(self.vocab_file				,     self.merges_file				,     **self.special_tokens_map     )
     SCREAMING_SNAKE_CASE		    =      """lower newer"""
     SCREAMING_SNAKE_CASE		    =      ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
     SCREAMING_SNAKE_CASE		    =      tokenizer.tokenize(SCREAMING_SNAKE_CASE_     )  # , add_prefix_space=True)
     self.assertListEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_     )

     SCREAMING_SNAKE_CASE		    =      tokens + [tokenizer.unk_token]
     SCREAMING_SNAKE_CASE		    =      [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
     self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_     )				,     SCREAMING_SNAKE_CASE_     )

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizer()

     self.assertListEqual(tokenizer.encode('Hello world!'				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )				,     [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2]     )
     self.assertListEqual(
         tokenizer.encode('Hello world! cécé herlolip 418'				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )				,     [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]				,     )

    @slow
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.tokenizer_class.from_pretrained('roberta-base'     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.encode('sequence builders'				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.encode('multi-sequence build'				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(
         'sequence builders'				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(
         'sequence builders'				,     'multi-sequence build'				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_     )

     assert encoded_sentence == encoded_text_from_decode
     assert encoded_pair == encoded_pair_from_decode

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.get_tokenizer()

     SCREAMING_SNAKE_CASE		    =      """Encode this sequence."""
     SCREAMING_SNAKE_CASE		    =      tokenizer.byte_encoder[""" """.encode('utf-8'     )[0]]

     # Testing encoder arguments
     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
     self.assertNotEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
     self.assertEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_     )

     tokenizer.add_special_tokens({'bos_token': '<s>'}     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_ids_to_tokens(encoded[1]     )[0]
     self.assertNotEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_     )

     # Testing spaces after special tokens
     SCREAMING_SNAKE_CASE		    =      """<mask>"""
     tokenizer.add_special_tokens(
         {'mask_token': AddedToken(SCREAMING_SNAKE_CASE_				,     lstrip=SCREAMING_SNAKE_CASE_				,     rstrip=SCREAMING_SNAKE_CASE_     )}     )  # mask token has a left space
     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_     )

     SCREAMING_SNAKE_CASE		    =      """Encode <mask> sequence"""
     SCREAMING_SNAKE_CASE		    =      """Encode <mask>sequence"""

     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      encoded.index(SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
     self.assertEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_     )

     SCREAMING_SNAKE_CASE		    =      tokenizer.encode(SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      encoded.index(SCREAMING_SNAKE_CASE_     )
     SCREAMING_SNAKE_CASE		    =      tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
     self.assertNotEqual(SCREAMING_SNAKE_CASE_				,     SCREAMING_SNAKE_CASE_     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     pass

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_				,     **SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      """A, <mask> AllenNLP sentence."""
       SCREAMING_SNAKE_CASE		    =      tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     return_token_type_ids=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_				,     return_token_type_ids=SCREAMING_SNAKE_CASE_     )

       # 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']     )				,     )

       SCREAMING_SNAKE_CASE		    =      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']     )
       SCREAMING_SNAKE_CASE		    =      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, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )
       self.assertSequenceEqual(tokens_r['input_ids']				,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]     )

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

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     for trim_offsets, add_prefix_space in itertools.product([True, False]				,     repeat=2     ):
      SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
          self.tmpdirname				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )

      SCREAMING_SNAKE_CASE		    =      json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
      SCREAMING_SNAKE_CASE		    =      json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()     )

      self.assertEqual(pre_tokenizer_state['add_prefix_space']				,     SCREAMING_SNAKE_CASE_     )

      self.assertEqual(post_processor_state['add_prefix_space']				,     SCREAMING_SNAKE_CASE_     )
      self.assertEqual(post_processor_state['trim_offsets']				,     SCREAMING_SNAKE_CASE_     )




    def UpperCamelCase      (      self			:   Dict     ):

     """simple docstring"""






     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
      with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
       SCREAMING_SNAKE_CASE		    =      """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
       SCREAMING_SNAKE_CASE		    =      F"""{text_of_1_token} {text_of_1_token}"""

       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
           SCREAMING_SNAKE_CASE_				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_r(SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
       self.assertEqual(encoding.offset_mapping[0]				,     (0, len(SCREAMING_SNAKE_CASE_     ))     )
       self.assertEqual(
           encoding.offset_mapping[1]				,     (len(SCREAMING_SNAKE_CASE_     ) + 1, len(SCREAMING_SNAKE_CASE_     ) + 1 + len(SCREAMING_SNAKE_CASE_     ))				,     )

       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
           SCREAMING_SNAKE_CASE_				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_r(SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
       self.assertEqual(encoding.offset_mapping[0]				,     (0, len(SCREAMING_SNAKE_CASE_     ))     )
       self.assertEqual(
           encoding.offset_mapping[1]				,     (len(SCREAMING_SNAKE_CASE_     ) + 1, len(SCREAMING_SNAKE_CASE_     ) + 1 + len(SCREAMING_SNAKE_CASE_     ))				,     )

       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
           SCREAMING_SNAKE_CASE_				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_r(SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
       self.assertEqual(encoding.offset_mapping[0]				,     (0, len(SCREAMING_SNAKE_CASE_     ))     )
       self.assertEqual(
           encoding.offset_mapping[1]				,     (len(SCREAMING_SNAKE_CASE_     ), len(SCREAMING_SNAKE_CASE_     ) + 1 + len(SCREAMING_SNAKE_CASE_     ))				,     )

       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
           SCREAMING_SNAKE_CASE_				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_r(SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
       self.assertEqual(encoding.offset_mapping[0]				,     (0, len(SCREAMING_SNAKE_CASE_     ))     )
       self.assertEqual(
           encoding.offset_mapping[1]				,     (len(SCREAMING_SNAKE_CASE_     ), len(SCREAMING_SNAKE_CASE_     ) + 1 + len(SCREAMING_SNAKE_CASE_     ))				,     )

       SCREAMING_SNAKE_CASE		    =      F""" {text}"""

       # tokenizer_r = self.rust_tokenizer_class.from_pretrained(
       #     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
       # )
       # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
       # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
       # self.assertEqual(
       #     encoding.offset_mapping[1],
       #     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
       # )

       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
           SCREAMING_SNAKE_CASE_				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_r(SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
       self.assertEqual(encoding.offset_mapping[0]				,     (1, 1 + len(SCREAMING_SNAKE_CASE_     ))     )
       self.assertEqual(
           encoding.offset_mapping[1]				,     (1 + len(SCREAMING_SNAKE_CASE_     ) + 1, 1 + len(SCREAMING_SNAKE_CASE_     ) + 1 + len(SCREAMING_SNAKE_CASE_     ))				,     )

       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
           SCREAMING_SNAKE_CASE_				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_r(SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
       self.assertEqual(encoding.offset_mapping[0]				,     (0, 1 + len(SCREAMING_SNAKE_CASE_     ))     )
       self.assertEqual(
           encoding.offset_mapping[1]				,     (1 + len(SCREAMING_SNAKE_CASE_     ), 1 + len(SCREAMING_SNAKE_CASE_     ) + 1 + len(SCREAMING_SNAKE_CASE_     ))				,     )

       SCREAMING_SNAKE_CASE		    =      self.rust_tokenizer_class.from_pretrained(
           SCREAMING_SNAKE_CASE_				,     use_fast=SCREAMING_SNAKE_CASE_				,     add_prefix_space=SCREAMING_SNAKE_CASE_				,     trim_offsets=SCREAMING_SNAKE_CASE_     )
       SCREAMING_SNAKE_CASE		    =      tokenizer_r(SCREAMING_SNAKE_CASE_				,     return_offsets_mapping=SCREAMING_SNAKE_CASE_				,     add_special_tokens=SCREAMING_SNAKE_CASE_     )
       self.assertEqual(encoding.offset_mapping[0]				,     (0, 1 + len(SCREAMING_SNAKE_CASE_     ))     )
       self.assertEqual(
           encoding.offset_mapping[1]				,     (1 + len(SCREAMING_SNAKE_CASE_     ), 1 + len(SCREAMING_SNAKE_CASE_     ) + 1 + len(SCREAMING_SNAKE_CASE_     ))				,     )



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Any	   =      ['image_processor', 'tokenizer']
 __lowerCAmelCase	:  Union[str, Any]	   =      'AutoImageProcessor'
 __lowerCAmelCase	:  int	   =      'AutoTokenizer'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   super().__init__(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)
   lowercase__	: Union[str, Any]     		=   self.image_processor


 def __call__(       self       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   if text is None and images is None:
     raise ValueError("""You have to specify either text or images. Both cannot be none.""")

   if text is not None:
     lowercase__	: List[str]     		=   self.tokenizer(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if images is not None:
     lowercase__	: Optional[int]     		=   self.image_processor(SCREAMING_SNAKE_CASE_       ,	return_tensors=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)

   if text is not None and images is not None:
     lowercase__	: Union[str, Any]     		=   image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   else:
     return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE_)       ,	tensor_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self       ,	*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_)



 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   return ["input_ids", "attention_mask", "pixel_values"]






