code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers.testing_utils import require_vision
  from transformers.utils import is_vision_available
  
  
  if is_vision_available():
         from PIL import Image
  
         from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast
  
  
  
  
  
  
  @require_vision
  class 						lowercase     (   unittest.TestCase):
  
  
  
  
  
  
        def    A__(self					)       ->   Any:
           SCREAMING_SNAKE_CASE__			:				int       						= tempfile.mkdtemp()
  
           SCREAMING_SNAKE_CASE__			:				Any       						= CLIPImageProcessor()
           SCREAMING_SNAKE_CASE__			:				List[str]       						= BertTokenizer.from_pretrained(
               '''hf-internal-testing/tiny-random-BertModel''' ,  model_input_names=['''input_ids''', '''attention_mask''']					)
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor(a__ ,  a__					)
  
           processor.save_pretrained(self.tmpdirname					)
  
  
  
  
  
  
        def    A__(self ,  **a__					)       ->   int:
           return AutoProcessor.from_pretrained(self.tmpdirname ,  **a__					).tokenizer
  
  
  
  
  
  
        def    A__(self ,  **a__					)       ->   Optional[int]:
           return AutoProcessor.from_pretrained(self.tmpdirname ,  **a__					).image_processor
  
  
  
  
  
  
        def    A__(self					)       ->   Tuple:
           shutil.rmtree(self.tmpdirname					)
  
  
  
  
  
  
        def    A__(self					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				str       						= [np.random.randint(2_5_5 ,  size=(3, 3_0, 4_0_0) ,  dtype=np.uinta					)]
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [Image.fromarray(np.moveaxis(a__ ,  0 ,  -1					)					) for x in image_inputs]
  
           return image_inputs
  
  
  
  
  
  
        def    A__(self					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				Tuple       						= GitProcessor(tokenizer=self.get_tokenizer() ,  image_processor=self.get_image_processor()					)
           processor.save_pretrained(self.tmpdirname					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.get_tokenizer(bos_token='''(BOS)''' ,  eos_token='''(EOS)'''					)
           SCREAMING_SNAKE_CASE__			:				Any       						= self.get_image_processor(do_normalize=a__ ,  padding_value=1.0					)
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor.from_pretrained(
               self.tmpdirname ,  bos_token='''(BOS)''' ,  eos_token='''(EOS)''' ,  do_normalize=a__ ,  padding_value=1.0					)
  
           self.assertEqual(processor.tokenizer.get_vocab() ,  tokenizer_add_kwargs.get_vocab()					)
           self.assertIsInstance(processor.tokenizer ,  a__					)
  
           self.assertEqual(processor.image_processor.to_json_string() ,  image_processor_add_kwargs.to_json_string()					)
           self.assertIsInstance(processor.image_processor ,  a__					)
  
  
  
  
  
  
        def    A__(self					)       ->   Optional[Any]:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.get_image_processor()
           SCREAMING_SNAKE_CASE__			:				Any       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				Any       						= self.prepare_image_inputs()
  
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= image_processor(a__ ,  return_tensors='''np'''					)
           SCREAMING_SNAKE_CASE__			:				int       						= processor(images=a__ ,  return_tensors='''np'''					)
  
           for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum() ,  input_processor[key].sum() ,  delta=1E-2					)
  
  
  
  
  
  
        def    A__(self					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				Dict       						= self.get_image_processor()
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				str       						= '''lower newer'''
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= processor(text=a__					)
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= tokenizer(a__ ,  return_token_type_ids=a__					)
  
           for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key] ,  encoded_processor[key]					)
  
  
  
  
  
  
        def    A__(self					)       ->   List[str]:
           SCREAMING_SNAKE_CASE__			:				Union[str, Any]       						= self.get_image_processor()
           SCREAMING_SNAKE_CASE__			:				List[str]       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''lower newer'''
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= self.prepare_image_inputs()
  
           SCREAMING_SNAKE_CASE__			:				List[str]       						= processor(text=a__ ,  images=a__					)
  
           self.assertListEqual(list(inputs.keys()					) ,  ['''input_ids''', '''attention_mask''', '''pixel_values''']					)
  
           # test if it raises when no input is passed
           with pytest.raises(a__					):
              processor()
  
  
  
  
  
  
        def    A__(self					)       ->   str:
           SCREAMING_SNAKE_CASE__			:				int       						= self.get_image_processor()
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				Any       						= [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
           SCREAMING_SNAKE_CASE__			:				str       						= processor.batch_decode(a__					)
           SCREAMING_SNAKE_CASE__			:				str       						= tokenizer.batch_decode(a__					)
  
           self.assertListEqual(a__ ,  a__					)
  
  
  
  
  
  
        def    A__(self					)       ->   List[Any]:
           SCREAMING_SNAKE_CASE__			:				Any       						= self.get_image_processor()
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= self.get_tokenizer()
  
           SCREAMING_SNAKE_CASE__			:				int       						= GitProcessor(tokenizer=a__ ,  image_processor=a__					)
  
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= '''lower newer'''
           SCREAMING_SNAKE_CASE__			:				Any       						= self.prepare_image_inputs()
  
           SCREAMING_SNAKE_CASE__			:				int       						= processor(text=a__ ,  images=a__					)
  
           # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
           self.assertListEqual(list(inputs.keys()					) ,  ['''input_ids''', '''attention_mask''', '''pixel_values''']					)
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_splinter import SplinterTokenizer
  
  
  lowercase__			=	logging.get_logger(__name__)
  
  lowercase__			=	{'''vocab_file''': '''vocab.txt'''}
  
  lowercase__			=	{
      '''vocab_file''': {
          '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt''',
          '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt''',
          '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt''',
          '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt''',
      }
  }
  
  lowercase__			=	{
      '''tau/splinter-base''': 5_1_2,
      '''tau/splinter-base-qass''': 5_1_2,
      '''tau/splinter-large''': 5_1_2,
      '''tau/splinter-large-qass''': 5_1_2,
  }
  
  lowercase__			=	{
      '''tau/splinter-base''': {'''do_lower_case''': False},
      '''tau/splinter-base-qass''': {'''do_lower_case''': False},
      '''tau/splinter-large''': {'''do_lower_case''': False},
      '''tau/splinter-large-qass''': {'''do_lower_case''': False},
  }
  
  
  
  
  
  
  class 						lowercase     (   UpperCAmelCase_):
        __lowercase 		=  VOCAB_FILES_NAMES
        __lowercase 		=  PRETRAINED_VOCAB_FILES_MAP
        __lowercase 		=  PRETRAINED_INIT_CONFIGURATION
        __lowercase 		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
        __lowercase 		=  SplinterTokenizer
  
  
  
  
  
  
        def __init__(self ,  a__=None ,  a__=None ,  a__=True ,  a__="[UNK]" ,  a__="[SEP]" ,  a__="[PAD]" ,  a__="[CLS]" ,  a__="[MASK]" ,  a__="[QUESTION]" ,  a__=True ,  a__=None ,  **a__ ,  )       ->   Any:
           super().__init__(
               a__ ,  tokenizer_file=a__ ,  do_lower_case=a__ ,  unk_token=a__ ,  sep_token=a__ ,  pad_token=a__ ,  cls_token=a__ ,  mask_token=a__ ,  tokenize_chinese_chars=a__ ,  strip_accents=a__ ,  additional_special_tokens=(question_token,) ,  **a__ ,  )
  
           SCREAMING_SNAKE_CASE__			:				Tuple       						= json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
           if (
               pre_tok_state.get('''lowercase''' ,  a__					) != do_lower_case
               or pre_tok_state.get('''strip_accents''' ,  a__					) != strip_accents
           ):
              SCREAMING_SNAKE_CASE__			:				List[Any]       						= getattr(a__ ,  pre_tok_state.pop('''type'''					)					)
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case
              SCREAMING_SNAKE_CASE__			:				Optional[int]       						= strip_accents
              SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= pre_tok_class(**a__					)
  
           SCREAMING_SNAKE_CASE__			:				Optional[int]       						= do_lower_case
  
  
  
  
  
  
        @property
        def    A__(self					)       ->   List[str]:
           return self.convert_tokens_to_ids(self.question_token					)
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  
           SCREAMING_SNAKE_CASE__			:				Dict       						= [self.cls_token_id]
           SCREAMING_SNAKE_CASE__			:				Tuple       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				List[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
           if self.padding_side == "right":
              # Input is question-then-context
              return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
           else:
              # Input is context-then-question
              return cls + token_ids_a + sep + token_ids_a + question_suffix + sep
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   List[int]:
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.sep_token_id]
           SCREAMING_SNAKE_CASE__			:				Any       						= [self.cls_token_id]
           SCREAMING_SNAKE_CASE__			:				Optional[Any]       						= [self.question_token_id] + [self.convert_tokens_to_ids('''.'''					)]
           if token_ids_a is None:
              return len(cls + token_ids_a + sep					) * [0]
  
           if self.padding_side == "right":
              # Input is question-then-context
              return len(cls + token_ids_a + question_suffix + sep					) * [0] + len(token_ids_a + sep					) * [1]
           else:
              # Input is context-then-question
              return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + question_suffix + sep					) * [1]
  
  
  
  
  
  
        def    A__(self ,  a__ ,  a__ = None					)       ->   Tuple[str]:
           SCREAMING_SNAKE_CASE__			:				int       						= self._tokenizer.model.save(a__ ,  name=a__					)
           return tuple(a__					)
  
  