code: 
  
  
  
  import unittest
  from pathlib import Path
  
  from transformers import is_vision_available, load_tool
  from transformers.testing_utils import get_tests_dir
  
  from .test_tools_common import ToolTesterMixin
  
  
  if is_vision_available():
   from PIL import Image
  
  
  
  
  
  
  class 			UpperCAmelCase_						(       unittest.TestCase ,    _UpperCamelCase		):
  
  
  
        def 		snake_case_      (  self  :			Any		):
             _UpperCAmelCase    :			List[str]              =					load_tool("image-question-answering"		)
             self.tool.setup()
             _UpperCAmelCase    :			str              =					load_tool("image-question-answering"  ,			remote=A		)
  
  
  
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Tuple              =					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"		)
             _UpperCAmelCase    :			int              =					self.tool(A  ,			"How many cats are sleeping on the couch?"		)
             self.assertEqual(A  ,			"2"		)
  
  
  
        def 		snake_case_      (  self  :			Optional[int]		):
             _UpperCAmelCase    :			int              =					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"		)
             _UpperCAmelCase    :			Optional[int]              =					self.remote_tool(A  ,			"How many cats are sleeping on the couch?"		)
             self.assertEqual(A  ,			"2"		)
  
  
  
        def 		snake_case_      (  self  :			Optional[Any]		):
             _UpperCAmelCase    :			List[Any]              =					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"		)
             _UpperCAmelCase    :			Optional[Any]              =					self.tool(image=A  ,			question="How many cats are sleeping on the couch?"		)
             self.assertEqual(A  ,			"2"		)
  
  
  
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			List[str]              =					Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"		)		) / "000000039769.png"		)
             _UpperCAmelCase    :			Tuple              =					self.remote_tool(image=A  ,			question="How many cats are sleeping on the couch?"		)
             self.assertEqual(A  ,			"2"		)
  
style_context: 
  
  
  
  """simple docstring"""
  
  
  
  
  from arguments import TokenizerTrainingArguments
  from datasets import load_dataset
  from tqdm import tqdm
  
  from transformers import AutoTokenizer, HfArgumentParser
  from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		str=10       )						->     List[Any]:
  
  
  
  
  
       '''simple docstring'''
  
       for _ in tqdm(range(0		,						args.n_examples		,						SCREAMING_SNAKE_CASE__       )       ):
            yield [next(SCREAMING_SNAKE_CASE__       )[args.text_column] for _ in range(SCREAMING_SNAKE_CASE__       )]
  
  
  # Configuration
  _lowerCAmelCase       :      Optional[Any]							=							HfArgumentParser(TokenizerTrainingArguments)
  _lowerCAmelCase       :      Dict							=							parser.parse_args()
  
  # Base tokenizer
  _lowerCAmelCase       :      int							=							AutoTokenizer.from_pretrained(args.base_tokenizer)
  _lowerCAmelCase       :      List[str]							=							list(bytes_to_unicode().values())
  
  # Load dataset
  _lowerCAmelCase       :      Dict							=							load_dataset(args.dataset_name, split="train", streaming=True)
  _lowerCAmelCase       :      List[Any]							=							iter(dataset)
  
  
  # Training and saving
  _lowerCAmelCase       :      List[str]							=							tokenizer.train_new_from_iterator(
      batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
  )
  new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
  