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'''
  
  
  
  
  
  def 				UpperCamelCase	(  snake_case = 99_80_01   ):
  
  
  
  
     '''simple docstring'''
  
     for number in range(n - 1	,     99_99	,     -1   ):
        SCREAMING_SNAKE_CASE__			:				int       						= str(snake_case   )
  
        # checks whether 'str_number' is a palindrome.
        if str_number == str_number[::-1]:
           SCREAMING_SNAKE_CASE__			:				Any       						= 9_99
  
           # if 'number' is a product of two 3-digit numbers
           # then number is the answer otherwise fetch next number.
           while divisor != 99:
              if (number % divisor == 0) and (len(str(number // divisor   )   ) == 3.0):
                 return number
              divisor -= 1
     raise ValueError('''That number is larger than our acceptable range.'''   )
  
  
  if __name__ == "__main__":
         print(F"{solution() = }")
  
  