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 			UpperCAmelCase_						(       unittest.TestCase		):
  
  
  
        def 		snake_case_      (  self  :			int		):
             _UpperCAmelCase    :			Optional[Any]              =					tempfile.mkdtemp()
  
             _UpperCAmelCase    :			Optional[int]              =					CLIPImageProcessor()
             _UpperCAmelCase    :			int              =					BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"  ,			model_input_names=["input_ids", "attention_mask"]		)
  
             _UpperCAmelCase    :			str              =					GitProcessor(A  ,			A		)
  
             processor.save_pretrained(self.tmpdirname		)
  
  
  
        def 		snake_case_      (  self  :			Dict  ,			**A  :			Dict		):
             return AutoProcessor.from_pretrained(self.tmpdirname  ,			**A		).tokenizer
  
  
  
        def 		snake_case_      (  self  :			List[Any]  ,			**A  :			Union[str, Any]		):
             return AutoProcessor.from_pretrained(self.tmpdirname  ,			**A		).image_processor
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             shutil.rmtree(self.tmpdirname		)
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			List[Any]              =					[np.random.randint(2_5_5  ,			size=(3, 3_0, 4_0_0)  ,			dtype=np.uinta		)]
  
             _UpperCAmelCase    :			Optional[Any]              =					[Image.fromarray(np.moveaxis(A  ,			0  ,			-1		)		) for x in image_inputs]
  
             return image_inputs
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			Optional[Any]              =					GitProcessor(tokenizer=self.get_tokenizer()  ,			image_processor=self.get_image_processor()		)
             processor.save_pretrained(self.tmpdirname		)
  
             _UpperCAmelCase    :			Tuple              =					self.get_tokenizer(bos_token="(BOS)"  ,			eos_token="(EOS)"		)
             _UpperCAmelCase    :			Tuple              =					self.get_image_processor(do_normalize=A  ,			padding_value=1.0		)
  
             _UpperCAmelCase    :			Optional[int]              =					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 		snake_case_      (  self  :			str		):
             _UpperCAmelCase    :			str              =					self.get_image_processor()
             _UpperCAmelCase    :			int              =					self.get_tokenizer()
  
             _UpperCAmelCase    :			str              =					GitProcessor(tokenizer=A  ,			image_processor=A		)
  
             _UpperCAmelCase    :			Union[str, Any]              =					self.prepare_image_inputs()
  
             _UpperCAmelCase    :			List[str]              =					image_processor(A  ,			return_tensors="np"		)
             _UpperCAmelCase    :			List[Any]              =					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 		snake_case_      (  self  :			Union[str, Any]		):
             _UpperCAmelCase    :			Dict              =					self.get_image_processor()
             _UpperCAmelCase    :			Optional[Any]              =					self.get_tokenizer()
  
             _UpperCAmelCase    :			Tuple              =					GitProcessor(tokenizer=A  ,			image_processor=A		)
  
             _UpperCAmelCase    :			Any              =					"lower newer"
  
             _UpperCAmelCase    :			Optional[int]              =					processor(text=A		)
  
             _UpperCAmelCase    :			int              =					tokenizer(A  ,			return_token_type_ids=A		)
  
             for key in encoded_tok.keys():
                  self.assertListEqual(encoded_tok[key]  ,			encoded_processor[key]		)
  
  
  
        def 		snake_case_      (  self  :			Tuple		):
             _UpperCAmelCase    :			str              =					self.get_image_processor()
             _UpperCAmelCase    :			Any              =					self.get_tokenizer()
  
             _UpperCAmelCase    :			int              =					GitProcessor(tokenizer=A  ,			image_processor=A		)
  
             _UpperCAmelCase    :			int              =					"lower newer"
             _UpperCAmelCase    :			Any              =					self.prepare_image_inputs()
  
             _UpperCAmelCase    :			Union[str, Any]              =					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 		snake_case_      (  self  :			List[str]		):
             _UpperCAmelCase    :			Dict              =					self.get_image_processor()
             _UpperCAmelCase    :			Optional[Any]              =					self.get_tokenizer()
  
             _UpperCAmelCase    :			Optional[Any]              =					GitProcessor(tokenizer=A  ,			image_processor=A		)
  
             _UpperCAmelCase    :			Optional[int]              =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
             _UpperCAmelCase    :			Union[str, Any]              =					processor.batch_decode(A		)
             _UpperCAmelCase    :			Dict              =					tokenizer.batch_decode(A		)
  
             self.assertListEqual(A  ,			A		)
  
  
  
        def 		snake_case_      (  self  :			Dict		):
             _UpperCAmelCase    :			Union[str, Any]              =					self.get_image_processor()
             _UpperCAmelCase    :			int              =					self.get_tokenizer()
  
             _UpperCAmelCase    :			int              =					GitProcessor(tokenizer=A  ,			image_processor=A		)
  
             _UpperCAmelCase    :			Dict              =					"lower newer"
             _UpperCAmelCase    :			Dict              =					self.prepare_image_inputs()
  
             _UpperCAmelCase    :			Tuple              =					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"""
  
  
  
  
  _lowerCAmelCase       :      str							=							{
      "0": 1,
      "1": 1,
      "2": 2,
      "3": 6,
      "4": 24,
      "5": 1_20,
      "6": 7_20,
      "7": 50_40,
      "8": 4_03_20,
      "9": 36_28_80,
  }
  
  _lowerCAmelCase       :      Optional[int]							=							{1_45: 1_45}
  
  _lowerCAmelCase       :      int							=							{
      1_45: 0,
      1_69: 3,
      3_63_01: 3,
      14_54: 3,
      8_71: 2,
      4_53_61: 2,
      8_72: 2,
  }
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       if n in CACHE_SUM_DIGIT_FACTORIALS:
            return CACHE_SUM_DIGIT_FACTORIALS[n]
       _UpperCAmelCase    :			Dict              =					sum(DIGIT_FACTORIALS[let] for let in str(SCREAMING_SNAKE_CASE__       )       )
       _UpperCAmelCase    :			Any              =					ret
       return ret
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int		,						SCREAMING_SNAKE_CASE__			:		set | None = None       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       _UpperCAmelCase    :			Dict              =					previous or set()
       if n in CHAIN_LENGTH_CACHE:
            return CHAIN_LENGTH_CACHE[n]
       _UpperCAmelCase    :			Dict              =					sum_digit_factorials(SCREAMING_SNAKE_CASE__       )
       if next_number in previous:
            _UpperCAmelCase    :			List[str]              =					0
            return 0
       else:
            previous.add(SCREAMING_SNAKE_CASE__       )
            _UpperCAmelCase    :			str              =					1 + chain_length(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__       )
            _UpperCAmelCase    :			str              =					ret
            return ret
  
  def 		__snake_case		(   SCREAMING_SNAKE_CASE__			:		int = 60		,						SCREAMING_SNAKE_CASE__			:		int = 1_000_000       )						->     int:
  
  
  
  
  
       '''simple docstring'''
  
       return sum(1 for i in range(1		,						SCREAMING_SNAKE_CASE__       ) if chain_length(SCREAMING_SNAKE_CASE__       ) == num_terms       )
  
  
  if __name__ == "__main__":
   print(F"{solution() = }")
  