
"""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						):
 """simple docstring"""




 def   a      ( self       : List[Any] )->					Tuple:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  tempfile.mkdtemp()

      UpperCAmelCase_						:    Optional[Any]    					=  CLIPImageProcessor()
      UpperCAmelCase_						:    List[str]    					=  BertTokenizer.from_pretrained(
          """hf-internal-testing/tiny-random-BertModel"""     ,   model_input_names=["""input_ids""", """attention_mask"""] )

      UpperCAmelCase_						:    List[Any]    					=  GitProcessor(a_     ,   a_ )

      processor.save_pretrained(self.tmpdirname )




 def   a      ( self       : Optional[Any]     ,   **a_       : str )->					Union[str, Any]:



      """simple docstring"""

      return AutoProcessor.from_pretrained(self.tmpdirname     ,   **a_ ).tokenizer




 def   a      ( self       : List[Any]     ,   **a_       : Union[str, Any] )->					List[str]:



      """simple docstring"""

      return AutoProcessor.from_pretrained(self.tmpdirname     ,   **a_ ).image_processor




 def   a      ( self       : str )->					List[str]:



      """simple docstring"""

      shutil.rmtree(self.tmpdirname )




 def   a      ( self       : Optional[Any] )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Any    					=  [np.random.randint(2_55     ,   size=(3, 30, 4_00)     ,   dtype=np.uinta )]

      UpperCAmelCase_						:    str    					=  [Image.fromarray(np.moveaxis(a_     ,   0     ,   -1 ) ) for x in image_inputs]

      return image_inputs




 def   a      ( self       : List[Any] )->					Union[str, Any]:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  GitProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor() )
      processor.save_pretrained(self.tmpdirname )

      UpperCAmelCase_						:    List[Any]    					=  self.get_tokenizer(bos_token="""(BOS)"""     ,   eos_token="""(EOS)""" )
      UpperCAmelCase_						:    List[Any]    					=  self.get_image_processor(do_normalize=a_     ,   padding_value=1.0 )

      UpperCAmelCase_						:    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   a      ( self       : Dict )->					int:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  self.get_image_processor()
      UpperCAmelCase_						:    Tuple    					=  self.get_tokenizer()

      UpperCAmelCase_						:    Dict    					=  GitProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    List[str]    					=  self.prepare_image_inputs()

      UpperCAmelCase_						:    Optional[Any]    					=  image_processor(a_     ,   return_tensors="""np""" )
      UpperCAmelCase_						:    Optional[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       : Optional[Any] )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  self.get_image_processor()
      UpperCAmelCase_						:    Union[str, Any]    					=  self.get_tokenizer()

      UpperCAmelCase_						:    List[Any]    					=  GitProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    int    					=  """lower newer"""

      UpperCAmelCase_						:    Union[str, Any]    					=  processor(text=a_ )

      UpperCAmelCase_						:    Union[str, Any]    					=  tokenizer(a_     ,   return_token_type_ids=a_ )

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]     ,   encoded_processor[key] )




 def   a      ( self       : Tuple )->					str:



      """simple docstring"""

      UpperCAmelCase_						:    List[str]    					=  self.get_image_processor()
      UpperCAmelCase_						:    List[Any]    					=  self.get_tokenizer()

      UpperCAmelCase_						:    Optional[Any]    					=  GitProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    Dict    					=  """lower newer"""
      UpperCAmelCase_						:    List[Any]    					=  self.prepare_image_inputs()

      UpperCAmelCase_						:    Optional[int]    					=  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       : int )->					Optional[Any]:



      """simple docstring"""

      UpperCAmelCase_						:    List[Any]    					=  self.get_image_processor()
      UpperCAmelCase_						:    Optional[int]    					=  self.get_tokenizer()

      UpperCAmelCase_						:    Dict    					=  GitProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    Optional[int]    					=  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      UpperCAmelCase_						:    str    					=  processor.batch_decode(a_ )
      UpperCAmelCase_						:    Tuple    					=  tokenizer.batch_decode(a_ )

      self.assertListEqual(a_     ,   a_ )






 def   a      ( self       : int )->					List[str]:



      """simple docstring"""

      UpperCAmelCase_						:    Optional[Any]    					=  self.get_image_processor()
      UpperCAmelCase_						:    str    					=  self.get_tokenizer()

      UpperCAmelCase_						:    Tuple    					=  GitProcessor(tokenizer=a_     ,   image_processor=a_ )

      UpperCAmelCase_						:    List[str]    					=  """lower newer"""
      UpperCAmelCase_						:    str    					=  self.prepare_image_inputs()

      UpperCAmelCase_						:    List[Any]    					=  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"""] )



"""simple docstring"""







def 			A_			(					lowercase = 99_8001      )     ->							int:


     """simple docstring"""







     for number in range(n - 1						, 9999						, -1      ):
          UpperCAmelCase_						:    Union[str, Any]    					=  str(lowercase      )

          # checks whether 'str_number' is a palindrome.
          if str_number == str_number[::-1]:
               UpperCAmelCase_						:    List[str]    					=  999

               # 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() = }""")

