





"""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, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast

@require_vision
class 		UpperCamelCase__			(				unittest.TestCase       ):



     '''simple docstring'''

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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						tempfile.mkdtemp()

           SCREAMING_SNAKE_CASE		=						BlipImageProcessor()
           SCREAMING_SNAKE_CASE		=						BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""	)

           SCREAMING_SNAKE_CASE		=						BlipProcessor(__lowerCamelCase						,__lowerCamelCase	)

           processor.save_pretrained(self.tmpdirname	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,**lowerCamelCase__						:   List[Any]	)					->		Any:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]						,**lowerCamelCase__						:   List[Any]	)					->		Optional[int]:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Union[str, Any]	)					->		Optional[int]:




           '''simple docstring'''


           shutil.rmtree(self.tmpdirname	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   int	)					->		Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						[np.random.randint(255						,size=(3, 30, 400)						,dtype=np.uinta	)]

           SCREAMING_SNAKE_CASE		=						[Image.fromarray(np.moveaxis(__lowerCamelCase						,0						,-1	)	) for x in image_inputs]

           return image_inputs






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=self.get_tokenizer()						,image_processor=self.get_image_processor()	)
           processor.save_pretrained(self.tmpdirname	)

           SCREAMING_SNAKE_CASE		=						self.get_tokenizer(bos_token="""(BOS)"""						,eos_token="""(EOS)"""	)
           SCREAMING_SNAKE_CASE		=						self.get_image_processor(do_normalize=__lowerCamelCase						,padding_value=1.0	)

           SCREAMING_SNAKE_CASE		=						BlipProcessor.from_pretrained(
               self.tmpdirname						,bos_token="""(BOS)"""						,eos_token="""(EOS)"""						,do_normalize=__lowerCamelCase						,padding_value=1.0	)

           self.assertEqual(processor.tokenizer.get_vocab()						,tokenizer_add_kwargs.get_vocab()	)
           self.assertIsInstance(processor.tokenizer						,__lowerCamelCase	)

           self.assertEqual(processor.image_processor.to_json_string()						,image_processor_add_kwargs.to_json_string()	)
           self.assertIsInstance(processor.image_processor						,__lowerCamelCase	)






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=__lowerCamelCase						,image_processor=__lowerCamelCase	)

           SCREAMING_SNAKE_CASE		=						self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE		=						image_processor(__lowerCamelCase						,return_tensors="""np"""	)
           SCREAMING_SNAKE_CASE		=						processor(images=__lowerCamelCase						,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     SCREAMING_SNAKE_CASE__  (							self						:   Optional[Any]	)					->		Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=__lowerCamelCase						,image_processor=__lowerCamelCase	)

           SCREAMING_SNAKE_CASE		=						"""lower newer"""

           SCREAMING_SNAKE_CASE		=						processor(text=__lowerCamelCase	)

           SCREAMING_SNAKE_CASE		=						tokenizer(__lowerCamelCase						,return_token_type_ids=__lowerCamelCase	)

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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Any	)					->		List[str]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=__lowerCamelCase						,image_processor=__lowerCamelCase	)

           SCREAMING_SNAKE_CASE		=						"""lower newer"""
           SCREAMING_SNAKE_CASE		=						self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE		=						processor(text=__lowerCamelCase						,images=__lowerCamelCase	)

           self.assertListEqual(list(inputs.keys()	)						,["""pixel_values""", """input_ids""", """attention_mask"""]	)

           # test if it raises when no input is passed
           with pytest.raises(__lowerCamelCase	):
                 processor()






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=__lowerCamelCase						,image_processor=__lowerCamelCase	)

           SCREAMING_SNAKE_CASE		=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           SCREAMING_SNAKE_CASE		=						processor.batch_decode(__lowerCamelCase	)
           SCREAMING_SNAKE_CASE		=						tokenizer.batch_decode(__lowerCamelCase	)

           self.assertListEqual(__lowerCamelCase						,__lowerCamelCase	)






     def     SCREAMING_SNAKE_CASE__  (							self						:   Optional[int]	)					->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.get_image_processor()
           SCREAMING_SNAKE_CASE		=						self.get_tokenizer()

           SCREAMING_SNAKE_CASE		=						BlipProcessor(tokenizer=__lowerCamelCase						,image_processor=__lowerCamelCase	)

           SCREAMING_SNAKE_CASE		=						"""lower newer"""
           SCREAMING_SNAKE_CASE		=						self.prepare_image_inputs()

           SCREAMING_SNAKE_CASE		=						processor(text=__lowerCamelCase						,images=__lowerCamelCase	)

           # For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
           self.assertListEqual(list(inputs.keys()	)						,["""pixel_values""", """input_ids""", """attention_mask"""]	)









def        __lowercase       (				_SCREAMING_SNAKE_CASE = 50     )			->       int:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						[1] * (length + 1)

      for row_length in range(3			,						length + 1     ):
            for block_length in range(3			,						row_length + 1     ):
                  for block_start in range(row_length - block_length     ):
                        ways_number[row_length] += ways_number[
                            row_length - block_start - block_length - 1
                        ]

                  ways_number[row_length] += 1

      return ways_number[length]


if __name__ == "__main__":
      print(F'''{solution() = }''')


