


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor





@require_tokenizers
@require_vision
class   UpperCAmelCase_    (unittest.TestCase    ):





    """simple docstring"""






    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      List[Any]														=						tempfile.mkdtemp()

         # fmt: off
         __lowerCamelCase  :      Any														=						['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
         # fmt: on
         __lowerCamelCase  :      List[Any]														=						os.path.join(self.tmpdirname     ,	VOCAB_FILES_NAMES['vocab_file']      )
         with open(self.vocab_file     ,	'w'     ,	encoding='utf-8'      ) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]      )      )

         __lowerCamelCase  :      Tuple														=						{
             'do_resize': True,
             'size': {'height': 18, 'width': 18},
             'do_normalize': True,
             'image_mean': [0.5, 0.5, 0.5],
             'image_std': [0.5, 0.5, 0.5],
         }
         __lowerCamelCase  :      Union[str, Any]														=						os.path.join(self.tmpdirname     ,	SCREAMING_SNAKE_CASE_      )
         with open(self.image_processor_file     ,	'w'     ,	encoding='utf-8'      ) as fp:
              json.dump(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Union[str, Any]:
         return BertTokenizer.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	**SCREAMING_SNAKE_CASE_      )							->	Optional[Any]:
         return ViTImageProcessor.from_pretrained(self.tmpdirname     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Optional[Any]:
         shutil.rmtree(self.tmpdirname      )







    def  lowercase_							(				self      )							->	Dict:
         __lowerCamelCase  :      List[str]														=						[np.random.randint(2_55     ,	size=(3, 30, 4_00)     ,	dtype=np.uinta      )]

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

         return image_inputs







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.get_tokenizer()
         __lowerCamelCase  :      Optional[int]														=						self.get_image_processor()

         __lowerCamelCase  :      Union[str, Any]														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         processor.save_pretrained(self.tmpdirname      )
         __lowerCamelCase  :      Tuple														=						VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname      )

         self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer.get_vocab()      )
         self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

         self.assertEqual(processor.image_processor.to_json_string()     ,	image_processor.to_json_string()      )
         self.assertIsInstance(processor.image_processor     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Optional[Any]														=						VisionTextDualEncoderProcessor(
             tokenizer=self.get_tokenizer()     ,	image_processor=self.get_image_processor()      )
         processor.save_pretrained(self.tmpdirname      )

         __lowerCamelCase  :      Any														=						self.get_tokenizer(bos_token='(BOS)'     ,	eos_token='(EOS)'      )
         __lowerCamelCase  :      Optional[int]														=						self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0      )

         __lowerCamelCase  :      List[str]														=						VisionTextDualEncoderProcessor.from_pretrained(
             self.tmpdirname     ,	bos_token='(BOS)'     ,	eos_token='(EOS)'     ,	do_normalize=SCREAMING_SNAKE_CASE_     ,	padding_value=1.0      )

         self.assertEqual(processor.tokenizer.get_vocab()     ,	tokenizer_add_kwargs.get_vocab()      )
         self.assertIsInstance(processor.tokenizer     ,	(BertTokenizer, BertTokenizerFast)      )

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







    def  lowercase_							(				self      )							->	Union[str, Any]:
         __lowerCamelCase  :      Optional[int]														=						self.get_image_processor()
         __lowerCamelCase  :      List[str]														=						self.get_tokenizer()

         __lowerCamelCase  :      int														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						self.prepare_image_inputs()

         __lowerCamelCase  :      Optional[int]														=						image_processor(SCREAMING_SNAKE_CASE_     ,	return_tensors='np'      )
         __lowerCamelCase  :      Union[str, Any]														=						processor(images=SCREAMING_SNAKE_CASE_     ,	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  lowercase_							(				self      )							->	List[str]:
         __lowerCamelCase  :      Dict														=						self.get_image_processor()
         __lowerCamelCase  :      str														=						self.get_tokenizer()

         __lowerCamelCase  :      Any														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[Any]														=						'lower newer'

         __lowerCamelCase  :      Optional[int]														=						processor(text=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      List[Any]														=						tokenizer(SCREAMING_SNAKE_CASE_      )

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







    def  lowercase_							(				self      )							->	str:
         __lowerCamelCase  :      List[Any]														=						self.get_image_processor()
         __lowerCamelCase  :      Any														=						self.get_tokenizer()

         __lowerCamelCase  :      int														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      int														=						'lower newer'
         __lowerCamelCase  :      List[str]														=						self.prepare_image_inputs()

         __lowerCamelCase  :      Any														=						processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_      )

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

         # test if it raises when no input is passed
         with self.assertRaises(SCREAMING_SNAKE_CASE_      ):
              processor()







    def  lowercase_							(				self      )							->	Optional[Any]:
         __lowerCamelCase  :      Tuple														=						self.get_image_processor()
         __lowerCamelCase  :      int														=						self.get_tokenizer()

         __lowerCamelCase  :      Optional[Any]														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

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

         __lowerCamelCase  :      int														=						processor.batch_decode(SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Tuple														=						tokenizer.batch_decode(SCREAMING_SNAKE_CASE_      )

         self.assertListEqual(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      List[str]														=						self.get_image_processor()
         __lowerCamelCase  :      List[str]														=						self.get_tokenizer()

         __lowerCamelCase  :      int														=						VisionTextDualEncoderProcessor(tokenizer=SCREAMING_SNAKE_CASE_     ,	image_processor=SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Any														=						'lower newer'
         __lowerCamelCase  :      int														=						self.prepare_image_inputs()

         __lowerCamelCase  :      Any														=						processor(text=SCREAMING_SNAKE_CASE_     ,	images=SCREAMING_SNAKE_CASE_      )

         self.assertListEqual(list(inputs.keys()      )     ,	processor.model_input_names      )












'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					int = 1_00_00_00    ):

      """simple docstring"""






      SCREAMING_SNAKE_CASE   :      Tuple    					=						[i - 1 for i in range(limit + 1    )]

      for i in range(2      ,limit + 1    ):
            if phi[i] == i - 1:
                  for j in range(2 * i      ,limit + 1      ,__UpperCamelCase    ):
                        phi[j] -= phi[j] // i

      return sum(phi[2 : limit + 1]    )


if __name__ == "__main__":
      print(solution())


