

"""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       )      ->      Union[str, Any]:






    '''simple docstring'''







    _UpperCAmelCase      :							List[str]       =   tempfile.mkdtemp()

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

    _UpperCAmelCase      :							Dict       =   GitProcessor(_A ,						_A       )

    processor.save_pretrained(self.tmpdirname       )
   def 							__snake_case	(						self ,						**_A       )      ->      int:






    '''simple docstring'''







    return AutoProcessor.from_pretrained(self.tmpdirname ,						**_A       ).tokenizer
   def 							__snake_case	(						self ,						**_A       )      ->      List[str]:






    '''simple docstring'''







    return AutoProcessor.from_pretrained(self.tmpdirname ,						**_A       ).image_processor
   def 							__snake_case	(						self       )      ->      int:






    '''simple docstring'''







    shutil.rmtree(self.tmpdirname       )
   def 							__snake_case	(						self       )      ->      Optional[int]:






    '''simple docstring'''







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

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

    return image_inputs
   def 							__snake_case	(						self       )      ->      int:






    '''simple docstring'''







    _UpperCAmelCase      :							List[str]       =   GitProcessor(tokenizer=self.get_tokenizer() ,						image_processor=self.get_image_processor()       )
    processor.save_pretrained(self.tmpdirname       )

    _UpperCAmelCase      :							Union[str, Any]       =   self.get_tokenizer(bos_token="""(BOS)""" ,						eos_token="""(EOS)"""       )
    _UpperCAmelCase      :							List[str]       =   self.get_image_processor(do_normalize=_A ,						padding_value=1.0       )

    _UpperCAmelCase      :							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 							__snake_case	(						self       )      ->      Dict:






    '''simple docstring'''







    _UpperCAmelCase      :							Tuple       =   self.get_image_processor()
    _UpperCAmelCase      :							List[str]       =   self.get_tokenizer()

    _UpperCAmelCase      :							Optional[int]       =   GitProcessor(tokenizer=_A ,						image_processor=_A       )

    _UpperCAmelCase      :							Tuple       =   self.prepare_image_inputs()

    _UpperCAmelCase      :							Dict       =   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 							__snake_case	(						self       )      ->      int:






    '''simple docstring'''







    _UpperCAmelCase      :							Dict       =   self.get_image_processor()
    _UpperCAmelCase      :							Tuple       =   self.get_tokenizer()

    _UpperCAmelCase      :							List[str]       =   GitProcessor(tokenizer=_A ,						image_processor=_A       )

    _UpperCAmelCase      :							Optional[Any]       =   """lower newer"""

    _UpperCAmelCase      :							Optional[int]       =   processor(text=_A       )

    _UpperCAmelCase      :							List[str]       =   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       )      ->      str:






    '''simple docstring'''







    _UpperCAmelCase      :							Optional[Any]       =   self.get_image_processor()
    _UpperCAmelCase      :							Union[str, Any]       =   self.get_tokenizer()

    _UpperCAmelCase      :							Any       =   GitProcessor(tokenizer=_A ,						image_processor=_A       )

    _UpperCAmelCase      :							Tuple       =   """lower newer"""
    _UpperCAmelCase      :							int       =   self.prepare_image_inputs()

    _UpperCAmelCase      :							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       )      ->      Any:






    '''simple docstring'''







    _UpperCAmelCase      :							int       =   self.get_image_processor()
    _UpperCAmelCase      :							Union[str, Any]       =   self.get_tokenizer()

    _UpperCAmelCase      :							int       =   GitProcessor(tokenizer=_A ,						image_processor=_A       )

    _UpperCAmelCase      :							Optional[Any]       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    _UpperCAmelCase      :							Optional[int]       =   processor.batch_decode(_A       )
    _UpperCAmelCase      :							Union[str, Any]       =   tokenizer.batch_decode(_A       )

    self.assertListEqual(_A ,						_A       )
   def 							__snake_case	(						self       )      ->      Optional[int]:






    '''simple docstring'''







    _UpperCAmelCase      :							Any       =   self.get_image_processor()
    _UpperCAmelCase      :							int       =   self.get_tokenizer()

    _UpperCAmelCase      :							Union[str, Any]       =   GitProcessor(tokenizer=_A ,						image_processor=_A       )

    _UpperCAmelCase      :							int       =   """lower newer"""
    _UpperCAmelCase      :							Optional[Any]       =   self.prepare_image_inputs()

    _UpperCAmelCase      :							str       =   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"""






import math
def  UpperCamelCase   (     _lowerCAmelCase       :  int	)			->     bool:

	if 1 < number < 4:
		# 2 and 3 are primes
		return True
	elif number < 2 or number % 2 == 0 or number % 3 == 0:
		# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
		return False

	# All primes number are in format of 6k +/- 1
	for i in range(5,					int(math.sqrt(_lowerCAmelCase	) + 1	),					6	):
		if number % i == 0 or number % (i + 2) == 0:
			return False
	return True
def  UpperCamelCase   (     _lowerCAmelCase       :  int = 2000000	)			->     int:
	return sum(num for num in range(3,					_lowerCAmelCase,					2	) if is_prime(_lowerCAmelCase	)	) + 2 if n > 2 else 0


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





