





"""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 				__lowercase			(			unittest.TestCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self     ):
									__a :						int			       =  tempfile.mkdtemp()

									# fmt: off
									__a :						Dict			       =  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
									# fmt: on
									__a :						Dict			       =  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]     )     )

									__a :						int			       =  {
									    '''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],
									}
									__a :						Dict			       =  os.path.join(self.tmpdirname	,			_UpperCAmelCase     )
									with open(self.image_processor_file	,			'''w'''	,			encoding='''utf-8'''     ) as fp:
													json.dump(_UpperCAmelCase	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			**_UpperCAmelCase     ):
									return BertTokenizer.from_pretrained(self.tmpdirname	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			**_UpperCAmelCase     ):
									return ViTImageProcessor.from_pretrained(self.tmpdirname	,			**_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									shutil.rmtree(self.tmpdirname     )







					def  _lowerCamelCase		(						self     ):
									__a :						Any			       =  [np.random.randint(255	,			size=(3, 30, 400)	,			dtype=np.uinta     )]

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

									return image_inputs







					def  _lowerCamelCase		(						self     ):
									__a :						Union[str, Any]			       =  self.get_tokenizer()
									__a :						Union[str, Any]			       =  self.get_image_processor()

									__a :						str			       =  VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,			image_processor=_UpperCAmelCase     )

									processor.save_pretrained(self.tmpdirname     )
									__a :						Union[str, Any]			       =  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	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  VisionTextDualEncoderProcessor(
									    tokenizer=self.get_tokenizer()	,			image_processor=self.get_image_processor()     )
									processor.save_pretrained(self.tmpdirname     )

									__a :						Any			       =  self.get_tokenizer(bos_token='''(BOS)'''	,			eos_token='''(EOS)'''     )
									__a :						List[Any]			       =  self.get_image_processor(do_normalize=_UpperCAmelCase	,			padding_value=1.0     )

									__a :						Tuple			       =  VisionTextDualEncoderProcessor.from_pretrained(
									    self.tmpdirname	,			bos_token='''(BOS)'''	,			eos_token='''(EOS)'''	,			do_normalize=_UpperCAmelCase	,			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	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						List[str]			       =  self.get_image_processor()
									__a :						List[str]			       =  self.get_tokenizer()

									__a :						Any			       =  VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,			image_processor=_UpperCAmelCase     )

									__a :						Tuple			       =  self.prepare_image_inputs()

									__a :						Union[str, Any]			       =  image_processor(_UpperCAmelCase	,			return_tensors='''np'''     )
									__a :						Tuple			       =  processor(images=_UpperCAmelCase	,			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  _lowerCamelCase		(						self     ):
									__a :						Optional[Any]			       =  self.get_image_processor()
									__a :						str			       =  self.get_tokenizer()

									__a :						List[str]			       =  VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,			image_processor=_UpperCAmelCase     )

									__a :						List[Any]			       =  '''lower newer'''

									__a :						Dict			       =  processor(text=_UpperCAmelCase     )

									__a :						Any			       =  tokenizer(_UpperCAmelCase     )

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







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.get_image_processor()
									__a :						Optional[Any]			       =  self.get_tokenizer()

									__a :						Any			       =  VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,			image_processor=_UpperCAmelCase     )

									__a :						int			       =  '''lower newer'''
									__a :						List[Any]			       =  self.prepare_image_inputs()

									__a :						int			       =  processor(text=_UpperCAmelCase	,			images=_UpperCAmelCase     )

									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(_UpperCAmelCase     ):
													processor()







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.get_image_processor()
									__a :						Dict			       =  self.get_tokenizer()

									__a :						Union[str, Any]			       =  VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,			image_processor=_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									__a :						Optional[int]			       =  processor.batch_decode(_UpperCAmelCase     )
									__a :						int			       =  tokenizer.batch_decode(_UpperCAmelCase     )

									self.assertListEqual(_UpperCAmelCase	,			_UpperCAmelCase     )







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  self.get_image_processor()
									__a :						Union[str, Any]			       =  self.get_tokenizer()

									__a :						Optional[int]			       =  VisionTextDualEncoderProcessor(tokenizer=_UpperCAmelCase	,			image_processor=_UpperCAmelCase     )

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

									__a :						Any			       =  processor(text=_UpperCAmelCase	,			images=_UpperCAmelCase     )

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






"""simple docstring"""






from math import isqrt, loga

def     __A      (			a_						:int)		->		list[int]:
				__a :						int			       =  [True] * max_number
				for i in range(2   ,       isqrt(max_number - 1) + 1):
								if is_prime[i]:
												for j in range(i**2   ,       a_   ,       a_):
																__a :						int			       =  False

				return [i for i in range(2   ,       a_) if is_prime[i]]

def     __A      (			a_						:int = 80_08_00   ,       a_						:int = 80_08_00)		->		int:
				__a :						str			       =  degree * loga(a_)
				__a :						Tuple			       =  int(a_)
				__a :						int			       =  calculate_prime_numbers(a_)

				__a :						List[Any]			       =  0
				__a :						Optional[Any]			       =  0
				__a :						Dict			       =  len(a_) - 1
				while left < right:
								while (
								    prime_numbers[right] * loga(prime_numbers[left])
								    + prime_numbers[left] * loga(prime_numbers[right])
								    > upper_bound
								):
												right -= 1
								hybrid_integers_count += right - left
								left += 1

				return hybrid_integers_count


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