'''simple docstring'''

import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
						from PIL import Image

						from transformers import FlavaImageProcessor, FlavaProcessor
						from transformers.models.flava.image_processing_flava import (
						    FLAVA_CODEBOOK_MEAN,
						    FLAVA_CODEBOOK_STD,
						    FLAVA_IMAGE_MEAN,
						    FLAVA_IMAGE_STD,
						)




@require_vision
class 		SCREAMING_SNAKE_CASE_				(unittest.TestCase			):



			"""simple docstring"""






			def      lowerCamelCase   (       self				):
									snake_case   :					str							=						tempfile.mkdtemp()

									# fmt: off
									snake_case   :					Any							=						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
									# fmt: on
									snake_case   :					int							=						os.path.join(self.tmpdirname							, VOCAB_FILES_NAMES["vocab_file"]				)

									with open(self.vocab_file							, "w"							, encoding="utf-8"				) as fp:
															fp.write("".join([x + "\n" for x in vocab_tokens]				)				)

									snake_case   :					List[Any]							=						{
									    "image_mean": FLAVA_IMAGE_MEAN,
									    "image_std": FLAVA_IMAGE_STD,
									    "do_normalize": True,
									    "do_resize": True,
									    "size": 224,
									    "do_center_crop": True,
									    "crop_size": 224,
									    "input_size_patches": 14,
									    "total_mask_patches": 75,
									    "mask_group_max_patches": None,
									    "mask_group_min_patches": 16,
									    "mask_group_min_aspect_ratio": 0.3,
									    "mask_group_max_aspect_ratio": None,
									    "codebook_do_resize": True,
									    "codebook_size": 112,
									    "codebook_do_center_crop": True,
									    "codebook_crop_size": 112,
									    "codebook_do_map_pixels": True,
									    "codebook_do_normalize": True,
									    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
									    "codebook_image_std": FLAVA_CODEBOOK_STD,
									}

									snake_case   :					str							=						os.path.join(self.tmpdirname							, snake_case_				)
									with open(self.image_processor_file							, "w"							, encoding="utf-8"				) as fp:
															json.dump(snake_case_							, snake_case_				)



			def      lowerCamelCase   (       self							, **snake_case_				):
									return BertTokenizer.from_pretrained(self.tmpdirname							, **snake_case_				)



			def      lowerCamelCase   (       self							, **snake_case_				):
									return BertTokenizerFast.from_pretrained(self.tmpdirname							, **snake_case_				)



			def      lowerCamelCase   (       self							, **snake_case_				):
									return FlavaImageProcessor.from_pretrained(self.tmpdirname							, **snake_case_				)



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



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

									snake_case   :					Union[str, Any]							=						[Image.fromarray(np.moveaxis(snake_case_							, 0							, -1				)				) for x in image_inputs]

									return image_inputs



			def      lowerCamelCase   (       self				):
									snake_case   :					List[Any]							=						self.get_tokenizer()
									snake_case   :					Optional[int]							=						self.get_rust_tokenizer()
									snake_case   :					str							=						self.get_image_processor()

									snake_case   :					Any							=						FlavaProcessor(tokenizer=snake_case_							, image_processor=snake_case_				)
									processor_slow.save_pretrained(self.tmpdirname				)
									snake_case   :					str							=						FlavaProcessor.from_pretrained(self.tmpdirname							, use_fast=snake_case_				)

									snake_case   :					str							=						FlavaProcessor(tokenizer=snake_case_							, image_processor=snake_case_				)
									processor_fast.save_pretrained(self.tmpdirname				)
									snake_case   :					List[str]							=						FlavaProcessor.from_pretrained(self.tmpdirname				)

									self.assertEqual(processor_slow.tokenizer.get_vocab()							, tokenizer_slow.get_vocab()				)
									self.assertEqual(processor_fast.tokenizer.get_vocab()							, tokenizer_fast.get_vocab()				)
									self.assertEqual(tokenizer_slow.get_vocab()							, tokenizer_fast.get_vocab()				)
									self.assertIsInstance(processor_slow.tokenizer							, snake_case_				)
									self.assertIsInstance(processor_fast.tokenizer							, snake_case_				)

									self.assertEqual(processor_slow.image_processor.to_json_string()							, image_processor.to_json_string()				)
									self.assertEqual(processor_fast.image_processor.to_json_string()							, image_processor.to_json_string()				)
									self.assertIsInstance(processor_slow.image_processor							, snake_case_				)
									self.assertIsInstance(processor_fast.image_processor							, snake_case_				)



			def      lowerCamelCase   (       self				):
									snake_case   :					Any							=						FlavaProcessor(tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()				)
									processor.save_pretrained(self.tmpdirname				)

									snake_case   :					Tuple							=						self.get_tokenizer(bos_token="(BOS)"							, eos_token="(EOS)"				)
									snake_case   :					Optional[int]							=						self.get_image_processor(do_normalize=snake_case_							, padding_value=1.0				)

									snake_case   :					str							=						FlavaProcessor.from_pretrained(
									    self.tmpdirname							, bos_token="(BOS)"							, eos_token="(EOS)"							, do_normalize=snake_case_							, padding_value=1.0				)

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

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



			def      lowerCamelCase   (       self				):
									snake_case   :					Optional[Any]							=						self.get_image_processor()
									snake_case   :					Optional[Any]							=						self.get_tokenizer()

									snake_case   :					List[str]							=						FlavaProcessor(tokenizer=snake_case_							, image_processor=snake_case_				)

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

									snake_case   :					Tuple							=						image_processor(snake_case_							, return_tensors="np"				)
									snake_case   :					Union[str, Any]							=						processor(images=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				)

									# With rest of the args
									random.seed(1_234				)
									snake_case   :					str							=						image_processor(
									    snake_case_							, return_image_mask=snake_case_							, return_codebook_pixels=snake_case_							, return_tensors="np"				)
									random.seed(1_234				)
									snake_case   :					Dict							=						processor(
									    images=snake_case_							, return_image_mask=snake_case_							, return_codebook_pixels=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      lowerCamelCase   (       self				):
									snake_case   :					List[Any]							=						self.get_image_processor()
									snake_case   :					Tuple							=						self.get_tokenizer()

									snake_case   :					str							=						FlavaProcessor(tokenizer=snake_case_							, image_processor=snake_case_				)

									snake_case   :					Optional[Any]							=						"lower newer"

									snake_case   :					Optional[int]							=						processor(text=snake_case_				)

									snake_case   :					str							=						tokenizer(snake_case_				)

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



			def      lowerCamelCase   (       self				):
									snake_case   :					Tuple							=						self.get_image_processor()
									snake_case   :					Optional[int]							=						self.get_tokenizer()

									snake_case   :					Dict							=						FlavaProcessor(tokenizer=snake_case_							, image_processor=snake_case_				)

									snake_case   :					Dict							=						"lower newer"
									snake_case   :					Optional[Any]							=						self.prepare_image_inputs()

									snake_case   :					Tuple							=						processor(text=snake_case_							, images=snake_case_				)

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

									# add extra args
									snake_case   :					List[str]							=						processor(text=snake_case_							, images=snake_case_							, return_codebook_pixels=snake_case_							, return_image_mask=snake_case_				)

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

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



			def      lowerCamelCase   (       self				):
									snake_case   :					Dict							=						self.get_image_processor()
									snake_case   :					str							=						self.get_tokenizer()

									snake_case   :					List[str]							=						FlavaProcessor(tokenizer=snake_case_							, image_processor=snake_case_				)

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

									snake_case   :					int							=						processor.batch_decode(snake_case_				)
									snake_case   :					Optional[Any]							=						tokenizer.batch_decode(snake_case_				)

									self.assertListEqual(snake_case_							, snake_case_				)






			def      lowerCamelCase   (       self				):
									snake_case   :					str							=						self.get_image_processor()
									snake_case   :					str							=						self.get_tokenizer()

									snake_case   :					Optional[int]							=						FlavaProcessor(tokenizer=snake_case_							, image_processor=snake_case_				)

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

									snake_case   :					Tuple							=						processor(text=snake_case_							, images=snake_case_				)

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






'''simple docstring'''

import math


def 					UpperCAmelCase__      (							_lowerCAmelCase						: int)						->		bool:



						"""simple docstring"""

						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 = 200_0000)						->		int:



						"""simple docstring"""

						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() = }")





