




"""simple docstring"""



import json
import os
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 AlignProcessor, EfficientNetImageProcessor






@require_vision
class 				snake_case__     (   unittest.TestCase   ):


 def 		a__ (				self		):
        __a     				=  tempfile.mkdtemp()

        __a     				=  [
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "want",
            "##want",
            "##ed",
            "wa",
            "un",
            "runn",
            "##ing",
            ",",
            "low",
            "lowest",
        ]
        __a     				=  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     				=  {
            "do_resize": True,
            "size": 20,
            "do_center_crop": True,
            "crop_size": 18,
            "do_normalize": True,
            "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
            "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        __a     				=  os.path.join(self.tmpdirname  ,						lowercase_		)
        with open(self.image_processor_file  ,						"w"  ,						encoding="utf-8"		) as fp:
               json.dump(lowercase_  ,						lowercase_		)


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


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


 def 		a__ (				self  ,						**lowerCamelCase		):
        return EfficientNetImageProcessor.from_pretrained(self.tmpdirname  ,						**lowercase_		)


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


 def 		a__ (				self		):
        __a     				=  [np.random.randint(255  ,						size=(3, 30, 400)  ,						dtype=np.uinta		)]
        __a     				=  [Image.fromarray(np.moveaxis(lowercase_  ,						0  ,						-1		)		) for x in image_inputs]
        return image_inputs


 def 		a__ (				self		):
        __a     				=  self.get_tokenizer()
        __a     				=  self.get_rust_tokenizer()
        __a     				=  self.get_image_processor()

        __a     				=  AlignProcessor(tokenizer=lowercase_  ,						image_processor=lowercase_		)
        processor_slow.save_pretrained(self.tmpdirname		)
        __a     				=  AlignProcessor.from_pretrained(self.tmpdirname  ,						use_fast=lowercase_		)

        __a     				=  AlignProcessor(tokenizer=lowercase_  ,						image_processor=lowercase_		)
        processor_fast.save_pretrained(self.tmpdirname		)
        __a     				=  AlignProcessor.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  ,						lowercase_		)
        self.assertIsInstance(processor_fast.tokenizer  ,						lowercase_		)

        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  ,						lowercase_		)
        self.assertIsInstance(processor_fast.image_processor  ,						lowercase_		)


 def 		a__ (				self		):
        __a     				=  AlignProcessor(tokenizer=self.get_tokenizer()  ,						image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

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

        __a     				=  AlignProcessor.from_pretrained(
            self.tmpdirname  ,						bos_token="(BOS)"  ,						eos_token="(EOS)"  ,						do_normalize=lowercase_  ,						padding_value=1.0		)

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

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


 def 		a__ (				self		):
        __a     				=  self.get_image_processor()
        __a     				=  self.get_tokenizer()

        __a     				=  AlignProcessor(tokenizer=lowercase_  ,						image_processor=lowercase_		)

        __a     				=  self.prepare_image_inputs()

        __a     				=  image_processor(lowercase_  ,						return_tensors="np"		)
        __a     				=  processor(images=lowercase_  ,						return_tensors="np"		)

        for key in input_image_proc.keys():
               self.assertAlmostEqual(input_image_proc[key].sum()  ,						input_processor[key].sum()  ,						delta=1E-2		)


 def 		a__ (				self		):
        __a     				=  self.get_image_processor()
        __a     				=  self.get_tokenizer()

        __a     				=  AlignProcessor(tokenizer=lowercase_  ,						image_processor=lowercase_		)

        __a     				=  "lower newer"

        __a     				=  processor(text=lowercase_		)

        __a     				=  tokenizer(lowercase_  ,						padding="max_length"  ,						max_length=64		)

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


 def 		a__ (				self		):
        __a     				=  self.get_image_processor()
        __a     				=  self.get_tokenizer()

        __a     				=  AlignProcessor(tokenizer=lowercase_  ,						image_processor=lowercase_		)

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

        __a     				=  processor(text=lowercase_  ,						images=lowercase_		)

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

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


 def 		a__ (				self		):
        __a     				=  self.get_image_processor()
        __a     				=  self.get_tokenizer()

        __a     				=  AlignProcessor(tokenizer=lowercase_  ,						image_processor=lowercase_		)

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

        __a     				=  processor.batch_decode(lowercase_		)
        __a     				=  tokenizer.batch_decode(lowercase_		)

        self.assertListEqual(lowercase_  ,						lowercase_		)






 def 		a__ (				self		):
        __a     				=  self.get_image_processor()
        __a     				=  self.get_tokenizer()

        __a     				=  AlignProcessor(tokenizer=lowercase_  ,						image_processor=lowercase_		)

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

        __a     				=  processor(text=lowercase_  ,						images=lowercase_		)

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

"""simple docstring"""



import math



def 					_lowerCamelCase(					a							):
       __a     				=  []
       __a     				=  2
       __a     				=  int(math.sqrt(a							)							)  # Size of every segment
       __a     				=  [True] * (end + 1)
       __a     				=  []

       while start <= end:
              if temp[start] is True:
                     in_prime.append(a							)
                     for i in range(start * start ,  end + 1 ,  a							):
                            __a     				=  False
              start += 1
       prime += in_prime

       __a     				=  end + 1
       __a     				=  min(2 * end ,  a							)

       while low <= n:
              __a     				=  [True] * (high - low + 1)
              for each in in_prime:
                     __a     				=  math.floor(low / each							) * each
                     if t < low:
                            t += each

                     for j in range(a ,  high + 1 ,  a							):
                            __a     				=  False

              for j in range(len(a							)							):
                     if temp[j] is True:
                            prime.append(j + low							)

              __a     				=  high + 1
              __a     				=  min(high + end ,  a							)

       return prime




print(sieve(10**6))
