






import math
from itertools import permutations







def 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase    )				->			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 	SCREAMING_SNAKE_CASE							(      __lowerCAmelCase      ,					__lowerCAmelCase    )				->			bool:
     UpperCamelCase__       ,  UpperCamelCase__    :     int        =						0, len(__lowerCAmelCase    ) - 1
     while left <= right:
          UpperCamelCase__    :     Optional[Any]        =						(left + right) // 2
          if prime_list[middle] == target:
               return True
          elif prime_list[middle] < target:
               UpperCamelCase__    :     List[Any]        =						middle + 1
          else:
               UpperCamelCase__    :     Any        =						middle - 1

     return False







def 	SCREAMING_SNAKE_CASE							(      )				->			Optional[int]:
     UpperCamelCase__    :     Optional[Any]        =						[n for n in range(1001      ,					1_0000      ,					2    ) if is_prime(__lowerCAmelCase    )]
     UpperCamelCase__    :     Any        =						[]

     for number in prime_list:
          UpperCamelCase__    :     Optional[Any]        =						[]

          for prime_member in permutations(list(str(__lowerCAmelCase    )    )    ):
               UpperCamelCase__    :     List[Any]        =						int("".join(__lowerCAmelCase    )    )

               if prime % 2 == 0:
                    continue

               if search(__lowerCAmelCase      ,					__lowerCAmelCase    ):
                    tmp_numbers.append(__lowerCAmelCase    )

          tmp_numbers.sort()
          if len(__lowerCAmelCase    ) >= 3:
               candidates.append(__lowerCAmelCase    )

     UpperCamelCase__    :     List[Any]        =						[]
     for candidate in candidates:
          UpperCamelCase__    :     Any        =						len(__lowerCAmelCase    )
          UpperCamelCase__    :     Any        =						False

          for i in range(__lowerCAmelCase    ):
               for j in range(i + 1      ,					__lowerCAmelCase    ):
                    for k in range(j + 1      ,					__lowerCAmelCase    ):
                         if (
                             abs(candidate[i] - candidate[j]    )
                             == abs(candidate[j] - candidate[k]    )
                             and len({candidate[i], candidate[j], candidate[k]}    ) == 3
                         ):
                              passed.append(
                                  sorted([candidate[i], candidate[j], candidate[k]]    )    )
                              UpperCamelCase__    :     Any        =						True

                         if found:
                              break
                    if found:
                         break
               if found:
                    break

     UpperCamelCase__    :     int        =						set()
     for seq in passed:
          answer.add("".join([str(__lowerCAmelCase    ) for i in seq]    )    )

     return max(int(__lowerCAmelCase    ) for x in answer    )


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







import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:





 class     __a						:







  @staticmethod
  def       __lowercase      (	*SCREAMING_SNAKE_CASE      :     Optional[Any]							,     **SCREAMING_SNAKE_CASE      :     Union[str, Any]		):


       '''simple docstring'''






       pass





@is_pipeline_test
@require_torch_or_tf
@require_vision
class     __a						(					unittest.TestCase ):
 _lowerCAmelCase : Tuple 				=				MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 _lowerCAmelCase : str 				=				TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING







 def       __lowercase      (	self      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     str							,     SCREAMING_SNAKE_CASE      :     Union[str, Any]							,     SCREAMING_SNAKE_CASE      :     Optional[Any]		):


      '''simple docstring'''






      UpperCamelCase__    :     Dict        =						ImageClassificationPipeline(model=SCREAMING_SNAKE_CASE							,     image_processor=SCREAMING_SNAKE_CASE							,     top_k=2		)
      UpperCamelCase__    :     Union[str, Any]        =						[
          Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
          "http://images.cocodataset.org/val2017/000000039769.jpg",
      ]
      return image_classifier, examples







 def       __lowercase      (	self      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple							,     SCREAMING_SNAKE_CASE      :     Tuple		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						image_classifier("./tests/fixtures/tests_samples/COCO/000000039769.png"		)

      self.assertEqual(
          SCREAMING_SNAKE_CASE							,     [
              {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
          ]							,     )

      import datasets

      UpperCamelCase__    :     int        =						datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,     "image"							,     split="test"		)

      # Accepts URL + PIL.Image + lists
      UpperCamelCase__    :     Tuple        =						image_classifier(
          [
              Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              # RGBA
              dataset[0]["file"],
              # LA
              dataset[1]["file"],
              # L
              dataset[2]["file"],
          ]		)
      self.assertEqual(
          SCREAMING_SNAKE_CASE							,     [
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
              [
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
                  {"score": ANY(SCREAMING_SNAKE_CASE		), "label": ANY(SCREAMING_SNAKE_CASE		)},
              ],
          ]							,     )







 @require_torch
 def       __lowercase      (	self      :     List[str]		):


      '''simple docstring'''






      UpperCamelCase__    :     List[Any]        =						"hf-internal-testing/tiny-random-vit"
      UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE		)

      UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )

      UpperCamelCase__    :     Optional[Any]        =						image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]							,     top_k=2							,     )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]							,     )







 @require_tf
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     str        =						"hf-internal-testing/tiny-random-vit"
      UpperCamelCase__    :     int        =						pipeline("image-classification"							,     model=SCREAMING_SNAKE_CASE							,     framework="tf"		)

      UpperCamelCase__    :     List[Any]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}]							,     )

      UpperCamelCase__    :     int        =						image_classifier(
          [
              "http://images.cocodataset.org/val2017/000000039769.jpg",
              "http://images.cocodataset.org/val2017/000000039769.jpg",
          ]							,     top_k=2							,     )
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
              [{"label": "LABEL_1", "score": 0.5_7_4}, {"label": "LABEL_0", "score": 0.4_2_6}],
          ]							,     )







 def       __lowercase      (	self      :     Optional[int]		):


      '''simple docstring'''






      UpperCamelCase__    :     Any        =						PreTrainedTokenizer()

      # Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
      UpperCamelCase__    :     str        =						pipeline(
          "image-classification"							,     model="hf-internal-testing/tiny-random-vit"							,     tokenizer=SCREAMING_SNAKE_CASE		)

      self.assertIs(image_classifier.tokenizer							,     SCREAMING_SNAKE_CASE		)







 @slow
 @require_torch
 def       __lowercase      (	self      :     int		):


      '''simple docstring'''






      UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-conv"		)
      UpperCamelCase__    :     Any        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.4_3_8_5, "label": "tabby, tabby cat"},
              {"score": 0.3_2_1, "label": "tiger cat"},
              {"score": 0.0_5_0_2, "label": "Egyptian cat"},
              {"score": 0.0_1_3_7, "label": "crib, cot"},
              {"score": 0.0_0_7, "label": "radiator"},
          ]							,     )

      UpperCamelCase__    :     Optional[Any]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-fourier"		)
      UpperCamelCase__    :     List[str]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.5_6_5_8, "label": "tabby, tabby cat"},
              {"score": 0.1_3_0_9, "label": "tiger cat"},
              {"score": 0.0_7_2_2, "label": "Egyptian cat"},
              {"score": 0.0_7_0_7, "label": "remote control, remote"},
              {"score": 0.0_0_8_2, "label": "computer keyboard, keypad"},
          ]							,     )

      UpperCamelCase__    :     List[str]        =						pipeline("image-classification"							,     model="deepmind/vision-perceiver-learned"		)
      UpperCamelCase__    :     Optional[int]        =						image_classifier("http://images.cocodataset.org/val2017/000000039769.jpg"		)
      self.assertEqual(
          nested_simplify(SCREAMING_SNAKE_CASE							,     decimals=4		)							,     [
              {"score": 0.3_0_2_2, "label": "tabby, tabby cat"},
              {"score": 0.2_3_6_2, "label": "Egyptian cat"},
              {"score": 0.1_8_5_6, "label": "tiger cat"},
              {"score": 0.0_3_2_4, "label": "remote control, remote"},
              {"score": 0.0_0_9_6, "label": "quilt, comforter, comfort, puff"},
          ]							,     )