




from __future__ import annotations

import math







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):

					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(SCREAMING_SNAKE_CASE_  ) + 1  )   ,   6  ):
										if number % i == 0 or number % (i + 2) == 0:
															return False
					return True


UpperCamelCase__					    =    [num for num in range(3, 100001, 2) if not is_prime(num)]







def 			_a   (		SCREAMING_SNAKE_CASE_							:       int  ):
					if not isinstance(SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_  ):
										raise ValueError("n must be an integer"  )
					if n <= 0:
										raise ValueError("n must be >= 0"  )

					__lowerCAmelCase            = []
					for num in range(len(SCREAMING_SNAKE_CASE_  )  ):
										__lowerCAmelCase            = 0
										while 2 * i * i <= odd_composites[num]:
															__lowerCAmelCase            = odd_composites[num] - 2 * i * i
															if is_prime(SCREAMING_SNAKE_CASE_  ):
																				break
															i += 1
										else:
															list_nums.append(odd_composites[num]  )
															if len(SCREAMING_SNAKE_CASE_  ) == n:
																				return list_nums

					return []







def 			_a   (		):
					return compute_nums(1  )[0]


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










import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
					from PIL import Image
else:
					class     a__					:






											@staticmethod
											def        __SCREAMING_SNAKE_CASE(     *_A	,    **_A    ):




																"""simple docstring"""



																pass
@is_pipeline_test
@require_torch
@require_vision
class     a__					(      unittest.TestCase   ):
						_a							:					Any      =					MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = [
											    {
											        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    ),
											        "question": "How many cats are there?",
											    },
											    {
											        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
											        "question": "How many cats are there?",
											    },
											]
											return vqa_pipeline, examples






						def        __SCREAMING_SNAKE_CASE(     self	,    _A	,    _A    ):




											"""simple docstring"""



											__lowerCAmelCase            = vqa_pipeline(_A	,    top_k=1    )
											self.assertEqual(
											    _A	,    [
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											        [{"score": ANY(_A    ), "answer": ANY(_A    )}],
											    ]	,    )






						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="hf-internal-testing/tiny-vilt-random-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question="How many cats are there?"	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    _A	,    [{"score": ANY(_A    ), "answer": ANY(_A    )}, {"score": ANY(_A    ), "answer": ANY(_A    )}]    )






						@slow
						@require_torch
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											__lowerCAmelCase            = pipeline("visual-question-answering"	,    model="dandelin/vilt-b32-finetuned-vqa"    )
											__lowerCAmelCase            = "./tests/fixtures/tests_samples/COCO/000000039769.png"
											__lowerCAmelCase            = "How many cats are there?"

											__lowerCAmelCase            = vqa_pipeline(image=_A	,    question=_A	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline({"image": image, "question": question}	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]    )

											__lowerCAmelCase            = vqa_pipeline(
											    [{"image": image, "question": question}, {"image": image, "question": question}]	,    top_k=2    )
											self.assertEqual(
											    nested_simplify(_A	,    decimals=4    )	,    [[{"score": 0.87_99, "answer": "2"}, {"score": 0.2_96, "answer": "1"}]] * 2	,    )






						@require_tf
						@unittest.skip("Visual question answering not implemented in TF"    )
						def        __SCREAMING_SNAKE_CASE(     self    ):




											"""simple docstring"""



											pass




