




from __future__ import annotations
def    snake_case_	(						SCREAMING_SNAKE_CASE_							)     ->					list[int]:
							lowercase__    :			str     =				[True] * limit
							lowercase__    :			int     =				False
							lowercase__    :			Union[str, Any]     =				False
							lowercase__    :			Optional[Any]     =				True

							for i in range(3							,int(limit**0.5 + 1							)							,2							):
														lowercase__    :			Dict     =				i * 2
														while index < limit:
																					lowercase__    :			List[Any]     =				False
																					lowercase__    :			int     =				index + i

							lowercase__    :			Optional[int]     =				[2]

							for i in range(3							,__lowerCamelCase							,2							):
														if is_prime[i]:
																					primes.append(__lowerCamelCase							)

							return primes
def    snake_case_	(						SCREAMING_SNAKE_CASE_ = 1_00_00_00							)     ->					int:
							lowercase__    :			Any     =				prime_sieve(__lowerCamelCase							)
							lowercase__    :			Tuple     =				0
							lowercase__    :			Optional[Any]     =				0

							for i in range(len(__lowerCamelCase							)							):
														for j in range(i + length							,len(__lowerCamelCase							)							):
																					lowercase__    :			Optional[int]     =				sum(primes[i:j]							)
																					if sol >= ceiling:
																												break

																					if sol in primes:
																												lowercase__    :			int     =				j - i
																												lowercase__    :			Any     =				sol

							return largest


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




import unittest

from transformers import 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 		UpperCamelCase				(    *snake_case_				:     Any					,						**snake_case_				:     str)->				int:
										pass






@is_pipeline_test
@require_vision
class     __a			(  unittest.TestCase					):



	@require_torch
	def 		UpperCamelCase				(    self				:     Dict)->				List[str]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						)
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""a""", """b""", """c"""])

						# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
						# python and torch versions.
						self.assertIn(
						    nested_simplify(snake_case_)					,						[
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}],
						        [{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """c"""}, {"""score""": 0.3_3_3, """label""": """b"""}],
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



	@require_tf
	def 		UpperCamelCase				(    self				:     Dict)->				Optional[Any]:
						__lowerCAmelCase						 =pipeline(
						    model="""hf-internal-testing/tiny-random-clip-zero-shot-image-classification"""					,						framework="""tf""")
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""a""", """b""", """c"""])

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[{"""score""": 0.3_3_3, """label""": """a"""}, {"""score""": 0.3_3_3, """label""": """b"""}, {"""score""": 0.3_3_3, """label""": """c"""}]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""A""", """B""", """C"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						        [
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						            {"""score""": 0.3_3_3, """label""": ANY(snake_case_)},
						        ],
						    ]					,						)



	@slow
	@require_torch
	def 		UpperCamelCase				(    self				:     Any)->				Dict:
						__lowerCAmelCase						 =pipeline(
						    task="""zero-shot-image-classification"""					,						model="""openai/clip-vit-base-patch32"""					,						)
						# This is an image of 2 cats with remotes and no planes
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])

						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)



	@slow
	@require_tf
	def 		UpperCamelCase				(    self				:     Optional[int])->				int:
						__lowerCAmelCase						 =pipeline(
						    task="""zero-shot-image-classification"""					,						model="""openai/clip-vit-base-patch32"""					,						framework="""tf""")
						# This is an image of 2 cats with remotes and no planes
						__lowerCAmelCase						 =Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
						__lowerCAmelCase						 =image_classifier(snake_case_					,						candidate_labels=["""cat""", """plane""", """remote"""])
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        {"""score""": 0.5_1_1, """label""": """remote"""},
						        {"""score""": 0.4_8_5, """label""": """cat"""},
						        {"""score""": 0.0_0_4, """label""": """plane"""},
						    ]					,						)

						__lowerCAmelCase						 =image_classifier([image] * 5					,						candidate_labels=["""cat""", """plane""", """remote"""]					,						batch_size=2)
						self.assertEqual(
						    nested_simplify(snake_case_)					,						[
						        [
						            {"""score""": 0.5_1_1, """label""": """remote"""},
						            {"""score""": 0.4_8_5, """label""": """cat"""},
						            {"""score""": 0.0_0_4, """label""": """plane"""},
						        ],
						    ]
						    * 5					,						)




