





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 			lowercase				:



								"""simple docstring"""

								@staticmethod
								def 		__UpperCAmelCase       (    *lowerCamelCase_			:		Any ,				**lowerCamelCase_			:		Tuple					):





												'''simple docstring'''







												pass


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



						"""simple docstring"""

						@require_torch
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			int  						=   pipeline(
										    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' ,				)
										_snake_case :			List[Any]  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Dict  						=   image_classifier(lowerCamelCase_ ,				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(lowerCamelCase_					) ,				[
										        [{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}],
										        [{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'c'}, {'score': 0.333, 'label': 'b'}],
										    ] ,				)

										_snake_case :			Dict  						=   image_classifier([image] * 5 ,				candidate_labels=['A', 'B', 'C'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										    ] ,				)







						@require_tf
						def 		__UpperCAmelCase       (    self			:		int					):





										'''simple docstring'''







										_snake_case :			Union[str, Any]  						=   pipeline(
										    model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification' ,				framework='tf'					)
										_snake_case :			Optional[int]  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Dict  						=   image_classifier(lowerCamelCase_ ,				candidate_labels=['a', 'b', 'c']					)

										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[{'score': 0.333, 'label': 'a'}, {'score': 0.333, 'label': 'b'}, {'score': 0.333, 'label': 'c'}] ,				)

										_snake_case :			Optional[int]  						=   image_classifier([image] * 5 ,				candidate_labels=['A', 'B', 'C'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										        [
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										            {'score': 0.333, 'label': ANY(lowerCamelCase_					)},
										        ],
										    ] ,				)







						@slow
						@require_torch
						def 		__UpperCAmelCase       (    self			:		Dict					):





										'''simple docstring'''







										_snake_case :			List[str]  						=   pipeline(
										    task='zero-shot-image-classification' ,				model='openai/clip-vit-base-patch32' ,				)
										# This is an image of 2 cats with remotes and no planes
										_snake_case :			Tuple  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Any  						=   image_classifier(lowerCamelCase_ ,				candidate_labels=['cat', 'plane', 'remote']					)

										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        {'score': 0.511, 'label': 'remote'},
										        {'score': 0.485, 'label': 'cat'},
										        {'score': 0.004, 'label': 'plane'},
										    ] ,				)

										_snake_case :			Optional[Any]  						=   image_classifier([image] * 5 ,				candidate_labels=['cat', 'plane', 'remote'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.511, 'label': 'remote'},
										            {'score': 0.485, 'label': 'cat'},
										            {'score': 0.004, 'label': 'plane'},
										        ],
										    ]
										    * 5 ,				)







						@slow
						@require_tf
						def 		__UpperCAmelCase       (    self			:		str					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   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
										_snake_case :			Union[str, Any]  						=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
										_snake_case :			Tuple  						=   image_classifier(lowerCamelCase_ ,				candidate_labels=['cat', 'plane', 'remote']					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        {'score': 0.511, 'label': 'remote'},
										        {'score': 0.485, 'label': 'cat'},
										        {'score': 0.004, 'label': 'plane'},
										    ] ,				)

										_snake_case :			Tuple  						=   image_classifier([image] * 5 ,				candidate_labels=['cat', 'plane', 'remote'] ,				batch_size=2					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_					) ,				[
										        [
										            {'score': 0.511, 'label': 'remote'},
										            {'score': 0.485, 'label': 'cat'},
										            {'score': 0.004, 'label': 'plane'},
										        ],
										    ]
										    * 5 ,				)













import math

def       A__(		__lowerCAmelCase ):

				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       A__(		__lowerCAmelCase = 1_00_01 ):

				try:
								_snake_case :			int  						=   int(__lowerCAmelCase )
				except (TypeError, ValueError):
								raise TypeError('Parameter nth must be int or castable to int.' ) from None
				if nth <= 0:
								raise ValueError('Parameter nth must be greater than or equal to one.' )
				_snake_case :			list[int]  						=   []
				_snake_case :			List[Any]  						=   2
				while len(__lowerCAmelCase ) < nth:
								if is_prime(__lowerCAmelCase ):
												primes.append(__lowerCAmelCase )
												num += 1
								else:
												num += 1
				return primes[len(__lowerCAmelCase ) - 1]


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






