

"""simple docstring"""




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      _UpperCAmelCase   :



					@staticmethod
					def        a  (  *_lowercase     :       Optional[int]			,    **_lowercase     :       List[str]						):
						pass
@is_pipeline_test
@require_vision
class      _UpperCAmelCase   (							unittest.TestCase       ):



	@require_torch
	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= pipeline(
		    model='''hf-internal-testing/tiny-random-clip-zero-shot-image-classification'''			,    )
		__UpperCAmelCase      		= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
		__UpperCAmelCase      		= image_classifier(_lowercase			,    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(_lowercase						)			,    [
		        [{'''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'''}],
		    ]			,    )

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



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

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

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



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

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

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



	@slow
	@require_tf
	def        a  (  self     :       Dict						):
		__UpperCAmelCase      		= 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
		__UpperCAmelCase      		= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
		__UpperCAmelCase      		= image_classifier(_lowercase			,    candidate_labels=['''cat''', '''plane''', '''remote''']						)
		self.assertEqual(
		    nested_simplify(_lowercase						)			,    [
		        {'''score''': 0.511, '''label''': '''remote'''},
		        {'''score''': 0.485, '''label''': '''cat'''},
		        {'''score''': 0.004, '''label''': '''plane'''},
		    ]			,    )

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





import math
from collections.abc import Iterator
from itertools import takewhile






def     __a					(		__UpperCAmelCase       ):

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






def     __a					(		):
 a__												= 2
 while True:
  if is_prime(__UpperCAmelCase       ):
   yield num
  num += 1






def     __a					(		__UpperCAmelCase = 200_0000       ):
 return sum(takewhile(lambda __UpperCAmelCase       : x < n							,  prime_generator()       )       )


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





