

"""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			,    )



"""simple docstring"""


import unittest

from knapsack import greedy_knapsack as kp





class lowerCamelCase__   (				unittest.TestCase    ):



    '''simple docstring'''




    def UpperCamelCase__		( self    )    ->      Any:
          A				  =		[1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
          A				  =		[2, 4, 6, 8, 1_0, 1_2]
          A				  =		1_0_0
          self.assertEqual(kp.calc_profit(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ) ,2_1_0    )




    def UpperCamelCase__		( self    )    ->      List[Any]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      Any:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Weight can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(lowerCamelCase_ ,"""Profit can not be negative."""    )




    def UpperCamelCase__		( self    )    ->      Optional[int]:
          self.assertRaisesRegex(lowerCamelCase_ ,"""max_weight must greater than zero."""    )




    def UpperCamelCase__		( self    )    ->      str:
          self.assertRaisesRegex(
              lowerCamelCase_ ,"""The length of profit and weight must be same."""    )


if __name__ == "__main__":
  unittest.main()




