





'''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   (				*lowerCamelCase			:   Tuple , **lowerCamelCase			:   Optional[Any]		):




						'''simple docstring'''
						pass






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







	@require_torch
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = pipeline(
				    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
				a__     = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				a__     = 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"}],
				    ] , )

				a__     = 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 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = pipeline(
				    model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf"		)
				a__     = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				a__     = 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"}] , )

				a__     = 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 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = pipeline(
				    task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
				# This is an image of 2 cats with remotes and no planes
				a__     = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				a__     = 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"},
				    ] , )

				a__     = 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 						__a   (				self			:   int		):




				'''simple docstring'''
				a__     = 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
				a__     = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				a__     = 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"},
				    ] , )

				a__     = 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 , )










'''simple docstring'''


from __future__ import annotations

from math import pi







def    _lowerCamelCase  (__lowerCamelCase  :     float					,  __lowerCamelCase  :     float					,  __lowerCamelCase  :     float     )   ->    dict[str, float]:

			if (inductance, frequency, reactance).count(0     ) != 1:
						raise ValueError("One and only one argument must be 0"     )
			if inductance < 0:
						raise ValueError("Inductance cannot be negative"     )
			if frequency < 0:
						raise ValueError("Frequency cannot be negative"     )
			if reactance < 0:
						raise ValueError("Inductive reactance cannot be negative"     )
			if inductance == 0:
						return {"inductance": reactance / (2 * pi * frequency)}
			elif frequency == 0:
						return {"frequency": reactance / (2 * pi * inductance)}
			elif reactance == 0:
						return {"reactance": 2 * pi * frequency * inductance}
			else:
						raise ValueError("Exactly one argument must be 0"     )


if __name__ == "__main__":
		import doctest

		doctest.testmod()



