



from ..utils import DummyObject, requires_backends







class     __lowerCAmelCase					(    metaclass=A		):
			UpperCamelCase           =  ['''torch''', '''scipy''']



			def __init__(		self     :						Any				,	*A     :						Any				,	**A     :						str)     ->					Union[str, Any]:



						"""simple docstring"""






						requires_backends(self				,	['torch', 'scipy'])



			@classmethod
			def 					_lowerCamelCase (		cls     :						Dict				,	*A     :						int				,	**A     :						Optional[Any])     ->					Dict:



						"""simple docstring"""






						requires_backends(cls				,	['torch', 'scipy'])



			@classmethod
			def 					_lowerCamelCase (		cls     :						Optional[Any]				,	*A     :						List[Any]				,	**A     :						Union[str, Any])     ->					str:



						"""simple docstring"""






						requires_backends(cls				,	['torch', 'scipy'])





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     __lowerCAmelCase					:



										@staticmethod
										def 					_lowerCamelCase (		*A     :						Union[str, Any]				,	**A     :						List[Any])     ->					Union[str, Any]:



													"""simple docstring"""






													pass







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



			@require_torch
			def 					_lowerCamelCase (		self     :						List[str])     ->					Tuple:



						"""simple docstring"""






						_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(A				,	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(A)				,	[
						        [{'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'}],
						    ]				,	)

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



			@require_tf
			def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



						"""simple docstring"""






						_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(A				,	candidate_labels=['a', 'b', 'c'])

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

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



			@slow
			@require_torch
			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[Any]:



						"""simple docstring"""






						_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(A				,	candidate_labels=['cat', 'plane', 'remote'])

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

						_UpperCAmelCase	 =     image_classifier([image] * 5				,	candidate_labels=['cat', 'plane', 'remote']				,	batch_size=2)
						self.assertEqual(
						    nested_simplify(A)				,	[
						        [
						            {'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 					_lowerCamelCase (		self     :						List[str])     ->					Any:



						"""simple docstring"""






						_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(A				,	candidate_labels=['cat', 'plane', 'remote'])
						self.assertEqual(
						    nested_simplify(A)				,	[
						        {'score': 0.5_1_1, 'label': 'remote'},
						        {'score': 0.4_8_5, 'label': 'cat'},
						        {'score': 0.0_0_4, 'label': 'plane'},
						    ]				,	)

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