





import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, 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        SCREAMING_SNAKE_CASE__					:



        @staticmethod
        def 							UpperCamelCase     (				*__lowerCamelCase,**__lowerCamelCase					):
               pass




@is_pipeline_test
@require_vision
@require_torch
class        SCREAMING_SNAKE_CASE__					(		unittest.TestCase ):
 __SCREAMING_SNAKE_CASE							=      MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		pipeline(
            '''zero-shot-object-detection''',model='''hf-internal-testing/tiny-random-owlvit-object-detection'''					)

        A__		   =		[
            {
                '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
            }
        ]
        return object_detector, examples



 def 							UpperCamelCase     (				self,__lowerCamelCase,__lowerCamelCase					):
        A__		   =		object_detector(examples[0],threshold=0.0					)

        A__		   =		len(__lowerCamelCase					)
        self.assertGreater(__lowerCamelCase,0					)
        self.assertEqual(
            __lowerCamelCase,[
                {
                    '''score''': ANY(__lowerCamelCase					),
                    '''label''': ANY(__lowerCamelCase					),
                    '''box''': {'''xmin''': ANY(__lowerCamelCase					), '''ymin''': ANY(__lowerCamelCase					), '''xmax''': ANY(__lowerCamelCase					), '''ymax''': ANY(__lowerCamelCase					)},
                }
                for i in range(__lowerCamelCase					)
            ],)



 @require_tf
 @unittest.skip('''Zero Shot Object Detection not implemented in TF'''					)
 def 							UpperCamelCase     (				self					):
        pass



 @require_torch
 def 							UpperCamelCase     (				self					):
        A__		   =		pipeline(
            '''zero-shot-object-detection''',model='''hf-internal-testing/tiny-random-owlvit-object-detection'''					)

        A__		   =		object_detector(
            '''./tests/fixtures/tests_samples/COCO/000000039769.png''',candidate_labels=['''cat''', '''remote''', '''couch'''],threshold=0.64,)

        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
            ],)

        A__		   =		object_detector(
            [
                {
                    '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                }
            ],threshold=0.64,)

        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                [
                    {'''score''': 0.7235, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.7218, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.7184, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
                    {'''score''': 0.6748, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.6656, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.6614, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
                    {'''score''': 0.6456, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                    {'''score''': 0.642, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
                    {'''score''': 0.6419, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
                ]
            ],)



 @require_torch
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		pipeline('''zero-shot-object-detection'''					)

        A__		   =		object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg''',candidate_labels=['''cat''', '''remote''', '''couch'''],)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
            ],)

        A__		   =		object_detector(
            [
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
                {
                    '''image''': '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                    '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
                },
            ],)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                [
                    {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
                [
                    {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                    {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                    {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
                    {'''score''': 0.1474, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
                    {'''score''': 0.1208, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
                ],
            ],)



 @require_tf
 @unittest.skip('''Zero Shot Object Detection not implemented in TF'''					)
 def 							UpperCamelCase     (				self					):
        pass



 @require_torch
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		0.2
        A__		   =		pipeline('''zero-shot-object-detection'''					)

        A__		   =		object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg''',candidate_labels=['''cat''', '''remote''', '''couch'''],threshold=__lowerCamelCase,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
                {'''score''': 0.2537, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
            ],)




 @require_torch
 @slow
 def 							UpperCamelCase     (				self					):
        A__		   =		2
        A__		   =		pipeline('''zero-shot-object-detection'''					)

        A__		   =		object_detector(
            '''http://images.cocodataset.org/val2017/000000039769.jpg''',candidate_labels=['''cat''', '''remote''', '''couch'''],top_k=__lowerCamelCase,)
        self.assertEqual(
            nested_simplify(__lowerCamelCase,decimals=4					),[
                {'''score''': 0.2868, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
                {'''score''': 0.277, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
            ],)












import warnings

from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline  # noqa F401


warnings.warn(
    'The `inpainting.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionInpaintPipeline` instead.'
)





