
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 							snake_case_ :





					@staticmethod
					def 						__A						(		*__lowerCAmelCase      ,						**__lowerCAmelCase      ):
							pass

@is_pipeline_test
@require_vision
@require_torch
class 							snake_case_ (				unittest.TestCase    ):
			__lowerCamelCase							:     Union[str, Any]						=			MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			pipeline(
					    'zero-shot-object-detection'      ,						model='hf-internal-testing/tiny-random-owlvit-object-detection'      )

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





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			object_detector(examples[0]      ,						threshold=0.0      )

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





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





			@require_torch
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			pipeline(
					    'zero-shot-object-detection'      ,						model='hf-internal-testing/tiny-random-owlvit-object-detection'      )

					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			object_detector(
					    './tests/fixtures/tests_samples/COCO/000000039769.png'      ,						candidate_labels=['cat', 'remote', 'couch']      ,						threshold=0.64      ,						)

					self.assertEqual(
					    nested_simplify(_UpperCamelCase      ,						decimals=4      )      ,						[
					        {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
					        {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
					        {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
					        {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
					        {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
					        {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
					        {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
					        {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
					        {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
					    ]      ,						)

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

					self.assertEqual(
					    nested_simplify(_UpperCamelCase      ,						decimals=4      )      ,						[
					        [
					            {'score': 0.72_35, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
					            {'score': 0.72_18, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
					            {'score': 0.71_84, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
					            {'score': 0.67_48, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
					            {'score': 0.66_56, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
					            {'score': 0.66_14, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
					            {'score': 0.64_56, 'label': 'remote', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
					            {'score': 0.6_42, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 274, 'xmax': 93, 'ymax': 297}},
					            {'score': 0.64_19, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
					        ]
					    ]      ,						)





			@require_torch
			@slow
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	str		  =			pipeline('zero-shot-object-detection'      )

					SCREAMING_SNAKE_CASE_					:	Any		  =			object_detector(
					    'http://images.cocodataset.org/val2017/000000039769.jpg'      ,						candidate_labels=['cat', 'remote', 'couch']      ,						)
					self.assertEqual(
					    nested_simplify(_UpperCamelCase      ,						decimals=4      )      ,						[
					        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
					        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
					        {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
					        {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
					        {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
					    ]      ,						)

					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			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(_UpperCamelCase      ,						decimals=4      )      ,						[
					        [
					            {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
					            {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
					            {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
					            {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
					            {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
					        ],
					        [
					            {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
					            {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
					            {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
					            {'score': 0.14_74, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
					            {'score': 0.12_08, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
					        ],
					    ]      ,						)





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





			@require_torch
			@slow
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			0.2
					SCREAMING_SNAKE_CASE_					:	str		  =			pipeline('zero-shot-object-detection'      )

					SCREAMING_SNAKE_CASE_					:	str		  =			object_detector(
					    'http://images.cocodataset.org/val2017/000000039769.jpg'      ,						candidate_labels=['cat', 'remote', 'couch']      ,						threshold=_UpperCamelCase      ,						)
					self.assertEqual(
					    nested_simplify(_UpperCamelCase      ,						decimals=4      )      ,						[
					        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
					        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
					        {'score': 0.25_37, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
					    ]      ,						)






			@require_torch
			@slow
			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	List[Any]		  =			2
					SCREAMING_SNAKE_CASE_					:	Any		  =			pipeline('zero-shot-object-detection'      )

					SCREAMING_SNAKE_CASE_					:	int		  =			object_detector(
					    'http://images.cocodataset.org/val2017/000000039769.jpg'      ,						candidate_labels=['cat', 'remote', 'couch']      ,						top_k=_UpperCamelCase      ,						)
					self.assertEqual(
					    nested_simplify(_UpperCamelCase      ,						decimals=4      )      ,						[
					        {'score': 0.28_68, 'label': 'cat', 'box': {'xmin': 324, 'ymin': 20, 'xmax': 640, 'ymax': 373}},
					        {'score': 0.2_77, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 177, 'ymax': 115}},
					    ]      ,						)













import warnings

from ...utils import logging
from .image_processing_glpn import GLPNImageProcessor


UpperCAmelCase_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):





     def __init__(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        warnings.warn(
            '''The class GLPNFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
            ''' use GLPNImageProcessor instead.'''				,							_UpperCamelCase				,							)
        super().__init__(*_UpperCamelCase				,							**_UpperCamelCase					)