



"""simple docstring"""




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 _lowercase					(*SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    ):

															"""simple docstring"""







															pass





@is_pipeline_test
@require_vision
@require_torch
class  snake_case     (							unittest.TestCase   ):
							UpperCAmelCase__						     =      MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = pipeline(
									    '''zero-shot-object-detection'''       ,       model='''hf-internal-testing/tiny-random-owlvit-object-detection'''    )

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

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = object_detector(examples[0]       ,       threshold=0.0    )

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

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

									"""simple docstring"""







									pass

							@require_torch
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = pipeline(
									    '''zero-shot-object-detection'''       ,       model='''hf-internal-testing/tiny-random-owlvit-object-detection'''    )

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

									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_       ,       decimals=4    )       ,       [
									        {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
									        {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
									        {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
									        {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
									        {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
									        {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
									        {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
									        {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
									        {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
									    ]       ,       )

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

									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_       ,       decimals=4    )       ,       [
									        [
									            {'''score''': 0.72_35, '''label''': '''cat''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
									            {'''score''': 0.72_18, '''label''': '''remote''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
									            {'''score''': 0.71_84, '''label''': '''couch''', '''box''': {'''xmin''': 2_04, '''ymin''': 1_67, '''xmax''': 2_32, '''ymax''': 1_90}},
									            {'''score''': 0.67_48, '''label''': '''remote''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
									            {'''score''': 0.66_56, '''label''': '''cat''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
									            {'''score''': 0.66_14, '''label''': '''couch''', '''box''': {'''xmin''': 5_71, '''ymin''': 83, '''xmax''': 5_98, '''ymax''': 1_03}},
									            {'''score''': 0.64_56, '''label''': '''remote''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
									            {'''score''': 0.6_42, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 2_74, '''xmax''': 93, '''ymax''': 2_97}},
									            {'''score''': 0.64_19, '''label''': '''cat''', '''box''': {'''xmin''': 4_94, '''ymin''': 1_05, '''xmax''': 5_21, '''ymax''': 1_27}},
									        ]
									    ]       ,       )

							@require_torch
							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = pipeline('''zero-shot-object-detection'''    )

									SCREAMING_SNAKE_CASE_						 = object_detector(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,       candidate_labels=['''cat''', '''remote''', '''couch''']       ,       )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_       ,       decimals=4    )       ,       [
									        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
									        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
									        {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
									        {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
									        {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
									    ]       ,       )

									SCREAMING_SNAKE_CASE_						 = 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(SCREAMING_SNAKE_CASE_       ,       decimals=4    )       ,       [
									        [
									            {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
									            {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
									            {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
									            {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
									            {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
									        ],
									        [
									            {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
									            {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
									            {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
									            {'''score''': 0.14_74, '''label''': '''remote''', '''box''': {'''xmin''': 3_35, '''ymin''': 74, '''xmax''': 3_71, '''ymax''': 1_87}},
									            {'''score''': 0.12_08, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 6_42, '''ymax''': 4_76}},
									        ],
									    ]       ,       )

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

									"""simple docstring"""







									pass

							@require_torch
							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 0.2
									SCREAMING_SNAKE_CASE_						 = pipeline('''zero-shot-object-detection'''    )

									SCREAMING_SNAKE_CASE_						 = object_detector(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,       candidate_labels=['''cat''', '''remote''', '''couch''']       ,       threshold=SCREAMING_SNAKE_CASE_       ,       )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_       ,       decimals=4    )       ,       [
									        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
									        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
									        {'''score''': 0.25_37, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 3_15, '''ymax''': 4_72}},
									    ]       ,       )







							@require_torch
							@slow
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 2
									SCREAMING_SNAKE_CASE_						 = pipeline('''zero-shot-object-detection'''    )

									SCREAMING_SNAKE_CASE_						 = object_detector(
									    '''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,       candidate_labels=['''cat''', '''remote''', '''couch''']       ,       top_k=SCREAMING_SNAKE_CASE_       ,       )
									self.assertEqual(
									    nested_simplify(SCREAMING_SNAKE_CASE_       ,       decimals=4    )       ,       [
									        {'''score''': 0.28_68, '''label''': '''cat''', '''box''': {'''xmin''': 3_24, '''ymin''': 20, '''xmax''': 6_40, '''ymax''': 3_73}},
									        {'''score''': 0.2_77, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 1_77, '''ymax''': 1_15}},
									    ]       ,       )

"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''glpn'''

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=3       ,       SCREAMING_SNAKE_CASE_=4       ,       SCREAMING_SNAKE_CASE_=[2, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[8, 4, 2, 1]       ,       SCREAMING_SNAKE_CASE_=[32, 64, 1_60, 2_56]       ,       SCREAMING_SNAKE_CASE_=[7, 3, 3, 3]       ,       SCREAMING_SNAKE_CASE_=[4, 2, 2, 2]       ,       SCREAMING_SNAKE_CASE_=[1, 2, 5, 8]       ,       SCREAMING_SNAKE_CASE_=[4, 4, 4, 4]       ,       SCREAMING_SNAKE_CASE_="gelu"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=0.1       ,       SCREAMING_SNAKE_CASE_=1e-6       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=10       ,       SCREAMING_SNAKE_CASE_=-1       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(**SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = num_channels
									SCREAMING_SNAKE_CASE_						 = num_encoder_blocks
									SCREAMING_SNAKE_CASE_						 = depths
									SCREAMING_SNAKE_CASE_						 = sr_ratios
									SCREAMING_SNAKE_CASE_						 = hidden_sizes
									SCREAMING_SNAKE_CASE_						 = patch_sizes
									SCREAMING_SNAKE_CASE_						 = strides
									SCREAMING_SNAKE_CASE_						 = mlp_ratios
									SCREAMING_SNAKE_CASE_						 = num_attention_heads
									SCREAMING_SNAKE_CASE_						 = hidden_act
									SCREAMING_SNAKE_CASE_						 = hidden_dropout_prob
									SCREAMING_SNAKE_CASE_						 = attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = drop_path_rate
									SCREAMING_SNAKE_CASE_						 = layer_norm_eps
									SCREAMING_SNAKE_CASE_						 = decoder_hidden_size
									SCREAMING_SNAKE_CASE_						 = max_depth
									SCREAMING_SNAKE_CASE_						 = head_in_index