






'''simple docstring'''







import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


UpperCamelCase     :   Dict	      =		logging.get_logger(__name__)

enable_full_determinism()




class 		UpperCamelCase__     (a ,a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       UNetaDModel
		_UpperCamelCase						=       'sample'






		@property
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	4
								lowerCamelCase__								=	3
								lowerCamelCase__								=	(32, 32)

								lowerCamelCase__								=	floats_tensor((batch_size, num_channels) + sizes							).to(_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor([10]							).to(_lowerCAmelCase							)

								return {"sample": noise, "timestep": time_step}






		@property
		def       UpperCamelCase_       (   self							):
								return (3, 32, 32)






		@property
		def       UpperCamelCase_       (   self							):
								return (3, 32, 32)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	{
								    """block_out_channels""": (32, 64),
								    """down_block_types""": ("""DownBlock2D""", """AttnDownBlock2D"""),
								    """up_block_types""": ("""AttnUpBlock2D""", """UpBlock2D"""),
								    """attention_head_dim""": 3,
								    """out_channels""": 3,
								    """in_channels""": 3,
								    """layers_per_block""": 2,
								    """sample_size""": 32,
								}
								lowerCamelCase__								=	self.dummy_input
								return init_dict, inputs_dict




class 		UpperCamelCase__     (a ,a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       UNetaDModel
		_UpperCamelCase						=       'sample'






		@property
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	4
								lowerCamelCase__								=	4
								lowerCamelCase__								=	(32, 32)

								lowerCamelCase__								=	floats_tensor((batch_size, num_channels) + sizes							).to(_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor([10]							).to(_lowerCAmelCase							)

								return {"sample": noise, "timestep": time_step}






		@property
		def       UpperCamelCase_       (   self							):
								return (4, 32, 32)






		@property
		def       UpperCamelCase_       (   self							):
								return (4, 32, 32)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	{
								    """sample_size""": 32,
								    """in_channels""": 4,
								    """out_channels""": 4,
								    """layers_per_block""": 2,
								    """block_out_channels""": (32, 64),
								    """attention_head_dim""": 32,
								    """down_block_types""": ("""DownBlock2D""", """DownBlock2D"""),
								    """up_block_types""": ("""UpBlock2D""", """UpBlock2D"""),
								}
								lowerCamelCase__								=	self.dummy_input
								return init_dict, inputs_dict






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" ,output_loading_info=_lowerCAmelCase							)

								self.assertIsNotNone(_lowerCAmelCase							)
								self.assertEqual(len(loading_info["""missing_keys"""]							) ,0							)

								model.to(_lowerCAmelCase							)
								lowerCamelCase__								=	model(**self.dummy_input							).sample

								assert image is not None, "Make sure output is not None"






		@unittest.skipIf(torch_device != """cuda""" ,"""This test is supposed to run on GPU"""							)
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" ,output_loading_info=_lowerCAmelCase							)
								model.to(_lowerCAmelCase							)
								lowerCamelCase__								=	model(**self.dummy_input							).sample

								assert image is not None, "Make sure output is not None"






		@unittest.skipIf(torch_device != """cuda""" ,"""This test is supposed to run on GPU"""							)
		def       UpperCamelCase_       (   self							):
								# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
								lowerCamelCase__							,      lowerCamelCase__								=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update""" ,output_loading_info=_lowerCAmelCase							)
								model_accelerate.to(_lowerCAmelCase							)
								model_accelerate.eval()

								lowerCamelCase__								=	torch.randn(
								    1 ,model_accelerate.config.in_channels ,model_accelerate.config.sample_size ,model_accelerate.config.sample_size ,generator=torch.manual_seed(0							) ,)
								lowerCamelCase__								=	noise.to(_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor([10] * noise.shape[0]							).to(_lowerCAmelCase							)

								lowerCamelCase__								=	model_accelerate(_lowerCAmelCase ,_lowerCAmelCase							)["""sample"""]

								# two models don't need to stay in the device at the same time
								del model_accelerate
								torch.cuda.empty_cache()
								gc.collect()

								lowerCamelCase__							,      lowerCamelCase__								=	UNetaDModel.from_pretrained(
								    """fusing/unet-ldm-dummy-update""" ,output_loading_info=_lowerCAmelCase ,low_cpu_mem_usage=_lowerCAmelCase							)
								model_normal_load.to(_lowerCAmelCase							)
								model_normal_load.eval()
								lowerCamelCase__								=	model_normal_load(_lowerCAmelCase ,_lowerCAmelCase							)["""sample"""]

								assert torch_all_close(_lowerCAmelCase ,_lowerCAmelCase ,rtol=1E-3							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""							)
								model.eval()
								model.to(_lowerCAmelCase							)

								lowerCamelCase__								=	torch.randn(
								    1 ,model.config.in_channels ,model.config.sample_size ,model.config.sample_size ,generator=torch.manual_seed(0							) ,)
								lowerCamelCase__								=	noise.to(_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor([10] * noise.shape[0]							).to(_lowerCAmelCase							)

								with torch.no_grad():
														lowerCamelCase__								=	model(_lowerCAmelCase ,_lowerCAmelCase							).sample

								lowerCamelCase__								=	output[0, -1, -3:, -3:].flatten().cpu()
								# fmt: off
								lowerCamelCase__								=	torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]							)
								# fmt: on

								self.assertTrue(torch_all_close(_lowerCAmelCase ,_lowerCAmelCase ,rtol=1E-3							)							)




class 		UpperCamelCase__     (a ,a ,unittest.TestCase      ):




		'''simple docstring'''





		_UpperCamelCase						=       UNetaDModel
		_UpperCamelCase						=       'sample'






		@property
		def       UpperCamelCase_       (   self ,_lowerCAmelCase=(32, 32)							):
								lowerCamelCase__								=	4
								lowerCamelCase__								=	3

								lowerCamelCase__								=	floats_tensor((batch_size, num_channels) + sizes							).to(_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor(batch_size * [10]							).to(dtype=torch.intaa ,device=_lowerCAmelCase							)

								return {"sample": noise, "timestep": time_step}






		@property
		def       UpperCamelCase_       (   self							):
								return (3, 32, 32)






		@property
		def       UpperCamelCase_       (   self							):
								return (3, 32, 32)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	{
								    """block_out_channels""": [32, 64, 64, 64],
								    """in_channels""": 3,
								    """layers_per_block""": 1,
								    """out_channels""": 3,
								    """time_embedding_type""": """fourier""",
								    """norm_eps""": 1E-6,
								    """mid_block_scale_factor""": math.sqrt(2.0							),
								    """norm_num_groups""": None,
								    """down_block_types""": [
								        """SkipDownBlock2D""",
								        """AttnSkipDownBlock2D""",
								        """SkipDownBlock2D""",
								        """SkipDownBlock2D""",
								    ],
								    """up_block_types""": [
								        """SkipUpBlock2D""",
								        """SkipUpBlock2D""",
								        """AttnSkipUpBlock2D""",
								        """SkipUpBlock2D""",
								    ],
								}
								lowerCamelCase__								=	self.dummy_input
								return init_dict, inputs_dict






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256""" ,output_loading_info=_lowerCAmelCase							)
								self.assertIsNotNone(_lowerCAmelCase							)
								self.assertEqual(len(loading_info["""missing_keys"""]							) ,0							)

								model.to(_lowerCAmelCase							)
								lowerCamelCase__								=	self.dummy_input
								lowerCamelCase__								=	floats_tensor((4, 3) + (2_56, 2_56)							).to(_lowerCAmelCase							)
								lowerCamelCase__								=	noise
								lowerCamelCase__								=	model(**_lowerCAmelCase							)

								assert image is not None, "Make sure output is not None"






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""							)
								model.to(_lowerCAmelCase							)

								lowerCamelCase__								=	4
								lowerCamelCase__								=	3
								lowerCamelCase__								=	(2_56, 2_56)

								lowerCamelCase__								=	torch.ones((batch_size, num_channels) + sizes							).to(_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor(batch_size * [1E-4]							).to(_lowerCAmelCase							)

								with torch.no_grad():
														lowerCamelCase__								=	model(_lowerCAmelCase ,_lowerCAmelCase							).sample

								lowerCamelCase__								=	output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase__								=	torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_0980.7129, -2_0028.8535, 8148.2822, 2342.2905, 567.7608]							)
								# fmt: on

								self.assertTrue(torch_all_close(_lowerCAmelCase ,_lowerCAmelCase ,rtol=1E-2							)							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""							)
								model.to(_lowerCAmelCase							)

								lowerCamelCase__								=	4
								lowerCamelCase__								=	3
								lowerCamelCase__								=	(32, 32)

								lowerCamelCase__								=	torch.ones((batch_size, num_channels) + sizes							).to(_lowerCAmelCase							)
								lowerCamelCase__								=	torch.tensor(batch_size * [1E-4]							).to(_lowerCAmelCase							)

								with torch.no_grad():
														lowerCamelCase__								=	model(_lowerCAmelCase ,_lowerCAmelCase							).sample

								lowerCamelCase__								=	output[0, -3:, -3:, -1].flatten().cpu()
								# fmt: off
								lowerCamelCase__								=	torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]							)
								# fmt: on

								self.assertTrue(torch_all_close(_lowerCAmelCase ,_lowerCAmelCase ,rtol=1E-2							)							)






		def       UpperCamelCase_       (   self							):
								# not required for this model
								pass





'''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     __lowerCAmelCase		:





       '''simple docstring'''






       @staticmethod
       def  UpperCamelCase__						(*UpperCamelCase       :       Optional[Any]  ,		**UpperCamelCase       :       Tuple					):

              '''simple docstring'''







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





      '''simple docstring'''






      lowerCAmelCase__   :	Optional[int]       =				MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



      def  UpperCamelCase__						(self       :       int  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       List[str]  ,		UpperCamelCase       :       str					):

             '''simple docstring'''







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

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



      def  UpperCamelCase__						(self       :       Optional[Any]  ,		UpperCamelCase       :       Tuple  ,		UpperCamelCase       :       Optional[int]					):

             '''simple docstring'''







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

             lowercase__      =					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  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







             pass



      @require_torch
      def  UpperCamelCase__						(self       :       Any					):

             '''simple docstring'''







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

             lowercase__      =					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}},
                 ]  ,		)

             lowercase__      =					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  UpperCamelCase__						(self       :       str					):

             '''simple docstring'''







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

             lowercase__      =					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}},
                 ]  ,		)

             lowercase__      =					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  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             pass



      @require_torch
      @slow
      def  UpperCamelCase__						(self       :       Tuple					):

             '''simple docstring'''







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

             lowercase__      =					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  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







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

             lowercase__      =					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}},
                 ]  ,		)




