


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class 		_snake_case  (      __a						,    __a						,    unittest.TestCase							):
 SCREAMING_SNAKE_CASE__      =       StableDiffusionPanoramaPipeline
 SCREAMING_SNAKE_CASE__      =       TEXT_TO_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__      =       TEXT_TO_IMAGE_BATCH_PARAMS
 SCREAMING_SNAKE_CASE__      =       TEXT_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__      =       TEXT_TO_IMAGE_IMAGE_PARAMS




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.manual_seed(0    )
       a						:Tuple 							=       UNetaDConditionModel(
           block_out_channels=(32, 64)					,  layers_per_block=1					,  sample_size=32					,  in_channels=4					,  out_channels=4					,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')					,  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')					,  cross_attention_dim=32					,  )
       a						:Optional[Any] 							=       DDIMScheduler()
       torch.manual_seed(0    )
       a						:str 							=       AutoencoderKL(
           block_out_channels=[32, 64]					,  in_channels=3					,  out_channels=3					,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']					,  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']					,  latent_channels=4					,  )
       torch.manual_seed(0    )
       a						:Dict 							=       CLIPTextConfig(
           bos_token_id=0					,  eos_token_id=2					,  hidden_size=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  )
       a						:str 							=       CLIPTextModel(_UpperCamelCase    )
       a						:List[str] 							=       CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

       a						:Optional[Any] 							=       {
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase=0    ):
       a						:str 							=       torch.manual_seed(_UpperCamelCase    )
       a						:str 							=       {
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           # Setting height and width to None to prevent OOMs on CPU.
           '''height''': None,
           '''width''': None,
           '''num_inference_steps''': 1,
           '''guidance_scale''': 6.0,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:Any 							=       self.get_dummy_components()
       a						:Dict 							=       StableDiffusionPanoramaPipeline(**_UpperCamelCase    )
       a						:Optional[Any] 							=       sd_pipe.to(_UpperCamelCase    )
       sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

       a						:Dict 							=       self.get_dummy_inputs(_UpperCamelCase    )
       a						:str 							=       sd_pipe(**_UpperCamelCase    ).images
       a						:Dict 							=       image[0, -3:, -3:, -1]
       assert image.shape == (1, 64, 64, 3)

       a						:Optional[Any] 							=       np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().test_inference_batch_consistent(batch_sizes=[1, 2]    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().test_inference_batch_single_identical(batch_size=2					,  expected_max_diff=3.25e-3    )




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:int 							=       self.get_dummy_components()
       a						:Optional[Any] 							=       StableDiffusionPanoramaPipeline(**_UpperCamelCase    )
       a						:Optional[Any] 							=       sd_pipe.to(_UpperCamelCase    )
       sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

       a						:List[str] 							=       self.get_dummy_inputs(_UpperCamelCase    )
       a						:Optional[int] 							=       '''french fries'''
       a						:List[Any] 							=       sd_pipe(**_UpperCamelCase					,  negative_prompt=_UpperCamelCase    )
       a						:List[str] 							=       output.images
       a						:Any 							=       image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       a						:Any 							=       np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:str 							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:List[str] 							=       self.get_dummy_components()
       a						:int 							=       StableDiffusionPanoramaPipeline(**_UpperCamelCase    )
       a						:int 							=       sd_pipe.to(_UpperCamelCase    )
       sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

       a						:Optional[Any] 							=       self.get_dummy_inputs(_UpperCamelCase    )
       a						:Union[str, Any] 							=       sd_pipe(**_UpperCamelCase					,  view_batch_size=2    )
       a						:str 							=       output.images
       a						:Optional[Any] 							=       image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       a						:int 							=       np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[str] 							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:Optional[Any] 							=       self.get_dummy_components()
       a						:Tuple 							=       EulerAncestralDiscreteScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''    )
       a						:Optional[int] 							=       StableDiffusionPanoramaPipeline(**_UpperCamelCase    )
       a						:int 							=       sd_pipe.to(_UpperCamelCase    )
       sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

       a						:Optional[Any] 							=       self.get_dummy_inputs(_UpperCamelCase    )
       a						:Dict 							=       sd_pipe(**_UpperCamelCase    ).images
       a						:int 							=       image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       a						:List[Any] 							=       np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Tuple 							=       '''cpu'''  # ensure determinism for the device-dependent torch.Generator
       a						:int 							=       self.get_dummy_components()
       a						:int 							=       PNDMScheduler(
           beta_start=0.0_0085					,  beta_end=0.012					,  beta_schedule='''scaled_linear'''					,  skip_prk_steps=_UpperCamelCase    )
       a						:str 							=       StableDiffusionPanoramaPipeline(**_UpperCamelCase    )
       a						:List[Any] 							=       sd_pipe.to(_UpperCamelCase    )
       sd_pipe.set_progress_bar_config(disable=_UpperCamelCase    )

       a						:Tuple 							=       self.get_dummy_inputs(_UpperCamelCase    )
       a						:str 							=       sd_pipe(**_UpperCamelCase    ).images
       a						:List[Any] 							=       image[0, -3:, -3:, -1]

       assert image.shape == (1, 64, 64, 3)

       a						:List[str] 							=       np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]    )

       assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-2







@slow
@require_torch_gpu
class 		_snake_case  (      unittest.TestCase							):




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase=0    ):
       a						:Tuple 							=       torch.manual_seed(_UpperCamelCase    )
       a						:Optional[int] 							=       {
           '''prompt''': '''a photo of the dolomites''',
           '''generator''': generator,
           '''num_inference_steps''': 3,
           '''guidance_scale''': 7.5,
           '''output_type''': '''numpy''',
       }
       return inputs




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Any 							=       '''stabilityai/stable-diffusion-2-base'''
       a						:Union[str, Any] 							=       DDIMScheduler.from_pretrained(_UpperCamelCase					,  subfolder='''scheduler'''    )
       a						:Dict 							=       StableDiffusionPanoramaPipeline.from_pretrained(_UpperCamelCase					,  scheduler=_UpperCamelCase					,  safety_checker=_UpperCamelCase    )
       pipe.to(_UpperCamelCase    )
       pipe.set_progress_bar_config(disable=_UpperCamelCase    )
       pipe.enable_attention_slicing()

       a						:Dict 							=       self.get_inputs()
       a						:Any 							=       pipe(**_UpperCamelCase    ).images
       a						:Any 							=       image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2048, 3)

       a						:int 							=       np.array(
           [
               0.3696_8392,
               0.2702_5372,
               0.3244_6766,
               0.2837_9387,
               0.3636_3274,
               0.3073_3347,
               0.2710_0027,
               0.2705_4125,
               0.2553_6096,
           ]    )

       assert np.abs(expected_slice - image_slice    ).max() < 1e-2




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:List[Any] 							=       StableDiffusionPanoramaPipeline.from_pretrained(
           '''stabilityai/stable-diffusion-2-base'''					,  safety_checker=_UpperCamelCase    )
       a						:List[Any] 							=       LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
       pipe.to(_UpperCamelCase    )
       pipe.set_progress_bar_config(disable=_UpperCamelCase    )
       pipe.enable_attention_slicing()

       a						:Any 							=       self.get_inputs()
       a						:List[str] 							=       pipe(**_UpperCamelCase    ).images
       a						:Dict 							=       image[0, -3:, -3:, -1].flatten()

       assert image.shape == (1, 512, 2048, 3)

       a						:Tuple 							=       np.array(
           [
               [
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
                   0.0,
               ]
           ]    )

       assert np.abs(expected_slice - image_slice    ).max() < 1e-3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       a						:Optional[Any] 							=       0

       def callback_fn(_lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ) -> None:
             a						:Optional[Any] 							=       True
             nonlocal number_of_steps
             number_of_steps += 1
             if step == 1:
                   a						:Any 							=       latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 256)
                   a						:Union[str, Any] 							=       latents[0, -3:, -3:, -1]

                   a						:Optional[Any] 							=       np.array(
                       [
                           0.1868_1869,
                           0.3390_7816,
                           0.536_1276,
                           0.1443_2865,
                           -0.0285_6611,
                           -0.7394_1123,
                           0.2339_7987,
                           0.4732_2682,
                           -0.3782_3164,
                       ]    )
                   assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2
             elif step == 2:
                   a						:Any 							=       latents.detach().cpu().numpy()
                   assert latents.shape == (1, 4, 64, 256)
                   a						:Tuple 							=       latents[0, -3:, -3:, -1]

                   a						:int 							=       np.array(
                       [
                           0.1853_9645,
                           0.3398_7248,
                           0.537_8559,
                           0.1443_7142,
                           -0.0245_5261,
                           -0.733_8317,
                           0.2399_0755,
                           0.4735_6272,
                           -0.378_6505,
                       ]    )

                   assert np.abs(latents_slice.flatten() - expected_slice    ).max() < 5e-2

       a						:Union[str, Any] 							=       False

       a						:List[Any] 							=       '''stabilityai/stable-diffusion-2-base'''
       a						:Optional[int] 							=       DDIMScheduler.from_pretrained(_UpperCamelCase					,  subfolder='''scheduler'''    )
       a						:Optional[int] 							=       StableDiffusionPanoramaPipeline.from_pretrained(_UpperCamelCase					,  scheduler=_UpperCamelCase					,  safety_checker=_UpperCamelCase    )
       a						:Union[str, Any] 							=       pipe.to(_UpperCamelCase    )
       pipe.set_progress_bar_config(disable=_UpperCamelCase    )
       pipe.enable_attention_slicing()

       a						:Optional[int] 							=       self.get_inputs()
       pipe(**_UpperCamelCase					,  callback=_UpperCamelCase					,  callback_steps=1    )
       assert callback_fn.has_been_called
       assert number_of_steps == 3




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       a						:Any 							=       '''stabilityai/stable-diffusion-2-base'''
       a						:Optional[Any] 							=       DDIMScheduler.from_pretrained(_UpperCamelCase					,  subfolder='''scheduler'''    )
       a						:Any 							=       StableDiffusionPanoramaPipeline.from_pretrained(_UpperCamelCase					,  scheduler=_UpperCamelCase					,  safety_checker=_UpperCamelCase    )
       a						:Optional[Any] 							=       pipe.to(_UpperCamelCase    )
       pipe.set_progress_bar_config(disable=_UpperCamelCase    )
       pipe.enable_attention_slicing(1    )
       pipe.enable_sequential_cpu_offload()

       a						:str 							=       self.get_inputs()
       a						:List[Any] 							=       pipe(**_UpperCamelCase    )

       a						:int 							=       torch.cuda.max_memory_allocated()
       # make sure that less than 5.2 GB is allocated
       assert mem_bytes < 5.5 * 10**9







import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
			from PIL import Image
else:





			class 	_lowerCAmelCase					:




							@staticmethod
							def      __a						(					*_UpperCamelCase				,				**_UpperCamelCase     )				->      str:
										pass





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					=MODEL_FOR_OBJECT_DETECTION_MAPPING




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				image_processor=_UpperCamelCase     )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"				,				threshold=0.0     )

							self.assertGreater(len(_UpperCamelCase     )				,				0     )
							for detected_object in outputs:
										self.assertEqual(
										    _UpperCamelCase				,				{
										        "score": ANY(_UpperCamelCase     ),
										        "label": ANY(_UpperCamelCase     ),
										        "box": {"xmin": ANY(_UpperCamelCase     ), "ymin": ANY(_UpperCamelCase     ), "xmax": ANY(_UpperCamelCase     ), "ymax": ANY(_UpperCamelCase     )},
										    }				,				)

							import datasets

							lowerCAmelCase_						=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"				,				"image"				,				split="test"     )

							lowerCAmelCase_						=	[
							    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     ),
							    "http://images.cocodataset.org/val2017/000000039769.jpg",
							    # RGBA
							    dataset[0]["file"],
							    # LA
							    dataset[1]["file"],
							    # L
							    dataset[2]["file"],
							]
							lowerCAmelCase_						=	object_detector(_UpperCamelCase				,				threshold=0.0     )

							self.assertEqual(len(_UpperCamelCase     )				,				len(_UpperCamelCase     )     )
							for outputs in batch_outputs:
										self.assertGreater(len(_UpperCamelCase     )				,				0     )
										for detected_object in outputs:
													self.assertEqual(
													    _UpperCamelCase				,				{
													        "score": ANY(_UpperCamelCase     ),
													        "label": ANY(_UpperCamelCase     ),
													        "box": {"xmin": ANY(_UpperCamelCase     ), "ymin": ANY(_UpperCamelCase     ), "xmax": ANY(_UpperCamelCase     ), "ymax": ANY(_UpperCamelCase     )},
													    }				,				)




				@require_tf
				@unittest.skip("Object detection not implemented in TF"     )
				def      __a						(					self     )				->      Optional[int]:
							pass




				@require_torch
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"hf-internal-testing/tiny-detr-mobilenetsv3"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=0.0     )

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]				,				threshold=0.0				,				)

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							        [
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							        {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							        [
							            {"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
							            {"score": 0.9960, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
							            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
							            {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							            {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							        ],
							    ]				,				)




				@require_torch
				@slow
				def      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	0.9985
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=_UpperCamelCase     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
							        {"score": 0.9987, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
							    ]				,				)




				@require_torch
				@require_pytesseract
				@slow
				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	"Narsil/layoutlmv3-finetuned-funsd"
							lowerCAmelCase_						=	0.9993

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase				,				threshold=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector(
							    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				decimals=4     )				,				[
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
							    ]				,				)



