





'''simple docstring'''




import gc
import random
import unittest

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

from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class  SCREAMING_SNAKE_CASE    (a__					,		a__					,		a__					,		unittest.TestCase							):
     lowerCAmelCase					=       StableDiffusionInpaintPipeline
     lowerCAmelCase					=       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
     lowerCAmelCase					=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
     lowerCAmelCase					=       frozenset(
         []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     lowerCAmelCase					=       frozenset([]							)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A						: Optional[int]      =       UNetaDConditionModel(
              block_out_channels=(32, 64)						,	layers_per_block=2						,	sample_size=32						,	in_channels=9						,	out_channels=4						,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')						,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')						,	cross_attention_dim=32						,	attention_head_dim=(2, 4)						,	use_linear_projection=_UpperCAmelCase						,	)
          __A						: Optional[int]      =       PNDMScheduler(skip_prk_steps=_UpperCAmelCase)
          torch.manual_seed(0)
          __A						: List[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						,	sample_size=128						,	)
          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-0_5						,	num_attention_heads=4						,	num_hidden_layers=5						,	pad_token_id=1						,	vocab_size=1000						,	hidden_act='gelu'						,	projection_dim=512						,	)
          __A						: str      =       CLIPTextModel(_UpperCAmelCase)
          __A						: int      =       CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

          __A						: Union[str, 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						,	_UpperCAmelCase						,	_UpperCAmelCase=0):

          '''simple docstring'''






          __A						: List[str]      =       floats_tensor((1, 3, 32, 32)						,	rng=random.Random(_UpperCAmelCase)).to(_UpperCAmelCase)
          __A						: Union[str, Any]      =       image.cpu().permute(0						,	2						,	3						,	1)[0]
          __A						: Optional[Any]      =       Image.fromarray(np.uinta(_UpperCAmelCase)).convert('RGB').resize((64, 64))
          __A						: Tuple      =       Image.fromarray(np.uinta(image + 4)).convert('RGB').resize((64, 64))
          if str(_UpperCAmelCase).startswith('mps'):
               __A						: Tuple      =       torch.manual_seed(_UpperCAmelCase)
          else:
               __A						: Tuple      =       torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A						: Union[str, Any]      =       {
              'prompt': 'A painting of a squirrel eating a burger',
              'image': init_image,
              'mask_image': mask_image,
              'generator': generator,
              'num_inference_steps': 2,
              'guidance_scale': 6.0,
              'output_type': 'numpy',
          }
          return inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: str      =       'cpu'  # ensure determinism for the device-dependent torch.Generator
          __A						: Tuple      =       self.get_dummy_components()
          __A						: Dict      =       StableDiffusionInpaintPipeline(**_UpperCAmelCase)
          __A						: Union[str, Any]      =       sd_pipe.to(_UpperCAmelCase)
          sd_pipe.set_progress_bar_config(disable=_UpperCAmelCase)

          __A						: Union[str, Any]      =       self.get_dummy_inputs(_UpperCAmelCase)
          __A						: List[str]      =       sd_pipe(**_UpperCAmelCase).images
          __A						: Any      =       image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          __A						: Union[str, Any]      =       np.array([0.4727, 0.5735, 0.3941, 0.5446, 0.5926, 0.4394, 0.5062, 0.4654, 0.4476])

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().test_inference_batch_single_identical(expected_max_diff=3e-3)




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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-inpaint/init_image.png')
          __A						: int      =       load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png')
          __A						: str      =       load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
              '/yellow_cat_sitting_on_a_park_bench.npy')

          __A						: Tuple      =       'stabilityai/stable-diffusion-2-inpainting'
          __A						: int      =       StableDiffusionInpaintPipeline.from_pretrained(_UpperCAmelCase						,	safety_checker=_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing()

          __A						: Dict      =       'Face of a yellow cat, high resolution, sitting on a park bench'

          __A						: Union[str, Any]      =       torch.manual_seed(0)
          __A						: List[str]      =       pipe(
              prompt=_UpperCAmelCase						,	image=_UpperCAmelCase						,	mask_image=_UpperCAmelCase						,	generator=_UpperCAmelCase						,	output_type='np'						,	)
          __A						: Dict      =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image).max() < 9e-3


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Union[str, Any]      =       load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-inpaint/init_image.png')
          __A						: Dict      =       load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png')
          __A						: int      =       load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint'
              '/yellow_cat_sitting_on_a_park_bench_fp16.npy')

          __A						: int      =       'stabilityai/stable-diffusion-2-inpainting'
          __A						: Optional[int]      =       StableDiffusionInpaintPipeline.from_pretrained(
              _UpperCAmelCase						,	torch_dtype=torch.floataa						,	safety_checker=_UpperCAmelCase						,	)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing()

          __A						: Union[str, Any]      =       'Face of a yellow cat, high resolution, sitting on a park bench'

          __A						: Optional[int]      =       torch.manual_seed(0)
          __A						: Optional[int]      =       pipe(
              prompt=_UpperCAmelCase						,	image=_UpperCAmelCase						,	mask_image=_UpperCAmelCase						,	generator=_UpperCAmelCase						,	output_type='np'						,	)
          __A						: Optional[int]      =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image).max() < 5e-1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          torch.cuda.empty_cache()
          torch.cuda.reset_max_memory_allocated()
          torch.cuda.reset_peak_memory_stats()

          __A						: Any      =       load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/sd2-inpaint/init_image.png')
          __A						: Dict      =       load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png')

          __A						: Any      =       'stabilityai/stable-diffusion-2-inpainting'
          __A						: Optional[int]      =       PNDMScheduler.from_pretrained(_UpperCAmelCase						,	subfolder='scheduler')
          __A						: str      =       StableDiffusionInpaintPipeline.from_pretrained(
              _UpperCAmelCase						,	safety_checker=_UpperCAmelCase						,	scheduler=_UpperCAmelCase						,	torch_dtype=torch.floataa						,	)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          pipe.enable_attention_slicing(1)
          pipe.enable_sequential_cpu_offload()

          __A						: Any      =       'Face of a yellow cat, high resolution, sitting on a park bench'

          __A						: Any      =       torch.manual_seed(0)
          __A						: Dict      =       pipe(
              prompt=_UpperCAmelCase						,	image=_UpperCAmelCase						,	mask_image=_UpperCAmelCase						,	generator=_UpperCAmelCase						,	num_inference_steps=2						,	output_type='np'						,	)

          __A						: Dict      =       torch.cuda.max_memory_allocated()
          # make sure that less than 2.65 GB is allocated
          assert mem_bytes < 2.65 * 10**9






'''simple docstring'''




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  SCREAMING_SNAKE_CASE    :


       @staticmethod
       def 							SCREAMING_SNAKE_CASE  (						*_UpperCAmelCase						,	**_UpperCAmelCase):

            '''simple docstring'''






            pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  SCREAMING_SNAKE_CASE    (unittest.TestCase							):
     lowerCAmelCase					=       MODEL_FOR_OBJECT_DETECTION_MAPPING


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: Dict      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	image_processor=_UpperCAmelCase)
          return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase):

          '''simple docstring'''






          __A						: str      =       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

          __A						: Tuple      =       datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,	'image'						,	split='test')

          __A						: List[str]      =       [
              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'],
          ]
          __A						: Union[str, Any]      =       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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[str]      =       'hf-internal-testing/tiny-detr-mobilenetsv3'

          __A						: Any      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: int      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: Any      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Dict      =       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}},
              ]						,	)

          __A						: Optional[Any]      =       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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[Any]      =       'facebook/detr-resnet-50'

          __A						: Union[str, Any]      =       AutoModelForObjectDetection.from_pretrained(_UpperCAmelCase)
          __A						: Tuple      =       AutoFeatureExtractor.from_pretrained(_UpperCAmelCase)
          __A						: List[Any]      =       ObjectDetectionPipeline(model=_UpperCAmelCase						,	feature_extractor=_UpperCAmelCase)

          __A						: Union[str, Any]      =       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}},
              ]						,	)

          __A						: Tuple      =       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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: Any      =       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}},
              ]						,	)

          __A						: str      =       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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: List[Any]      =       0.9985
          __A						: List[Any]      =       'facebook/detr-resnet-50'

          __A						: List[str]      =       pipeline('object-detection'						,	model=_UpperCAmelCase)

          __A						: List[Any]      =       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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Optional[int]      =       'Narsil/layoutlmv3-finetuned-funsd'
          __A						: Tuple      =       0.9993

          __A						: str      =       pipeline('object-detection'						,	model=_UpperCAmelCase						,	threshold=_UpperCAmelCase)

          __A						: Optional[int]      =       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}},
              ]						,	)