





'''simple docstring'''




import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()






def 			_lowerCAmelCase     (    __snake_case    :					Union[str, Any]     )	->	Optional[int]:
     __A		:					str						=			[tensor.shape for tensor in tensor_list]
     return all(shape == shapes[0] for shape in shapes[1:]     )


class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			StableDiffusionLatentUpscalePipeline
     lowerCAmelCase						     =			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
         '''height''',
         '''width''',
         '''cross_attention_kwargs''',
         '''negative_prompt_embeds''',
         '''prompt_embeds''',
     }
     lowerCAmelCase						     =			PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
     lowerCAmelCase						     =			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase						     =			frozenset(
         []				)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
     lowerCAmelCase						     =			frozenset([]				)

     lowerCAmelCase						     =			True


     @property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[int]						=			1
          __A		:					str						=			4
          __A		:					Dict						=			(16, 16)

          __A		:					List[Any]						=			floats_tensor((batch_size, num_channels) + sizes		,      rng=random.Random(0)).to(_UpperCAmelCase)
          return image


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          torch.manual_seed(0)
          __A		:					Dict						=			UNetaDConditionModel(
              act_fn='gelu'		,      attention_head_dim=8		,      norm_num_groups=_UpperCAmelCase		,      block_out_channels=[32, 32, 64, 64]		,      time_cond_proj_dim=160		,      conv_in_kernel=1		,      conv_out_kernel=1		,      cross_attention_dim=32		,      down_block_types=(
                  'KDownBlock2D',
                  'KCrossAttnDownBlock2D',
                  'KCrossAttnDownBlock2D',
                  'KCrossAttnDownBlock2D',
              )		,      in_channels=8		,      mid_block_type=_UpperCAmelCase		,      only_cross_attention=_UpperCAmelCase		,      out_channels=5		,      resnet_time_scale_shift='scale_shift'		,      time_embedding_type='fourier'		,      timestep_post_act='gelu'		,      up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')		,      )
          __A		:					int						=			AutoencoderKL(
              block_out_channels=[32, 32, 64, 64]		,      in_channels=3		,      out_channels=3		,      down_block_types=[
                  'DownEncoderBlock2D',
                  'DownEncoderBlock2D',
                  'DownEncoderBlock2D',
                  'DownEncoderBlock2D',
              ]		,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']		,      latent_channels=4		,      )
          __A		:					List[Any]						=			EulerDiscreteScheduler(prediction_type='sample')
          __A		:					Union[str, Any]						=			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='quick_gelu'		,      projection_dim=512		,      )
          __A		:					List[str]						=			CLIPTextModel(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

          __A		:					List[str]						=			{
              'unet': model.eval(),
              'vae': vae.eval(),
              'scheduler': scheduler,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
          }

          return components


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase=0):

          '''simple docstring'''






          if str(_UpperCAmelCase).startswith('mps'):
               __A		:					Optional[Any]						=			torch.manual_seed(_UpperCAmelCase)
          else:
               __A		:					Tuple						=			torch.Generator(device=_UpperCAmelCase).manual_seed(_UpperCAmelCase)
          __A		:					List[str]						=			{
              'prompt': 'A painting of a squirrel eating a burger',
              'image': self.dummy_image.cpu(),
              'generator': generator,
              'num_inference_steps': 2,
              'output_type': 'numpy',
          }
          return inputs


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Optional[Any]						=			'cpu'

          __A		:					str						=			self.get_dummy_components()
          __A		:					Optional[Any]						=			self.pipeline_class(**_UpperCAmelCase)
          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)

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

          self.assertEqual(image.shape		,      (1, 256, 256, 3))
          __A		:					Any						=			np.array(
              [0.47222412, 0.41921633, 0.44717434, 0.46874192, 0.42588258, 0.46150726, 0.4677534, 0.45583832, 0.48579055])
          __A		:					List[Any]						=			np.abs(image_slice.flatten() - expected_slice).max()
          self.assertLessEqual(_UpperCAmelCase		,      1e-3)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          super().test_attention_slicing_forward_pass(expected_max_diff=7e-3)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          super().test_inference_batch_single_identical(expected_max_diff=7e-3)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






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


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          super().test_save_load_local(expected_max_difference=3e-3)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          super().test_save_load_optional_components(expected_max_difference=3e-3)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Tuple						=			[
              'DDIMScheduler',
              'DDPMScheduler',
              'PNDMScheduler',
              'HeunDiscreteScheduler',
              'EulerAncestralDiscreteScheduler',
              'KDPM2DiscreteScheduler',
              'KDPM2AncestralDiscreteScheduler',
              'DPMSolverSDEScheduler',
          ]
          __A		:					int						=			self.get_dummy_components()
          __A		:					Union[str, Any]						=			self.pipeline_class(**_UpperCAmelCase)

          # make sure that PNDM does not need warm-up
          pipe.scheduler.register_to_config(skip_prk_steps=_UpperCAmelCase)

          pipe.to(_UpperCAmelCase)
          pipe.set_progress_bar_config(disable=_UpperCAmelCase)
          __A		:					int						=			self.get_dummy_inputs(_UpperCAmelCase)
          __A		:					Union[str, Any]						=			2

          __A		:					List[str]						=			[]
          for scheduler_enum in KarrasDiffusionSchedulers:
               if scheduler_enum.name in skip_schedulers:
                    # no sigma schedulers are not supported
                    # no schedulers
                    continue

               __A		:					List[str]						=			getattr(_UpperCAmelCase		,      scheduler_enum.name)
               __A		:					Optional[Any]						=			scheduler_cls.from_config(pipe.scheduler.config)
               __A		:					Any						=			pipe(**_UpperCAmelCase)[0]
               outputs.append(_UpperCAmelCase)

          assert check_same_shape(_UpperCAmelCase)




@require_torch_gpu
@slow
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		:					str						=			torch.manual_seed(33)

          __A		:					List[str]						=			StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'		,      torch_dtype=torch.floataa)
          pipe.to('cuda')

          __A		:					List[Any]						=			StableDiffusionLatentUpscalePipeline.from_pretrained(
              'stabilityai/sd-x2-latent-upscaler'		,      torch_dtype=torch.floataa)
          upscaler.to('cuda')

          __A		:					Any						=			'a photo of an astronaut high resolution, unreal engine, ultra realistic'

          __A		:					Any						=			pipe(_UpperCAmelCase		,      generator=_UpperCAmelCase		,      output_type='latent').images

          __A		:					int						=			upscaler(
              prompt=_UpperCAmelCase		,      image=_UpperCAmelCase		,      num_inference_steps=20		,      guidance_scale=0		,      generator=_UpperCAmelCase		,      output_type='np'		,      ).images[0]

          __A		:					Optional[Any]						=			load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy')
          assert np.abs((expected_image - image).mean()) < 5e-2


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			torch.manual_seed(33)

          __A		:					Union[str, Any]						=			StableDiffusionLatentUpscalePipeline.from_pretrained(
              'stabilityai/sd-x2-latent-upscaler'		,      torch_dtype=torch.floataa)
          upscaler.to('cuda')

          __A		:					Any						=			'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

          __A		:					Dict						=			load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png')

          __A		:					int						=			upscaler(
              prompt=_UpperCAmelCase		,      image=_UpperCAmelCase		,      num_inference_steps=20		,      guidance_scale=0		,      generator=_UpperCAmelCase		,      output_type='np'		,      ).images[0]

          __A		:					List[Any]						=			load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy')
          assert np.abs((expected_image - image).max()) < 5e-2


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 _UpperCamelCase				:
						'''simple docstring'''







						@staticmethod
						def 				__UpperCamelCase			(      *a				:			str   ,   **a				:			int						)				->							str:

							"""simple docstring"""

							pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MODEL_FOR_OBJECT_DETECTION_MAPPING
			def 				__UpperCamelCase			(      self				:			Optional[Any]   ,   a				:			str   ,   a				:			Optional[Any]   ,   a				:			Union[str, Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							ObjectDetectionPipeline(model=a   ,   image_processor=a						)
				return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
			def 				__UpperCamelCase			(      self				:			List[Any]   ,   a				:			Optional[int]   ,   a				:			Optional[int]						)				->							int:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"   ,   threshold=0.0						)

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

				import datasets

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

				SCREAMING_SNAKE_CASE :   Dict					  =							[
				    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"],
				]
				SCREAMING_SNAKE_CASE :   Tuple					  =							object_detector(a   ,   threshold=0.0						)

				self.assertEqual(len(a						)   ,   len(a						)						)
				for outputs in batch_outputs:
					self.assertGreater(len(a						)   ,   0						)
					for detected_object in outputs:
						self.assertEqual(
						    a   ,   {
						        "score": ANY(a						),
						        "label": ANY(a						),
						        "box": {"xmin": ANY(a						), "ymin": ANY(a						), "xmax": ANY(a						), "ymax": ANY(a						)},
						    }   ,   )
			@require_tf
			@unittest.skip("Object detection not implemented in TF"						)
			def 				__UpperCamelCase			(      self				:			Optional[int]						)				->							str:

				"""simple docstring"""

				pass
			@require_torch
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   str					  =							"hf-internal-testing/tiny-detr-mobilenetsv3"

				SCREAMING_SNAKE_CASE :   Dict					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Any					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   Tuple					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

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

				self.assertEqual(
				    nested_simplify(a   ,   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}},
				    ]   ,   )

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

				self.assertEqual(
				    nested_simplify(a   ,   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 				__UpperCamelCase			(      self				:			str						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							AutoModelForObjectDetection.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   List[str]					  =							AutoFeatureExtractor.from_pretrained(a						)
				SCREAMING_SNAKE_CASE :   int					  =							ObjectDetectionPipeline(model=a   ,   feature_extractor=a						)

				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   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}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   int					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]						)
				self.assertEqual(
				    nested_simplify(a   ,   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 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   Tuple					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   Tuple					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				self.assertEqual(
				    nested_simplify(a   ,   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}},
				    ]   ,   )

				SCREAMING_SNAKE_CASE :   str					  =							object_detector(
				    [
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				        "http://images.cocodataset.org/val2017/000000039769.jpg",
				    ]						)
				self.assertEqual(
				    nested_simplify(a   ,   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 				__UpperCamelCase			(      self				:			str						)				->							str:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Optional[int]					  =							0.9985
				SCREAMING_SNAKE_CASE :   int					  =							"facebook/detr-resnet-50"

				SCREAMING_SNAKE_CASE :   List[str]					  =							pipeline("object-detection"   ,   model=a						)

				SCREAMING_SNAKE_CASE :   str					  =							object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"   ,   threshold=a						)
				self.assertEqual(
				    nested_simplify(a   ,   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 				__UpperCamelCase			(      self				:			str						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Any					  =							"Narsil/layoutlmv3-finetuned-funsd"
				SCREAMING_SNAKE_CASE :   Dict					  =							0.9993

				SCREAMING_SNAKE_CASE :   str					  =							pipeline("object-detection"   ,   model=a   ,   threshold=a						)

				SCREAMING_SNAKE_CASE :   List[Any]					  =							object_detector(
				    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"						)
				self.assertEqual(
				    nested_simplify(a   ,   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}},
				    ]   ,   )