



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase							 =					logging.get_logger(__name__)

def 			A_      (	__a     :				int		):

				"""simple docstring"""
				a__				      =		DPTConfig()

				if "large" in checkpoint_url:
								a__				      =		1_024
								a__				      =		4_096
								a__				      =		24
								a__				      =		16
								a__				      =		[5, 11, 17, 23]
								a__				      =		[256, 512, 1_024, 1_024]
								a__				      =		(1, 384, 384)

				if "ade" in checkpoint_url:
								a__				      =		True

								a__				      =		150
								a__				      =		"""huggingface/label-files"""
								a__				      =		"""ade20k-id2label.json"""
								a__				      =		json.load(open(cached_download(hf_hub_url(__a ,		__a ,		repo_type="""dataset"""		)		) ,		"""r"""		)		)
								a__				      =		{int(__a		): v for k, v in idalabel.items()}
								a__				      =		idalabel
								a__				      =		{v: k for k, v in idalabel.items()}
								a__				      =		[1, 150, 480, 480]

				return config, expected_shape

def 			A_      (	__a     :				int		):

				"""simple docstring"""
				a__				      =		["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
				for k in ignore_keys:
								state_dict.pop(__a ,		__a		)

def 			A_      (	__a     :				Union[str, Any]		):

				"""simple docstring"""
				if (
				    "pretrained.model" in name
				    and "cls_token" not in name
				    and "pos_embed" not in name
				    and "patch_embed" not in name
				):
								a__				      =		name.replace("""pretrained.model""" ,		"""dpt.encoder"""		)
				if "pretrained.model" in name:
								a__				      =		name.replace("""pretrained.model""" ,		"""dpt.embeddings"""		)
				if "patch_embed" in name:
								a__				      =		name.replace("""patch_embed""" ,		"""patch_embeddings"""		)
				if "pos_embed" in name:
								a__				      =		name.replace("""pos_embed""" ,		"""position_embeddings"""		)
				if "attn.proj" in name:
								a__				      =		name.replace("""attn.proj""" ,		"""attention.output.dense"""		)
				if "proj" in name and "project" not in name:
								a__				      =		name.replace("""proj""" ,		"""projection"""		)
				if "blocks" in name:
								a__				      =		name.replace("""blocks""" ,		"""layer"""		)
				if "mlp.fc1" in name:
								a__				      =		name.replace("""mlp.fc1""" ,		"""intermediate.dense"""		)
				if "mlp.fc2" in name:
								a__				      =		name.replace("""mlp.fc2""" ,		"""output.dense"""		)
				if "norm1" in name:
								a__				      =		name.replace("""norm1""" ,		"""layernorm_before"""		)
				if "norm2" in name:
								a__				      =		name.replace("""norm2""" ,		"""layernorm_after"""		)
				if "scratch.output_conv" in name:
								a__				      =		name.replace("""scratch.output_conv""" ,		"""head"""		)
				if "scratch" in name:
								a__				      =		name.replace("""scratch""" ,		"""neck"""		)
				if "layer1_rn" in name:
								a__				      =		name.replace("""layer1_rn""" ,		"""convs.0"""		)
				if "layer2_rn" in name:
								a__				      =		name.replace("""layer2_rn""" ,		"""convs.1"""		)
				if "layer3_rn" in name:
								a__				      =		name.replace("""layer3_rn""" ,		"""convs.2"""		)
				if "layer4_rn" in name:
								a__				      =		name.replace("""layer4_rn""" ,		"""convs.3"""		)
				if "refinenet" in name:
								a__				      =		int(name[len("""neck.refinenet"""		) : len("""neck.refinenet"""		) + 1]		)
								# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
								a__				      =		name.replace(F'''refinenet{layer_idx}''' ,		F'''fusion_stage.layers.{abs(layer_idx-4		)}'''		)
				if "out_conv" in name:
								a__				      =		name.replace("""out_conv""" ,		"""projection"""		)
				if "resConfUnit1" in name:
								a__				      =		name.replace("""resConfUnit1""" ,		"""residual_layer1"""		)
				if "resConfUnit2" in name:
								a__				      =		name.replace("""resConfUnit2""" ,		"""residual_layer2"""		)
				if "conv1" in name:
								a__				      =		name.replace("""conv1""" ,		"""convolution1"""		)
				if "conv2" in name:
								a__				      =		name.replace("""conv2""" ,		"""convolution2"""		)
				# readout blocks
				if "pretrained.act_postprocess1.0.project.0" in name:
								a__				      =		name.replace("""pretrained.act_postprocess1.0.project.0""" ,		"""neck.reassemble_stage.readout_projects.0.0"""		)
				if "pretrained.act_postprocess2.0.project.0" in name:
								a__				      =		name.replace("""pretrained.act_postprocess2.0.project.0""" ,		"""neck.reassemble_stage.readout_projects.1.0"""		)
				if "pretrained.act_postprocess3.0.project.0" in name:
								a__				      =		name.replace("""pretrained.act_postprocess3.0.project.0""" ,		"""neck.reassemble_stage.readout_projects.2.0"""		)
				if "pretrained.act_postprocess4.0.project.0" in name:
								a__				      =		name.replace("""pretrained.act_postprocess4.0.project.0""" ,		"""neck.reassemble_stage.readout_projects.3.0"""		)
				# resize blocks
				if "pretrained.act_postprocess1.3" in name:
								a__				      =		name.replace("""pretrained.act_postprocess1.3""" ,		"""neck.reassemble_stage.layers.0.projection"""		)
				if "pretrained.act_postprocess1.4" in name:
								a__				      =		name.replace("""pretrained.act_postprocess1.4""" ,		"""neck.reassemble_stage.layers.0.resize"""		)
				if "pretrained.act_postprocess2.3" in name:
								a__				      =		name.replace("""pretrained.act_postprocess2.3""" ,		"""neck.reassemble_stage.layers.1.projection"""		)
				if "pretrained.act_postprocess2.4" in name:
								a__				      =		name.replace("""pretrained.act_postprocess2.4""" ,		"""neck.reassemble_stage.layers.1.resize"""		)
				if "pretrained.act_postprocess3.3" in name:
								a__				      =		name.replace("""pretrained.act_postprocess3.3""" ,		"""neck.reassemble_stage.layers.2.projection"""		)
				if "pretrained.act_postprocess4.3" in name:
								a__				      =		name.replace("""pretrained.act_postprocess4.3""" ,		"""neck.reassemble_stage.layers.3.projection"""		)
				if "pretrained.act_postprocess4.4" in name:
								a__				      =		name.replace("""pretrained.act_postprocess4.4""" ,		"""neck.reassemble_stage.layers.3.resize"""		)
				if "pretrained" in name:
								a__				      =		name.replace("""pretrained""" ,		"""dpt"""		)
				if "bn" in name:
								a__				      =		name.replace("""bn""" ,		"""batch_norm"""		)
				if "head" in name:
								a__				      =		name.replace("""head""" ,		"""head.head"""		)
				if "encoder.norm" in name:
								a__				      =		name.replace("""encoder.norm""" ,		"""layernorm"""		)
				if "auxlayer" in name:
								a__				      =		name.replace("""auxlayer""" ,		"""auxiliary_head.head"""		)

				return name

def 			A_      (	__a     :				Union[str, Any] ,		__a     :				Optional[int]		):

				"""simple docstring"""
				for i in range(config.num_hidden_layers		):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								a__				      =		state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''		)
								a__				      =		state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''		)
								# next, add query, keys and values (in that order) to the state dict
								a__				      =		in_proj_weight[: config.hidden_size, :]
								a__				      =		in_proj_bias[: config.hidden_size]
								a__				      =		in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								a__				      =		in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								a__				      =		in_proj_weight[
								    -config.hidden_size :, :
								]
								a__				      =		in_proj_bias[-config.hidden_size :]

def 			A_      (	):

				"""simple docstring"""
				a__				      =		"""http://images.cocodataset.org/val2017/000000039769.jpg"""
				a__				      =		Image.open(requests.get(__a ,		stream=__a		).raw		)
				return im

@torch.no_grad()
def 			A_      (	__a     :				Optional[int] ,		__a     :				Optional[int] ,		__a     :				Tuple ,		__a     :				Union[str, Any]		):

				"""simple docstring"""
				a__      ,			a__				      =		get_dpt_config(__a		)
				# load original state_dict from URL
				a__				      =		torch.hub.load_state_dict_from_url(__a ,		map_location="""cpu"""		)
				# remove certain keys
				remove_ignore_keys_(__a		)
				# rename keys
				for key in state_dict.copy().keys():
								a__				      =		state_dict.pop(__a		)
								a__				      =		val
				# read in qkv matrices
				read_in_q_k_v(__a ,		__a		)

				# load HuggingFace model
				a__				      =		DPTForSemanticSegmentation(__a		) if """ade""" in checkpoint_url else DPTForDepthEstimation(__a		)
				model.load_state_dict(__a		)
				model.eval()

				# Check outputs on an image
				a__				      =		480 if """ade""" in checkpoint_url else 384
				a__				      =		DPTImageProcessor(size=__a		)

				a__				      =		prepare_img()
				a__				      =		image_processor(__a ,		return_tensors="""pt"""		)

				# forward pass
				a__				      =		model(**__a		).logits if """ade""" in checkpoint_url else model(**__a		).predicted_depth

				# Assert logits
				a__				      =		torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]		)
				if "ade" in checkpoint_url:
								a__				      =		torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]		)
				assert outputs.shape == torch.Size(__a		)
				assert (
				    torch.allclose(outputs[0, 0, :3, :3] ,		__a ,		atol=1e-4		)
				    if "ade" in checkpoint_url
				    else torch.allclose(outputs[0, :3, :3] ,		__a		)
				)

				Path(__a		).mkdir(exist_ok=__a		)
				print(F'''Saving model to {pytorch_dump_folder_path}'''		)
				model.save_pretrained(__a		)
				print(F'''Saving image processor to {pytorch_dump_folder_path}'''		)
				image_processor.save_pretrained(__a		)

				if push_to_hub:
								print("""Pushing model to hub..."""		)
								model.push_to_hub(
								    repo_path_or_name=Path(__a ,		__a		) ,		organization="""nielsr""" ,		commit_message="""Add model""" ,		use_temp_dir=__a ,		)
								image_processor.push_to_hub(
								    repo_path_or_name=Path(__a ,		__a		) ,		organization="""nielsr""" ,		commit_message="""Add image processor""" ,		use_temp_dir=__a ,		)


if __name__ == "__main__":
	UpperCAmelCase							 =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--checkpoint_url""",
	    default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
	    type=str,
	    help="""URL of the original DPT checkpoint you'd like to convert.""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Path to the output PyTorch model directory.""",
	)
	parser.add_argument(
	    """--push_to_hub""",
	    action="""store_true""",
	)
	parser.add_argument(
	    """--model_name""",
	    default="""dpt-large""",
	    type=str,
	    help="""Name of the model, in case you're pushing to the hub.""",
	)

	UpperCAmelCase							 =					parser.parse_args()
	convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






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  __snake_case      :







								'''simple docstring'''






								@staticmethod
								def _a      (							*a_  ,  **a_		):
												pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class  __snake_case      (   unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__				:      Dict         =					MODEL_FOR_OBJECT_DETECTION_MAPPING


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		ObjectDetectionPipeline(model=a_  ,  image_processor=a_		)
											return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		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

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

											a__				      =		[
											    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__				      =		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 _a      (							self		):
											pass


							@require_torch
							def _a      (							self		):
											a__				      =		"""hf-internal-testing/tiny-detr-mobilenetsv3"""

											a__				      =		AutoModelForObjectDetection.from_pretrained(a_		)
											a__				      =		AutoFeatureExtractor.from_pretrained(a_		)
											a__				      =		ObjectDetectionPipeline(model=a_  ,  feature_extractor=a_		)

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

											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											        {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											    ]  ,  )

											a__				      =		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""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											            {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											        ],
											        [
											            {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											            {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											        ],
											    ]  ,  )


							@require_torch
							@slow
							def _a      (							self		):
											a__				      =		"""facebook/detr-resnet-50"""

											a__				      =		AutoModelForObjectDetection.from_pretrained(a_		)
											a__				      =		AutoFeatureExtractor.from_pretrained(a_		)
											a__				      =		ObjectDetectionPipeline(model=a_  ,  feature_extractor=a_		)

											a__				      =		object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											        {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											        {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											        {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											        {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											    ]  ,  )

											a__				      =		object_detector(
											    [
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											    ]		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											    ]  ,  )


							@require_torch
							@slow
							def _a      (							self		):
											a__				      =		"""facebook/detr-resnet-50"""

											a__				      =		pipeline("""object-detection"""  ,  model=a_		)

											a__				      =		object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											        {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											        {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											        {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											        {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											    ]  ,  )

											a__				      =		object_detector(
											    [
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											    ]		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											    ]  ,  )


							@require_torch
							@slow
							def _a      (							self		):
											a__				      =		0.9_985
											a__				      =		"""facebook/detr-resnet-50"""

											a__				      =		pipeline("""object-detection"""  ,  model=a_		)

											a__				      =		object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""  ,  threshold=a_		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											        {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											    ]  ,  )







							@require_torch
							@require_pytesseract
							@slow
							def _a      (							self		):
											a__				      =		"""Narsil/layoutlmv3-finetuned-funsd"""
											a__				      =		0.9_993

											a__				      =		pipeline("""object-detection"""  ,  model=a_  ,  threshold=a_		)

											a__				      =		object_detector(
											    """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
											        {"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
											    ]  ,  )

