



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  _lowercase			(       *_A      :     List[str]			,		**_A      :     int):
													pass




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							lowerCAmelCase__  (	unittest.TestCase						):
					__A     :    List[Any]           =						MODEL_FOR_OBJECT_DETECTION_MAPPING







					def  _lowercase			(       self      :     Optional[Any]			,		_A      :     Optional[Any]			,		_A      :     List[Any]			,		_A      :     int):
											A__	:							Union[str, Any]						    = ObjectDetectionPipeline(model=_A			,		image_processor=_A)
											return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







					def  _lowercase			(       self      :     str			,		_A      :     int			,		_A      :     Union[str, Any]):
											A__	:							Optional[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

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

											A__	:							int						    = [
											    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__	:							List[str]						    = 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  _lowercase			(       self      :     Dict):
											pass







					@require_torch
					def  _lowercase			(       self      :     str):
											A__	:							List[str]						    = "hf-internal-testing/tiny-detr-mobilenetsv3"

											A__	:							Union[str, Any]						    = AutoModelForObjectDetection.from_pretrained(_A)
											A__	:							Dict						    = AutoFeatureExtractor.from_pretrained(_A)
											A__	:							Union[str, Any]						    = ObjectDetectionPipeline(model=_A			,		feature_extractor=_A)

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

											self.assertEqual(
											    nested_simplify(_A			,		decimals=4)			,		[
											        {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
											        {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
											    ]			,		)

											A__	:							Any						    = 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": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
											            {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
											        ],
											        [
											            {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
											            {"score": 0.33_76, "label": "LABEL_0", "box": {"xmin": 159, "ymin": 120, "xmax": 480, "ymax": 359}},
											        ],
											    ]			,		)







					@require_torch
					@slow
					def  _lowercase			(       self      :     Dict):
											A__	:							Dict						    = "facebook/detr-resnet-50"

											A__	:							Dict						    = AutoModelForObjectDetection.from_pretrained(_A)
											A__	:							Tuple						    = AutoFeatureExtractor.from_pretrained(_A)
											A__	:							List[str]						    = ObjectDetectionPipeline(model=_A			,		feature_extractor=_A)

											A__	:							int						    = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
											self.assertEqual(
											    nested_simplify(_A			,		decimals=4)			,		[
											        {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
											        {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
											        {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
											        {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
											        {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
											    ]			,		)

											A__	:							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": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
											            {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
											            {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
											            {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
											            {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
											        ],
											        [
											            {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
											            {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
											            {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
											            {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
											            {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
											        ],
											    ]			,		)







					@require_torch
					@slow
					def  _lowercase			(       self      :     Any):
											A__	:							Any						    = "facebook/detr-resnet-50"

											A__	:							List[Any]						    = pipeline("object-detection"			,		model=_A)

											A__	:							Optional[int]						    = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg")
											self.assertEqual(
											    nested_simplify(_A			,		decimals=4)			,		[
											        {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
											        {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
											        {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
											        {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
											        {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
											    ]			,		)

											A__	:							Dict						    = object_detector(
											    [
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											        "http://images.cocodataset.org/val2017/000000039769.jpg",
											    ])
											self.assertEqual(
											    nested_simplify(_A			,		decimals=4)			,		[
											        [
											            {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
											            {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
											            {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
											            {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
											            {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
											        ],
											        [
											            {"score": 0.99_82, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 175, "ymax": 117}},
											            {"score": 0.99_60, "label": "remote", "box": {"xmin": 333, "ymin": 72, "xmax": 368, "ymax": 187}},
											            {"score": 0.99_55, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 639, "ymax": 473}},
											            {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
											            {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
											        ],
											    ]			,		)







					@require_torch
					@slow
					def  _lowercase			(       self      :     Union[str, Any]):
											A__	:							Union[str, Any]						    = 0.99_85
											A__	:							List[Any]						    = "facebook/detr-resnet-50"

											A__	:							Any						    = pipeline("object-detection"			,		model=_A)

											A__	:							List[Any]						    = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"			,		threshold=_A)
											self.assertEqual(
											    nested_simplify(_A			,		decimals=4)			,		[
											        {"score": 0.99_88, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 314, "ymax": 470}},
											        {"score": 0.99_87, "label": "cat", "box": {"xmin": 345, "ymin": 23, "xmax": 640, "ymax": 368}},
											    ]			,		)







					@require_torch
					@require_pytesseract
					@slow
					def  _lowercase			(       self      :     Any):
											A__	:							Union[str, Any]						    = "Narsil/layoutlmv3-finetuned-funsd"
											A__	:							Any						    = 0.99_93

											A__	:							int						    = pipeline("object-detection"			,		model=_A			,		threshold=_A)

											A__	:							Optional[Any]						    = object_detector(
											    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png")
											self.assertEqual(
											    nested_simplify(_A			,		decimals=4)			,		[
											        {"score": 0.99_93, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
											        {"score": 0.99_93, "label": "I-ANSWER", "box": {"xmin": 294, "ymin": 254, "xmax": 343, "ymax": 264}},
											    ]			,		)




def 	snake_case__       (					__lowercase       )		->		int:







						"""simple docstring"""

						if divisor % 5 == 0 or divisor % 2 == 0:
												return 0
						A__	:							Tuple						    = 1
						A__	:							Union[str, Any]						    = 1
						while repunit:
												A__	:							Union[str, Any]						    = (1_0 * repunit + 1) % divisor
												repunit_index += 1
						return repunit_index
def 	snake_case__       (					__lowercase = 1_0_0_0_0_0_0       )		->		int:







						"""simple docstring"""

						A__	:							Dict						    = limit - 1
						if divisor % 2 == 0:
												divisor += 1
						while least_divisible_repunit(__lowercase       ) <= limit:
												divisor += 2
						return divisor


if __name__ == "__main__":
		print(f"""{solution() = }""")