
from __future__ import annotations

from typing import Any






def    lowerCamelCase__				(				__lowerCAmelCase  :  list[Any]				):




			"""simple docstring"""


			create_state_space_tree(__lowerCAmelCase    ,							[]    ,							0				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  list[Any]    ,							__lowerCAmelCase  :  list[Any]    ,							__lowerCAmelCase  :  int				):




			"""simple docstring"""


			if index == len(__lowerCAmelCase				):
						print(__lowerCAmelCase				)
						return

			create_state_space_tree(__lowerCAmelCase    ,							__lowerCAmelCase    ,							index + 1				)
			current_subsequence.append(sequence[index]				)
			create_state_space_tree(__lowerCAmelCase    ,							__lowerCAmelCase    ,							index + 1				)
			current_subsequence.pop()


if __name__ == "__main__":
			_A   =     [3, 1, 2, 4]
			generate_all_subsequences(seq)

			seq.clear()
			seq.extend(["A", "B", "C"])
			generate_all_subsequences(seq)





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}},
							    ]				,				)



