

'''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        A__   :



          @staticmethod
          def        snake_case_      (       *UpperCamelCase__		,					**UpperCamelCase__							)    ->   Optional[int]:





                 '''simple docstring'''
                 pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class        A__   (   unittest.TestCase      ):
    lowercase						       =		MODEL_FOR_OBJECT_DETECTION_MAPPING



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   ObjectDetectionPipeline(model=UpperCamelCase_		,					image_processor=UpperCamelCase_							)
           return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



    def        snake_case_      (       self		,					UpperCamelCase__		,					UpperCamelCase__							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   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_						       =   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(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        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           pass



    @require_torch
    def        snake_case_      (       self							)    ->   Optional[Any]:





           '''simple docstring'''
           A_						       =   '''hf-internal-testing/tiny-detr-mobilenetsv3'''

           A_						       =   AutoModelForObjectDetection.from_pretrained(UpperCamelCase_							)
           A_						       =   AutoFeatureExtractor.from_pretrained(UpperCamelCase_							)
           A_						       =   ObjectDetectionPipeline(model=UpperCamelCase_		,					feature_extractor=UpperCamelCase_							)

           A_						       =   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_						       =   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        snake_case_      (       self							)    ->   List[str]:





           '''simple docstring'''
           A_						       =   '''facebook/detr-resnet-50'''

           A_						       =   AutoModelForObjectDetection.from_pretrained(UpperCamelCase_							)
           A_						       =   AutoFeatureExtractor.from_pretrained(UpperCamelCase_							)
           A_						       =   ObjectDetectionPipeline(model=UpperCamelCase_		,					feature_extractor=UpperCamelCase_							)

           A_						       =   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_						       =   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        snake_case_      (       self							)    ->   List[Any]:





           '''simple docstring'''
           A_						       =   '''facebook/detr-resnet-50'''

           A_						       =   pipeline("""object-detection"""		,					model=UpperCamelCase_							)

           A_						       =   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_						       =   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        snake_case_      (       self							)    ->   Any:





           '''simple docstring'''
           A_						       =   0.9985
           A_						       =   '''facebook/detr-resnet-50'''

           A_						       =   pipeline("""object-detection"""		,					model=UpperCamelCase_							)

           A_						       =   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        snake_case_      (       self							)    ->   Dict:





           '''simple docstring'''
           A_						       =   '''Narsil/layoutlmv3-finetuned-funsd'''
           A_						       =   0.9993

           A_						       =   pipeline("""object-detection"""		,					model=UpperCamelCase_		,					threshold=UpperCamelCase_							)

           A_						       =   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}},
               ]		,					)




'''simple docstring'''
from __future__ import annotations

from math import pi, sqrt


def      a (     __a					,   __a  )    ->      tuple:



					'''simple docstring'''






					if inductance <= 0:
										raise ValueError('''Inductance cannot be 0 or negative'''  )

					elif capacitance <= 0:
										raise ValueError('''Capacitance cannot be 0 or negative'''  )

					else:
										return (
										    "Resonant frequency",
										    float(1 / (2 * pi * (sqrt(inductance * capacitance  )))  ),
										)


if __name__ == "__main__":
		import doctest

		doctest.testmod()