


'''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			:
  '''simple docstring'''




  @staticmethod
  def   __UpperCamelCase   (    *lowerCamelCase_		,      **lowerCamelCase_      )      ->    str:
     pass







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




 __lowerCAmelCase						: Union[str, Any]							  =		MODEL_FOR_OBJECT_DETECTION_MAPPING




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Optional[int]:
    _a					:  Union[str, Any]						=  ObjectDetectionPipeline(model=lowerCamelCase_		,      image_processor=lowerCamelCase_      )
    return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    str:
    _a					:  Any						=  object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'		,      threshold=0.0      )

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

    import datasets

    _a					:  Tuple						=  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,      'image'		,      split='test'      )

    _a					:  str						=  [
        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					:  Any						=  object_detector(lowerCamelCase_		,      threshold=0.0      )

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




 @require_tf
 @unittest.skip('Object detection not implemented in TF'      )
 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    pass




 @require_torch
 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  int						=  'hf-internal-testing/tiny-detr-mobilenetsv3'

    _a					:  List[Any]						=  AutoModelForObjectDetection.from_pretrained(lowerCamelCase_      )
    _a					:  Optional[int]						=  AutoFeatureExtractor.from_pretrained(lowerCamelCase_      )
    _a					:  Dict						=  ObjectDetectionPipeline(model=lowerCamelCase_		,      feature_extractor=lowerCamelCase_      )

    _a					:  List[Any]						=  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,      threshold=0.0      )

    self.assertEqual(
        nested_simplify(lowerCamelCase_		,      decimals=4      )		,      [
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
        ]		,      )

    _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(lowerCamelCase_		,      decimals=4      )		,      [
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
            ],
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
            ],
        ]		,      )




 @require_torch
 @slow
 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Optional[Any]						=  'facebook/detr-resnet-50'

    _a					:  Union[str, Any]						=  AutoModelForObjectDetection.from_pretrained(lowerCamelCase_      )
    _a					:  Dict						=  AutoFeatureExtractor.from_pretrained(lowerCamelCase_      )
    _a					:  int						=  ObjectDetectionPipeline(model=lowerCamelCase_		,      feature_extractor=lowerCamelCase_      )

    _a					:  Dict						=  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'      )
    self.assertEqual(
        nested_simplify(lowerCamelCase_		,      decimals=4      )		,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
        ]		,      )

    _a					:  Any						=  object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]      )
    self.assertEqual(
        nested_simplify(lowerCamelCase_		,      decimals=4      )		,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
            ],
        ]		,      )




 @require_torch
 @slow
 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a					:  Optional[Any]						=  'facebook/detr-resnet-50'

    _a					:  str						=  pipeline('object-detection'		,      model=lowerCamelCase_      )

    _a					:  List[str]						=  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'      )
    self.assertEqual(
        nested_simplify(lowerCamelCase_		,      decimals=4      )		,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
        ]		,      )

    _a					:  int						=  object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]      )
    self.assertEqual(
        nested_simplify(lowerCamelCase_		,      decimals=4      )		,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
            ],
        ]		,      )




 @require_torch
 @slow
 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  Union[str, Any]						=  0.9985
    _a					:  Tuple						=  'facebook/detr-resnet-50'

    _a					:  Optional[int]						=  pipeline('object-detection'		,      model=lowerCamelCase_      )

    _a					:  List[Any]						=  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,      threshold=lowerCamelCase_      )
    self.assertEqual(
        nested_simplify(lowerCamelCase_		,      decimals=4      )		,      [
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
        ]		,      )




 @require_torch
 @require_pytesseract
 @slow
 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Any						=  'Narsil/layoutlmv3-finetuned-funsd'
    _a					:  Optional[int]						=  0.9993

    _a					:  Optional[Any]						=  pipeline('object-detection'		,      model=lowerCamelCase_		,      threshold=lowerCamelCase_      )

    _a					:  List[Any]						=  object_detector(
        'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'      )
    self.assertEqual(
        nested_simplify(lowerCamelCase_		,      decimals=4      )		,      [
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_9_4, 'ymin': 2_5_4, 'xmax': 3_4_3, 'ymax': 2_6_4}},
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_9_4, 'ymin': 2_5_4, 'xmax': 3_4_3, 'ymax': 2_6_4}},
        ]		,      )






'''simple docstring'''




from ..utils import DummyObject, requires_backends







class 					a			(				metaclass=snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: Union[str, Any]							  =		["""note_seq"""]




 def __init__(    self		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    int:
    requires_backends(self		,      ['note_seq']      )




 @classmethod
 def   __UpperCamelCase   (    cls		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    Union[str, Any]:
    requires_backends(cls		,      ['note_seq']      )




 @classmethod
 def   __UpperCamelCase   (    cls		,      *lowerCamelCase_		,      **lowerCamelCase_      )      ->    List[str]:
    requires_backends(cls		,      ['note_seq']      )


