




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     UpperCamelCase_   :

      '''simple docstring'''






      @staticmethod
      def 	SCREAMING_SNAKE_CASE							(      *UpperCAmelCase__				:     Optional[Any] ,			**UpperCAmelCase__				:     List[Any])						->Union[str, Any]:





             '''simple docstring'''

             pass


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

    '''simple docstring'''






    UpperCAmelCase__							  =				MODEL_FOR_OBJECT_DETECTION_MAPPING


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     List[Any] ,			UpperCAmelCase__				:     Union[str, Any] ,			UpperCAmelCase__				:     List[str])						->Any:





           '''simple docstring'''

           A__							=   ObjectDetectionPipeline(model=UpperCAmelCase__ ,			image_processor=UpperCAmelCase__)
           return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     str)						->List[str]:





           '''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 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Optional[int]:





           '''simple docstring'''

           pass


    @require_torch
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->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 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[int]:





           '''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 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Optional[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 	SCREAMING_SNAKE_CASE							(      self				:     int)						->List[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 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->str:





           '''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}},
               ] ,			)







def SCREAMING_SNAKE_CASE    (	lowercase_ = 50						)       ->	int:



       """simple docstring"""

       A__							=   [1] * (length + 1)

       for row_length in range(3			,    length + 1						):
              for block_length in range(3			,    row_length + 1						):
                     for block_start in range(row_length - block_length						):
                            ways_number[row_length] += ways_number[
                                row_length - block_start - block_length - 1
                            ]

                     ways_number[row_length] += 1

       return ways_number[length]


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


