





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     lowercase		(    *lowerCAmelCase_			:		List[str] ,  **lowerCAmelCase_			:		Union[str, Any]					)							->						str:
          pass



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

  """simple docstring"""


  a_      =			MODEL_FOR_OBJECT_DETECTION_MAPPING






  def     lowercase		(    self			:		List[str] ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		Dict ,  lowerCAmelCase_			:		List[str]					)							->						int:
     __lowerCAmelCase							     =     ObjectDetectionPipeline(model=lowerCAmelCase_ ,  image_processor=lowerCAmelCase_					)
     return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






  def     lowercase		(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Optional[Any] ,  lowerCAmelCase_			:		int					)							->						Optional[Any]:
     __lowerCAmelCase							     =     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

     __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(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     lowercase		(    self			:		List[Any]					)							->						Any:
     pass






  @require_torch
  def     lowercase		(    self			:		List[Any]					)							->						str:
     __lowerCAmelCase							     =     'hf-internal-testing/tiny-detr-mobilenetsv3'

     __lowerCAmelCase							     =     AutoModelForObjectDetection.from_pretrained(lowerCAmelCase_					)
     __lowerCAmelCase							     =     AutoFeatureExtractor.from_pretrained(lowerCAmelCase_					)
     __lowerCAmelCase							     =     ObjectDetectionPipeline(model=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_					)

     __lowerCAmelCase							     =     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' ,  threshold=0.0					)

     self.assertEqual(
         nested_simplify(lowerCAmelCase_ ,  decimals=4					) ,  [
             {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
             {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
         ] ,  )

     __lowerCAmelCase							     =     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': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
                 {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
             ],
             [
                 {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
                 {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 1_5_9, 'ymin': 1_2_0, 'xmax': 4_8_0, 'ymax': 3_5_9}},
             ],
         ] ,  )






  @require_torch
  @slow
  def     lowercase		(    self			:		Tuple					)							->						List[Any]:
     __lowerCAmelCase							     =     'facebook/detr-resnet-50'

     __lowerCAmelCase							     =     AutoModelForObjectDetection.from_pretrained(lowerCAmelCase_					)
     __lowerCAmelCase							     =     AutoFeatureExtractor.from_pretrained(lowerCAmelCase_					)
     __lowerCAmelCase							     =     ObjectDetectionPipeline(model=lowerCAmelCase_ ,  feature_extractor=lowerCAmelCase_					)

     __lowerCAmelCase							     =     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'					)
     self.assertEqual(
         nested_simplify(lowerCAmelCase_ ,  decimals=4					) ,  [
             {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
             {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
             {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
             {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
             {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
         ] ,  )

     __lowerCAmelCase							     =     object_detector(
         [
             'http://images.cocodataset.org/val2017/000000039769.jpg',
             'http://images.cocodataset.org/val2017/000000039769.jpg',
         ]					)
     self.assertEqual(
         nested_simplify(lowerCAmelCase_ ,  decimals=4					) ,  [
             [
                 {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                 {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                 {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                 {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                 {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
             ],
             [
                 {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                 {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                 {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                 {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                 {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
             ],
         ] ,  )






  @require_torch
  @slow
  def     lowercase		(    self			:		Optional[Any]					)							->						List[Any]:
     __lowerCAmelCase							     =     'facebook/detr-resnet-50'

     __lowerCAmelCase							     =     pipeline('object-detection' ,  model=lowerCAmelCase_					)

     __lowerCAmelCase							     =     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'					)
     self.assertEqual(
         nested_simplify(lowerCAmelCase_ ,  decimals=4					) ,  [
             {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
             {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
             {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
             {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
             {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
         ] ,  )

     __lowerCAmelCase							     =     object_detector(
         [
             'http://images.cocodataset.org/val2017/000000039769.jpg',
             'http://images.cocodataset.org/val2017/000000039769.jpg',
         ]					)
     self.assertEqual(
         nested_simplify(lowerCAmelCase_ ,  decimals=4					) ,  [
             [
                 {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                 {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                 {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                 {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                 {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
             ],
             [
                 {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 4_0, 'ymin': 7_0, 'xmax': 1_7_5, 'ymax': 1_1_7}},
                 {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 3_3_3, 'ymin': 7_2, 'xmax': 3_6_8, 'ymax': 1_8_7}},
                 {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_3_9, 'ymax': 4_7_3}},
                 {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
                 {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
             ],
         ] ,  )






  @require_torch
  @slow
  def     lowercase		(    self			:		Dict					)							->						int:
     __lowerCAmelCase							     =     0.99_85
     __lowerCAmelCase							     =     'facebook/detr-resnet-50'

     __lowerCAmelCase							     =     pipeline('object-detection' ,  model=lowerCAmelCase_					)

     __lowerCAmelCase							     =     object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' ,  threshold=lowerCAmelCase_					)
     self.assertEqual(
         nested_simplify(lowerCAmelCase_ ,  decimals=4					) ,  [
             {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 1_3, 'ymin': 5_2, 'xmax': 3_1_4, 'ymax': 4_7_0}},
             {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 3_4_5, 'ymin': 2_3, 'xmax': 6_4_0, 'ymax': 3_6_8}},
         ] ,  )






  @require_torch
  @require_pytesseract
  @slow
  def     lowercase		(    self			:		Optional[int]					)							->						str:
     __lowerCAmelCase							     =     'Narsil/layoutlmv3-finetuned-funsd'
     __lowerCAmelCase							     =     0.99_93

     __lowerCAmelCase							     =     pipeline('object-detection' ,  model=lowerCAmelCase_ ,  threshold=lowerCAmelCase_					)

     __lowerCAmelCase							     =     object_detector(
         'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'					)
     self.assertEqual(
         nested_simplify(lowerCAmelCase_ ,  decimals=4					) ,  [
             {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 2_9_4, 'ymin': 2_5_4, 'xmax': 3_4_3, 'ymax': 2_6_4}},
             {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 2_9_4, 'ymin': 2_5_4, 'xmax': 3_4_3, 'ymax': 2_6_4}},
         ] ,  )








from math import factorial



class     _UpperCAmelCase   :

  """simple docstring"""


  def __init__(    self			:		Dict ,  lowerCAmelCase_			:		Optional[int] ,  lowerCAmelCase_			:		str					)							->						Union[str, Any]:
     __lowerCAmelCase							     =     real
     if isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     [1] * rank
     else:
        __lowerCAmelCase							     =     rank






  def __repr__(    self			:		Optional[Any]					)							->						Tuple:
     return (
         f"""{self.real}+"""
         f"""{"+".join(str(lowerCAmelCase_					)+"E"+str(n+1					)for n,dual in enumerate(self.duals					)					)}"""
     )






  def     lowercase		(    self			:		str					)							->						Dict:
     __lowerCAmelCase							     =     self.duals.copy()
     while cur[-1] == 0:
        cur.pop(-1					)
     return Dual(self.real ,  lowerCAmelCase_					)






  def __add__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		Tuple					)							->						Optional[Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        return Dual(self.real + other ,  self.duals					)
     __lowerCAmelCase							     =     self.duals.copy()
     __lowerCAmelCase							     =     other.duals.copy()
     if len(lowerCAmelCase_					) > len(lowerCAmelCase_					):
        o_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     elif len(lowerCAmelCase_					) < len(lowerCAmelCase_					):
        s_dual.extend([1] * (len(lowerCAmelCase_					) - len(lowerCAmelCase_					))					)
     __lowerCAmelCase							     =     []
     for i in range(len(lowerCAmelCase_					)					):
        new_duals.append(s_dual[i] + o_dual[i]					)
     return Dual(self.real + other.real ,  lowerCAmelCase_					)

  a_      =			__add__






  def __sub__(    self			:		int ,  lowerCAmelCase_			:		Dict					)							->						Optional[Any]:
     return self + other * -1






  def __mul__(    self			:		int ,  lowerCAmelCase_			:		Optional[int]					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i * other					)
        return Dual(self.real * other ,  lowerCAmelCase_					)
     __lowerCAmelCase							     =     [0] * (len(self.duals					) + len(other.duals					) + 1)
     for i, item in enumerate(self.duals					):
        for j, jtem in enumerate(other.duals					):
           new_duals[i + j + 1] += item * jtem
     for k in range(len(self.duals					)					):
        new_duals[k] += self.duals[k] * other.real
     for index in range(len(other.duals					)					):
        new_duals[index] += other.duals[index] * self.real
     return Dual(self.real * other.real ,  lowerCAmelCase_					)

  a_      =			__mul__






  def __truediv__(    self			:		Union[str, Any] ,  lowerCAmelCase_			:		str					)							->						Dict:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i / other					)
        return Dual(self.real / other ,  lowerCAmelCase_					)
     raise ValueError






  def __floordiv__(    self			:		str ,  lowerCAmelCase_			:		List[Any]					)							->						Union[str, Any]:
     if not isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        __lowerCAmelCase							     =     []
        for i in self.duals:
           new_duals.append(i // other					)
        return Dual(self.real // other ,  lowerCAmelCase_					)
     raise ValueError






  def __pow__(    self			:		Tuple ,  lowerCAmelCase_			:		Dict					)							->						List[str]:
     if n < 0 or isinstance(lowerCAmelCase_ ,  lowerCAmelCase_					):
        raise ValueError('power must be a positive integer'					)
     if n == 0:
        return 1
     if n == 1:
        return self
     __lowerCAmelCase							     =     self
     for _ in range(n - 1					):
        x *= self
     return x





def   a_   (			lowerCAmelCase_				:      List[str],     lowerCAmelCase_				:      Optional[Any],     lowerCAmelCase_				:      Optional[Any]     ):
   if not callable(lowerCAmelCase_     ):
      raise ValueError('differentiate() requires a function as input for func'     )
   if not isinstance(lowerCAmelCase_,     (float, int)     ):
      raise ValueError('differentiate() requires a float as input for position'     )
   if not isinstance(lowerCAmelCase_,     lowerCAmelCase_     ):
      raise ValueError('differentiate() requires an int as input for order'     )
   __lowerCAmelCase							     =     Dual(lowerCAmelCase_,     1     )
   __lowerCAmelCase							     =     func(lowerCAmelCase_     )
   if order == 0:
      return result.real
   return result.duals[order - 1] * factorial(lowerCAmelCase_     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     def   a_   (			lowerCAmelCase_				:      int     ):
        return y**2 * y**4




     print(differentiate(f, 9, 2))

