



'''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 					a__  (*A ,   **A       )    ->     Optional[Any]:


        """simple docstring"""



        pass






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



 '''simple docstring'''







 __lowerCamelCase    :       List[str]          =       MODEL_FOR_OBJECT_DETECTION_MAPPING



 def 					a__  (self ,   A ,   A ,   A       )    ->     List[Any]:


      """simple docstring"""



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



 def 					a__  (self ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png''' ,   threshold=0.0       )

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

      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(snake_case_ ,   threshold=0.0       )

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



 @require_tf
 @unittest.skip('''Object detection not implemented in TF'''       )
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      pass



 @require_torch
 def 					a__  (self       )    ->     Any:


      """simple docstring"""



      _a      =  '''hf-internal-testing/tiny-detr-mobilenetsv3'''

      _a      =  AutoModelForObjectDetection.from_pretrained(snake_case_       )
      _a      =  AutoFeatureExtractor.from_pretrained(snake_case_       )
      _a      =  ObjectDetectionPipeline(model=snake_case_ ,   feature_extractor=snake_case_       )

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

      self.assertEqual(
          nested_simplify(snake_case_ ,   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(snake_case_ ,   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       )    ->     Dict:


      """simple docstring"""



      _a      =  '''facebook/detr-resnet-50'''

      _a      =  AutoModelForObjectDetection.from_pretrained(snake_case_       )
      _a      =  AutoFeatureExtractor.from_pretrained(snake_case_       )
      _a      =  ObjectDetectionPipeline(model=snake_case_ ,   feature_extractor=snake_case_       )

      _a      =  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
      self.assertEqual(
          nested_simplify(snake_case_ ,   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(snake_case_ ,   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:


      """simple docstring"""



      _a      =  '''facebook/detr-resnet-50'''

      _a      =  pipeline('''object-detection''' ,   model=snake_case_       )

      _a      =  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
      self.assertEqual(
          nested_simplify(snake_case_ ,   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(snake_case_ ,   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       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  0.9985
      _a      =  '''facebook/detr-resnet-50'''

      _a      =  pipeline('''object-detection''' ,   model=snake_case_       )

      _a      =  object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg''' ,   threshold=snake_case_       )
      self.assertEqual(
          nested_simplify(snake_case_ ,   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       )    ->     str:


      """simple docstring"""



      _a      =  '''Narsil/layoutlmv3-finetuned-funsd'''
      _a      =  0.9993

      _a      =  pipeline('''object-detection''' ,   model=snake_case_ ,   threshold=snake_case_       )

      _a      =  object_detector(
          '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''       )
      self.assertEqual(
          nested_simplify(snake_case_ ,   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 collections.abc import Callable
from typing import Any, Generic, TypeVar

__SCREAMING_SNAKE_CASE     :Optional[int]       =				TypeVar('''T''')







class   A_	(  Generic[T]			):




 def __init__(					self       :    List[Any]	,			snake_case_       :    list[T]	,			snake_case_       :    Callable[[T, T], T]    ):
      _UpperCAmelCase     							=   None

      _UpperCAmelCase     							=   len(snake_case_    )
      _UpperCAmelCase     							=   [any_type for _ in range(self.N    )] + arr
      _UpperCAmelCase     							=   fnc
      self.build()




 def      lowercase	(					self       :    List[Any]    ):
      for p in range(self.N - 1	,			0	,			-1    ):
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Optional[Any]	,			snake_case_       :    int	,			snake_case_       :    T    ):
      p += self.N
      _UpperCAmelCase     							=   v
      while p > 1:
           _UpperCAmelCase     							=   p // 2
           _UpperCAmelCase     							=   self.fn(self.st[p * 2]	,			self.st[p * 2 + 1]    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    int	,			snake_case_       :    int    ):  # noqa: E741
      _UpperCAmelCase							,  _UpperCAmelCase     							=   l + self.N, r + self.N

      _UpperCAmelCase     							=   None
      while l <= r:
           if l % 2 == 1:
                _UpperCAmelCase     							=   self.st[l] if res is None else self.fn(snake_case_	,			self.st[l]    )
           if r % 2 == 0:
                _UpperCAmelCase     							=   self.st[r] if res is None else self.fn(snake_case_	,			self.st[r]    )
           _UpperCAmelCase							,  _UpperCAmelCase     							=   (l + 1) // 2, (r - 1) // 2
      return res


if __name__ == "__main__":
  from functools import reduce

  __SCREAMING_SNAKE_CASE     :Union[str, Any]       =				[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

  __SCREAMING_SNAKE_CASE     :List[str]       =				{
      0: 7,
      1: 2,
      2: 6,
      3: -14,
      4: 5,
      5: 4,
      6: 7,
      7: -10,
      8: 9,
      9: 10,
      10: 12,
      11: 1,
  }

  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, min)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, max)
  __SCREAMING_SNAKE_CASE     :Any       =				SegmentTree(test_array, lambda a, b: a + b)
  def 	UpperCAmelCase_							(					)		->       None:


       '''simple docstring'''



       for i in range(len(__lowercase       )       ):
            for j in range(__lowercase			,     len(__lowercase       )       ):
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(__lowercase			,     test_array[i : j + 1]       )
                 _UpperCAmelCase     							=   reduce(lambda __lowercase			,     __lowercase       : a + b			,     test_array[i : j + 1]       )
                 assert min_range == min_segment_tree.query(__lowercase			,     __lowercase       )
                 assert max_range == max_segment_tree.query(__lowercase			,     __lowercase       )
                 assert sum_range == sum_segment_tree.query(__lowercase			,     __lowercase       )

  test_all_segments()

  for index, value in test_updates.items():
    __SCREAMING_SNAKE_CASE     :str       =				value
    min_segment_tree.update(index, value)
    max_segment_tree.update(index, value)
    sum_segment_tree.update(index, value)
    test_all_segments()




