


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 							lowercase  :







         @staticmethod
         def 							lowercase__       (						*_lowercase :						Optional[int]   ,   **_lowercase :						Optional[Any]      ):
               pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							lowercase  (						unittest.TestCase			):
     lowerCamelCase			:			Union[str, Any]      = MODEL_FOR_OBJECT_DETECTION_MAPPING







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						Any   ,   _lowercase :						Dict   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		ObjectDetectionPipeline(model=_lowercase   ,   image_processor=_lowercase      )
           return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







     def 							lowercase__       (						self :						Optional[int]   ,   _lowercase :						Any   ,   _lowercase :						str      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''   ,   threshold=0.0      )

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

           import datasets

           SCREAMING_SNAKE_CASE__       :  List[str]						     =		datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,   '''image'''   ,   split='''test'''      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[
               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'''],
           ]
           SCREAMING_SNAKE_CASE__       :  Dict						     =		object_detector(_lowercase   ,   threshold=0.0      )

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







     @require_tf
     @unittest.skip('''Object detection not implemented in TF'''      )
     def 							lowercase__       (						self :						int      ):
           pass







     @require_torch
     def 							lowercase__       (						self :						str      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		'''hf-internal-testing/tiny-detr-mobilenetsv3'''

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		AutoModelForObjectDetection.from_pretrained(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		AutoFeatureExtractor.from_pretrained(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  str						     =		ObjectDetectionPipeline(model=_lowercase   ,   feature_extractor=_lowercase      )

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

           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
                   {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
               ]   ,   )

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

           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   [
                       {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
                       {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
                   ],
                   [
                       {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
                       {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
                   ],
               ]   ,   )







     @require_torch
     @slow
     def 							lowercase__       (						self :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		'''facebook/detr-resnet-50'''

           SCREAMING_SNAKE_CASE__       :  Dict						     =		AutoModelForObjectDetection.from_pretrained(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		AutoFeatureExtractor.from_pretrained(_lowercase      )
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		ObjectDetectionPipeline(model=_lowercase   ,   feature_extractor=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''      )
           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                   {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                   {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                   {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                   {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
               ]   ,   )

           SCREAMING_SNAKE_CASE__       :  str						     =		object_detector(
               [
                   '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                   '''http://images.cocodataset.org/val2017/000000039769.jpg''',
               ]      )
           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   [
                       {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                       {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                       {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                       {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                       {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
                   ],
                   [
                       {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                       {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                       {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                       {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                       {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
                   ],
               ]   ,   )







     @require_torch
     @slow
     def 							lowercase__       (						self :						int      ):
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		'''facebook/detr-resnet-50'''

           SCREAMING_SNAKE_CASE__       :  int						     =		pipeline('''object-detection'''   ,   model=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Dict						     =		object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''      )
           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                   {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                   {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                   {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                   {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
               ]   ,   )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		object_detector(
               [
                   '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                   '''http://images.cocodataset.org/val2017/000000039769.jpg''',
               ]      )
           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   [
                       {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                       {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                       {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                       {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                       {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
                   ],
                   [
                       {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
                       {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
                       {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
                       {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                       {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
                   ],
               ]   ,   )







     @require_torch
     @slow
     def 							lowercase__       (						self :						Any      ):
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		0.9985
           SCREAMING_SNAKE_CASE__       :  Tuple						     =		'''facebook/detr-resnet-50'''

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		pipeline('''object-detection'''   ,   model=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''   ,   threshold=_lowercase      )
           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
                   {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
               ]   ,   )







     @require_torch
     @require_pytesseract
     @slow
     def 							lowercase__       (						self :						str      ):
           SCREAMING_SNAKE_CASE__       :  List[str]						     =		'''Narsil/layoutlmv3-finetuned-funsd'''
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		0.9993

           SCREAMING_SNAKE_CASE__       :  str						     =		pipeline('''object-detection'''   ,   model=_lowercase   ,   threshold=_lowercase      )

           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		object_detector(
               '''https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'''      )
           self.assertEqual(
               nested_simplify(_lowercase   ,   decimals=4      )   ,   [
                   {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
                   {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
               ]   ,   )














'''simple docstring'''



from math import isqrt



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int     )	->      bool:

   """simple docstring"""
   return all(number % divisor != 0 for divisor in range(2,       isqrt(SCREAMING_SNAKE_CASE__     ) + 1     )     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int = 10**6     )	->      int:

   """simple docstring"""
   __a					  =     0
   __a					  =     1
   __a					  =     7
   while prime_candidate < max_prime:
      primes_count += is_prime(SCREAMING_SNAKE_CASE__     )

      cube_index += 1
      prime_candidate += 6 * cube_index

   return primes_count


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