import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
    from PIL import Image
else:



    class  SCREAMING_SNAKE_CASE_ :
      '''simple docstring'''


      @staticmethod
      def 		SCREAMING_SNAKE_CASE_		(				*SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	**SCREAMING_SNAKE_CASE__    :					int     )    ->				int:
             pass



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


  lowercase		:     Any            =				MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Union[str, Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]      ,	SCREAMING_SNAKE_CASE__    :					Optional[Any]     )    ->				Dict:
         A						:     str         =pipeline(
             'zero-shot-object-detection'      ,	model='hf-internal-testing/tiny-random-owlvit-object-detection'     )

         A						:     Tuple         =[
             {
                 'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                 'candidate_labels': ['cat', 'remote', 'couch'],
             }
         ]
         return object_detector, examples







  def 		SCREAMING_SNAKE_CASE_		(				self    :					str      ,	SCREAMING_SNAKE_CASE__    :					Tuple      ,	SCREAMING_SNAKE_CASE__    :					Optional[int]     )    ->				Dict:
         A						:     Any         =object_detector(examples[0]      ,	threshold=0.0     )

         A						:     Dict         =len(SCREAMING_SNAKE_CASE__     )
         self.assertGreater(SCREAMING_SNAKE_CASE__      ,	0     )
         self.assertEqual(
             SCREAMING_SNAKE_CASE__      ,	[
                 {
                     'score': ANY(SCREAMING_SNAKE_CASE__     ),
                     'label': ANY(SCREAMING_SNAKE_CASE__     ),
                     'box': {'xmin': ANY(SCREAMING_SNAKE_CASE__     ), 'ymin': ANY(SCREAMING_SNAKE_CASE__     ), 'xmax': ANY(SCREAMING_SNAKE_CASE__     ), 'ymax': ANY(SCREAMING_SNAKE_CASE__     )},
                 }
                 for i in range(SCREAMING_SNAKE_CASE__     )
             ]      ,	)







  @require_tf
  @unittest.skip('Zero Shot Object Detection not implemented in TF'     )
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Tuple     )    ->				List[Any]:
         pass







  @require_torch
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[Any]     )    ->				Any:
         A						:     Any         =pipeline(
             'zero-shot-object-detection'      ,	model='hf-internal-testing/tiny-random-owlvit-object-detection'     )

         A						:     List[str]         =object_detector(
             './tests/fixtures/tests_samples/COCO/000000039769.png'      ,	candidate_labels=['cat', 'remote', 'couch']      ,	threshold=0.6_4      ,	)

         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[
                 {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                 {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                 {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                 {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                 {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                 {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                 {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
                 {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
                 {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
             ]      ,	)

         A						:     int         =object_detector(
             [
                 {
                     'image': './tests/fixtures/tests_samples/COCO/000000039769.png',
                     'candidate_labels': ['cat', 'remote', 'couch'],
                 }
             ]      ,	threshold=0.6_4      ,	)

         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[
                 [
                     {'score': 0.7_2_3_5, 'label': 'cat', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                     {'score': 0.7_2_1_8, 'label': 'remote', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                     {'score': 0.7_1_8_4, 'label': 'couch', 'box': {'xmin': 2_04, 'ymin': 1_67, 'xmax': 2_32, 'ymax': 1_90}},
                     {'score': 0.6_7_4_8, 'label': 'remote', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                     {'score': 0.6_6_5_6, 'label': 'cat', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                     {'score': 0.6_6_1_4, 'label': 'couch', 'box': {'xmin': 5_71, 'ymin': 83, 'xmax': 5_98, 'ymax': 1_03}},
                     {'score': 0.6_4_5_6, 'label': 'remote', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
                     {'score': 0.6_4_2, 'label': 'remote', 'box': {'xmin': 67, 'ymin': 2_74, 'xmax': 93, 'ymax': 2_97}},
                     {'score': 0.6_4_1_9, 'label': 'cat', 'box': {'xmin': 4_94, 'ymin': 1_05, 'xmax': 5_21, 'ymax': 1_27}},
                 ]
             ]      ,	)







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Any     )    ->				Dict:
         A						:     str         =pipeline('zero-shot-object-detection'     )

         A						:     int         =object_detector(
             'http://images.cocodataset.org/val2017/000000039769.jpg'      ,	candidate_labels=['cat', 'remote', 'couch']      ,	)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[
                 {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                 {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                 {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
                 {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
                 {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
             ]      ,	)

         A						:     Optional[Any]         =object_detector(
             [
                 {
                     'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                     'candidate_labels': ['cat', 'remote', 'couch'],
                 },
                 {
                     'image': 'http://images.cocodataset.org/val2017/000000039769.jpg',
                     'candidate_labels': ['cat', 'remote', 'couch'],
                 },
             ]      ,	)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[
                 [
                     {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                     {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                     {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
                     {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
                     {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
                 ],
                 [
                     {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                     {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                     {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
                     {'score': 0.1_4_7_4, 'label': 'remote', 'box': {'xmin': 3_35, 'ymin': 74, 'xmax': 3_71, 'ymax': 1_87}},
                     {'score': 0.1_2_0_8, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 6_42, 'ymax': 4_76}},
                 ],
             ]      ,	)







  @require_tf
  @unittest.skip('Zero Shot Object Detection not implemented in TF'     )
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Optional[int]     )    ->				int:
         pass







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					Union[str, Any]     )    ->				Optional[Any]:
         A						:     Dict         =0.2
         A						:     int         =pipeline('zero-shot-object-detection'     )

         A						:     str         =object_detector(
             'http://images.cocodataset.org/val2017/000000039769.jpg'      ,	candidate_labels=['cat', 'remote', 'couch']      ,	threshold=SCREAMING_SNAKE_CASE__      ,	)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[
                 {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                 {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
                 {'score': 0.2_5_3_7, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 3_15, 'ymax': 4_72}},
             ]      ,	)







  @require_torch
  @slow
  def 		SCREAMING_SNAKE_CASE_		(				self    :					int     )    ->				str:
         A						:     Any         =2
         A						:     str         =pipeline('zero-shot-object-detection'     )

         A						:     Dict         =object_detector(
             'http://images.cocodataset.org/val2017/000000039769.jpg'      ,	candidate_labels=['cat', 'remote', 'couch']      ,	top_k=SCREAMING_SNAKE_CASE__      ,	)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__      ,	decimals=4     )      ,	[
                 {'score': 0.2_8_6_8, 'label': 'cat', 'box': {'xmin': 3_24, 'ymin': 20, 'xmax': 6_40, 'ymax': 3_73}},
                 {'score': 0.2_7_7, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 72, 'xmax': 1_77, 'ymax': 1_15}},
             ]      ,	)







import math



def A__  (			lowercase:     int					)				->		list:
       A						:     Optional[Any]         =[True] * n
       A						:     Tuple         =False
       A						:     List[Any]         =False
       A						:     Dict         =True

       for i in range(3,		int(n**0.5 + 1					),		2					):
              A						:     Dict         =i * 2
              while index < n:
                     A						:     Dict         =False
                     A						:     Dict         =index + i

       A						:     Tuple         =[2]

       for i in range(3,		lowercase,		2					):
              if is_prime[i]:
                     primes.append(lowercase					)

       return primes



def A__  (			lowercase:     int = 999_966_663_333					)				->		int:
       A						:     Optional[int]         =math.floor(math.sqrt(lowercase					)					) + 100
       A						:     Optional[int]         =prime_sieve(lowercase					)

       A						:     Optional[Any]         =0
       A						:     List[Any]         =0
       A						:     Union[str, Any]         =primes[prime_index]

       while (last_prime**2) <= limit:
              A						:     Tuple         =primes[prime_index + 1]

              A						:     Optional[int]         =last_prime**2
              A						:     Tuple         =next_prime**2

              # Get numbers divisible by lps(current)
              A						:     int         =lower_bound + last_prime
              while upper_bound > current <= limit:
                     matches_sum += current
                     current += last_prime

              # Reset the upper_bound
              while (upper_bound - next_prime) > limit:
                     upper_bound -= next_prime

              # Add the numbers divisible by ups(current)
              A						:     List[Any]         =upper_bound - next_prime
              while current > lower_bound:
                     matches_sum += current
                     current -= next_prime

              # Remove the numbers divisible by both ups and lps
              A						:     Any         =0
              while upper_bound > current <= limit:
                     if current <= lower_bound:
                            # Increment the current number
                            current += last_prime * next_prime
                            continue

                     if current > limit:
                            break

                     # Remove twice since it was added by both ups and lps
                     matches_sum -= current * 2

                     # Increment the current number
                     current += last_prime * next_prime

              # Setup for next pair
              A						:     List[str]         =next_prime
              prime_index += 1

       return matches_sum


if __name__ == "__main__":
    print(solution())






