
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   lowerCamelCase	:





        @staticmethod
        def 	UpperCAmelCase(*_A   :					Optional[int]       ,					**_A   :					Dict     )		->		List[Any]:
            pass







@is_pipeline_test
@require_vision
@require_torch
class   lowerCamelCase	(						unittest.TestCase						):
     UpperCAmelCase__       :							Union[str, Any]     =	MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





     def 	UpperCAmelCase(self   :					str       ,					_A   :					List[Any]       ,					_A   :					Dict       ,					_A   :					Optional[int]     )		->		Tuple:
         snake_case				  =      pipeline(
             "zero-shot-object-detection"       ,					model="hf-internal-testing/tiny-random-owlvit-object-detection"     )

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





     def 	UpperCAmelCase(self   :					List[str]       ,					_A   :					List[str]       ,					_A   :					Tuple     )		->		Any:
         snake_case				  =      object_detector(examples[0]       ,					threshold=0.0     )

         snake_case				  =      len(_A     )
         self.assertGreater(_A       ,					0     )
         self.assertEqual(
             _A       ,					[
                 {
                     "score": ANY(_A     ),
                     "label": ANY(_A     ),
                     "box": {"xmin": ANY(_A     ), "ymin": ANY(_A     ), "xmax": ANY(_A     ), "ymax": ANY(_A     )},
                 }
                 for i in range(_A     )
             ]       ,					)





     @require_tf
     @unittest.skip("Zero Shot Object Detection not implemented in TF"     )
     def 	UpperCAmelCase(self   :					Dict     )		->		str:
         pass





     @require_torch
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Tuple:
         snake_case				  =      pipeline(
             "zero-shot-object-detection"       ,					model="hf-internal-testing/tiny-random-owlvit-object-detection"     )

         snake_case				  =      object_detector(
             "./tests/fixtures/tests_samples/COCO/000000039769.png"       ,					candidate_labels=["cat", "remote", "couch"]       ,					threshold=0.64       ,					)

         self.assertEqual(
             nested_simplify(_A       ,					decimals=4     )       ,					[
                 {"score": 0.72_35, "label": "cat", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
                 {"score": 0.72_18, "label": "remote", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
                 {"score": 0.71_84, "label": "couch", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
                 {"score": 0.67_48, "label": "remote", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
                 {"score": 0.66_56, "label": "cat", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
                 {"score": 0.66_14, "label": "couch", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
                 {"score": 0.64_56, "label": "remote", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
                 {"score": 0.6_42, "label": "remote", "box": {"xmin": 6_7, "ymin": 2_7_4, "xmax": 9_3, "ymax": 2_9_7}},
                 {"score": 0.64_19, "label": "cat", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
             ]       ,					)

         snake_case				  =      object_detector(
             [
                 {
                     "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
                     "candidate_labels": ["cat", "remote", "couch"],
                 }
             ]       ,					threshold=0.64       ,					)

         self.assertEqual(
             nested_simplify(_A       ,					decimals=4     )       ,					[
                 [
                     {"score": 0.72_35, "label": "cat", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
                     {"score": 0.72_18, "label": "remote", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
                     {"score": 0.71_84, "label": "couch", "box": {"xmin": 2_0_4, "ymin": 1_6_7, "xmax": 2_3_2, "ymax": 1_9_0}},
                     {"score": 0.67_48, "label": "remote", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
                     {"score": 0.66_56, "label": "cat", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
                     {"score": 0.66_14, "label": "couch", "box": {"xmin": 5_7_1, "ymin": 8_3, "xmax": 5_9_8, "ymax": 1_0_3}},
                     {"score": 0.64_56, "label": "remote", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
                     {"score": 0.6_42, "label": "remote", "box": {"xmin": 6_7, "ymin": 2_7_4, "xmax": 9_3, "ymax": 2_9_7}},
                     {"score": 0.64_19, "label": "cat", "box": {"xmin": 4_9_4, "ymin": 1_0_5, "xmax": 5_2_1, "ymax": 1_2_7}},
                 ]
             ]       ,					)





     @require_torch
     @slow
     def 	UpperCAmelCase(self   :					List[Any]     )		->		Union[str, Any]:
         snake_case				  =      pipeline("zero-shot-object-detection"     )

         snake_case				  =      object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"       ,					candidate_labels=["cat", "remote", "couch"]       ,					)
         self.assertEqual(
             nested_simplify(_A       ,					decimals=4     )       ,					[
                 {"score": 0.28_68, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
                 {"score": 0.2_77, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
                 {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
                 {"score": 0.14_74, "label": "remote", "box": {"xmin": 3_3_5, "ymin": 7_4, "xmax": 3_7_1, "ymax": 1_8_7}},
                 {"score": 0.12_08, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_4_2, "ymax": 4_7_6}},
             ]       ,					)

         snake_case				  =      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(_A       ,					decimals=4     )       ,					[
                 [
                     {"score": 0.28_68, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
                     {"score": 0.2_77, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
                     {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
                     {"score": 0.14_74, "label": "remote", "box": {"xmin": 3_3_5, "ymin": 7_4, "xmax": 3_7_1, "ymax": 1_8_7}},
                     {"score": 0.12_08, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_4_2, "ymax": 4_7_6}},
                 ],
                 [
                     {"score": 0.28_68, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
                     {"score": 0.2_77, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
                     {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
                     {"score": 0.14_74, "label": "remote", "box": {"xmin": 3_3_5, "ymin": 7_4, "xmax": 3_7_1, "ymax": 1_8_7}},
                     {"score": 0.12_08, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 6_4_2, "ymax": 4_7_6}},
                 ],
             ]       ,					)





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





     @require_torch
     @slow
     def 	UpperCAmelCase(self   :					Union[str, Any]     )		->		Dict:
         snake_case				  =      0.2
         snake_case				  =      pipeline("zero-shot-object-detection"     )

         snake_case				  =      object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"       ,					candidate_labels=["cat", "remote", "couch"]       ,					threshold=_A       ,					)
         self.assertEqual(
             nested_simplify(_A       ,					decimals=4     )       ,					[
                 {"score": 0.28_68, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
                 {"score": 0.2_77, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
                 {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 5_5, "xmax": 3_1_5, "ymax": 4_7_2}},
             ]       ,					)





     @require_torch
     @slow
     def 	UpperCAmelCase(self   :					List[str]     )		->		List[Any]:
         snake_case				  =      2
         snake_case				  =      pipeline("zero-shot-object-detection"     )

         snake_case				  =      object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"       ,					candidate_labels=["cat", "remote", "couch"]       ,					top_k=_A       ,					)
         self.assertEqual(
             nested_simplify(_A       ,					decimals=4     )       ,					[
                 {"score": 0.28_68, "label": "cat", "box": {"xmin": 3_2_4, "ymin": 2_0, "xmax": 6_4_0, "ymax": 3_7_3}},
                 {"score": 0.2_77, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_2, "xmax": 1_7_7, "ymax": 1_1_5}},
             ]       ,					)



_A						      =				0  # The first color of the flag.
_A						      =				1  # The second color of the flag.
_A						      =				2  # The third color of the flag.
_A						      =				(red, white, blue)



def 		lowercase_    (     A__	)							->							list:
    """simple docstring"""

    if not sequence:
        return []
    if len(A__	) == 1:
        return list(A__	)
    snake_case				  =      0
    snake_case				  =      len(A__	) - 1
    snake_case				  =      0
    while mid <= high:
        if sequence[mid] == colors[0]:
            snake_case  ,							snake_case				  =      sequence[mid], sequence[low]
            low += 1
            mid += 1
        elif sequence[mid] == colors[1]:
            mid += 1
        elif sequence[mid] == colors[2]:
            snake_case  ,							snake_case				  =      sequence[high], sequence[mid]
            high -= 1
        else:
            snake_case				  =      F'The elements inside the sequence must contains only {colors} values'
            raise ValueError(A__	)
    return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   _A						      =				input("Enter numbers separated by commas:\n").strip()
   _A						      =				[int(item.strip()) for item in user_input.split(",")]
   print(f"{dutch_national_flag_sort(unsorted)}")

