


"""simple docstring"""







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        lowercase__							(						*snake_case__		,     **snake_case__						):






              """simple docstring"""





              pass


@is_pipeline_test
@require_vision
@require_torch
class       SCREAMING_SNAKE_CASE__				(		unittest.TestCase				):
       """simple docstring"""


       a  :				Any   =MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

       def        lowercase__							(						self		,     snake_case__		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[int]								=							pipeline(
             "zero-shot-object-detection"		,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

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

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							object_detector(examples[0]		,     threshold=0.0						)

         lowerCAmelCase     :     Dict								=							len(snake_case__						)
         self.assertGreater(snake_case__		,     0						)
         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__						)},
                 }
                 for i in range(snake_case__						)
             ]		,     )

       @require_tf
       @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       @require_torch
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Any								=							pipeline(
             "zero-shot-object-detection"		,     model="hf-internal-testing/tiny-random-owlvit-object-detection"						)

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

         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                 {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                 {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                 {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
             ]		,     )

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

         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 [
                     {"score": 0.7235, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7218, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.7184, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                     {"score": 0.6748, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6656, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6614, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                     {"score": 0.6456, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                     {"score": 0.642, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                     {"score": 0.6419, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                 ]
             ]		,     )

       @require_torch
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							pipeline("zero-shot-object-detection"						)

         lowerCAmelCase     :     Any								=							object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,     candidate_labels=["cat", "remote", "couch"]		,     )
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                 {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                 {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
             ]		,     )

         lowerCAmelCase     :     Tuple								=							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(snake_case__		,     decimals=4						)		,     [
                 [
                     {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
                 [
                     {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                     {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                     {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                     {"score": 0.1474, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                     {"score": 0.1208, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                 ],
             ]		,     )

       @require_tf
       @unittest.skip("Zero Shot Object Detection not implemented in TF"						)
       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       @require_torch
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							0.2
         lowerCAmelCase     :     Dict								=							pipeline("zero-shot-object-detection"						)

         lowerCAmelCase     :     Any								=							object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,     candidate_labels=["cat", "remote", "couch"]		,     threshold=snake_case__		,     )
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                 {"score": 0.2537, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
             ]		,     )







       @require_torch
       @slow
       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							2
         lowerCAmelCase     :     Any								=							pipeline("zero-shot-object-detection"						)

         lowerCAmelCase     :     Optional[int]								=							object_detector(
             "http://images.cocodataset.org/val2017/000000039769.jpg"		,     candidate_labels=["cat", "remote", "couch"]		,     top_k=snake_case__		,     )
         self.assertEqual(
             nested_simplify(snake_case__		,     decimals=4						)		,     [
                 {"score": 0.2868, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                 {"score": 0.277, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
             ]		,     )












def 	UpperCamelCase__(       UpperCamelCase__			:       Dict  )->Dict:
       # if the collection is empty, returns empty
       if collection == []:
              return []

       # get some information about the collection
       A__											=						len(UpperCamelCase__  )
       A__											=						max(UpperCamelCase__  )
       A__											=						min(UpperCamelCase__  )

       # create the counting array
       A__											=						coll_max + 1 - coll_min
       A__											=						[0] * counting_arr_length

       # count how much a number appears in the collection
       for number in collection:
              counting_arr[number - coll_min] += 1

       # sum each position with it's predecessors. now, counting_arr[i] tells
       # us how many elements <= i has in the collection
       for i in range(1 ,     UpperCamelCase__  ):
              A__											=						counting_arr[i] + counting_arr[i - 1]

       # create the output collection
       A__											=						[0] * coll_len

       # place the elements in the output, respecting the original order (stable
       # sort) from end to begin, updating counting_arr
       for i in reversed(range(0 ,     UpperCamelCase__  )  ):
              A__											=						collection[i]
              counting_arr[collection[i] - coll_min] -= 1

       return ordered


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int]  )->Tuple:
       return "".join([chr(UpperCamelCase__  ) for i in counting_sort([ord(UpperCamelCase__  ) for c in string]  )]  )


if __name__ == "__main__":
       # Test string sort
       assert counting_sort_string('thisisthestring') == "eghhiiinrsssttt"

       a__:      Dict									=		input('Enter numbers separated by a comma:\n').strip()
       a__:      Any									=		[int(item) for item in user_input.split(',')]
       print(counting_sort(unsorted))





