





from collections.abc import Callable

import numpy as np




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )     ->		np.array:

       '''simple docstring'''





       UpperCamelCase_          =  int(np.ceil((x_end - xa) / step_size  )  )
       UpperCamelCase_          =  np.zeros((n + 1,)  )
       UpperCamelCase_          =  ya
       UpperCamelCase_          =  xa

       for k in range(_A  ):
              UpperCamelCase_          =  y[k] + step_size * ode_func(_A					,		y[k]  )
              UpperCamelCase_          =  y[k] + (
                  (step_size / 2) * (ode_func(_A					,		y[k]  ) + ode_func(x + step_size					,		_A  ))
              )
              x += step_size

       return y


if __name__ == "__main__":
     import doctest

     doctest.testmod()








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 							_UpperCamelCase					:

            @staticmethod
            def lowercase (		*_SCREAMING_SNAKE_CASE: Optional[int]					,   **_SCREAMING_SNAKE_CASE: Dict		)       -> Union[str, Any]:




                   """simple docstring"""


                   pass



@is_pipeline_test
@require_vision
@require_torch
class 							_UpperCamelCase					(	unittest.TestCase     ):
       _UpperCamelCase				:					str         =   MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

       def lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Optional[Any]		)       -> Any:




              """simple docstring"""


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

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

       def lowercase (		self: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Tuple		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  object_detector(examples[0]					,   threshold=0.0		)

              UpperCamelCase_          =  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 lowercase (		self: Tuple		)       -> List[str]:




              """simple docstring"""


              pass

       @require_torch
       def lowercase (		self: Union[str, Any]		)       -> str:




              """simple docstring"""


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

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

              self.assertEqual(
                  nested_simplify(_SCREAMING_SNAKE_CASE					,   decimals=4		)					,   [
                      {"score": 0.72_35, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                      {"score": 0.72_18, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                      {"score": 0.71_84, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                      {"score": 0.67_48, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                      {"score": 0.66_56, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                      {"score": 0.66_14, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                      {"score": 0.64_56, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                      {"score": 0.6_42, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                      {"score": 0.64_19, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                  ]					,   )

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

              self.assertEqual(
                  nested_simplify(_SCREAMING_SNAKE_CASE					,   decimals=4		)					,   [
                      [
                          {"score": 0.72_35, "label": "cat", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                          {"score": 0.72_18, "label": "remote", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                          {"score": 0.71_84, "label": "couch", "box": {"xmin": 204, "ymin": 167, "xmax": 232, "ymax": 190}},
                          {"score": 0.67_48, "label": "remote", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                          {"score": 0.66_56, "label": "cat", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                          {"score": 0.66_14, "label": "couch", "box": {"xmin": 571, "ymin": 83, "xmax": 598, "ymax": 103}},
                          {"score": 0.64_56, "label": "remote", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                          {"score": 0.6_42, "label": "remote", "box": {"xmin": 67, "ymin": 274, "xmax": 93, "ymax": 297}},
                          {"score": 0.64_19, "label": "cat", "box": {"xmin": 494, "ymin": 105, "xmax": 521, "ymax": 127}},
                      ]
                  ]					,   )

       @require_torch
       @slow
       def lowercase (		self: List[str]		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  pipeline("zero-shot-object-detection"		)

              UpperCamelCase_          =  object_detector(
                  "http://images.cocodataset.org/val2017/000000039769.jpg"					,   candidate_labels=["cat", "remote", "couch"]					,   )
              self.assertEqual(
                  nested_simplify(_SCREAMING_SNAKE_CASE					,   decimals=4		)					,   [
                      {"score": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                      {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                      {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                      {"score": 0.14_74, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                      {"score": 0.12_08, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                  ]					,   )

              UpperCamelCase_          =  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": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                          {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                          {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                          {"score": 0.14_74, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                          {"score": 0.12_08, "label": "couch", "box": {"xmin": 4, "ymin": 0, "xmax": 642, "ymax": 476}},
                      ],
                      [
                          {"score": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                          {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                          {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                          {"score": 0.14_74, "label": "remote", "box": {"xmin": 335, "ymin": 74, "xmax": 371, "ymax": 187}},
                          {"score": 0.12_08, "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: List[Any]		)       -> str:




              """simple docstring"""


              pass

       @require_torch
       @slow
       def lowercase (		self: Any		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  0.2
              UpperCamelCase_          =  pipeline("zero-shot-object-detection"		)

              UpperCamelCase_          =  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": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                      {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                      {"score": 0.25_37, "label": "cat", "box": {"xmin": 1, "ymin": 55, "xmax": 315, "ymax": 472}},
                  ]					,   )



       @require_torch
       @slow
       def lowercase (		self: Dict		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  2
              UpperCamelCase_          =  pipeline("zero-shot-object-detection"		)

              UpperCamelCase_          =  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": 0.28_68, "label": "cat", "box": {"xmin": 324, "ymin": 20, "xmax": 640, "ymax": 373}},
                      {"score": 0.2_77, "label": "remote", "box": {"xmin": 40, "ymin": 72, "xmax": 177, "ymax": 115}},
                  ]					,   )

