


"""simple docstring"""



from collections.abc import Iterable
from typing import Generic, TypeVar

__snake_case             =   TypeVar("""_T""")







class _lowerCAmelCase    (				Generic[_T]     ):







   def __init__(     self						,     UpperCamelCase__ = None						)     ->		None:

       '''simple docstring'''




       snake_case   :				list[_T]				 =   list(iterable or []						)
       snake_case   :				list[_T]				 =   []







   def __len__(     self						)     ->		int:

       '''simple docstring'''




       return len(self._stacka						) + len(self._stacka						)







   def __repr__(     self						)     ->		str:

       '''simple docstring'''




       return F'Queue({tuple(self._stacka[::-1] + self._stacka						)})'







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		None:

       '''simple docstring'''




       self._stacka.append(UpperCamelCase__						)







   def 			lowerCamelCase		(     self						)     ->		_T:

       '''simple docstring'''




       snake_case   :				Tuple				 =   self._stacka.pop
       snake_case   :				Dict				 =   self._stacka.append

       if not self._stacka:
           while self._stacka:
               stacka_append(stacka_pop()						)

       if not self._stacka:
           raise IndexError("Queue is empty"						)
       return self._stacka.pop()


if __name__ == "__main__":
  from doctest import testmod

  testmod()









"""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 _lowerCAmelCase    :







     @staticmethod
     def 			lowerCamelCase		(     *UpperCamelCase__						,     **UpperCamelCase__						)     ->		Union[str, Any]:

         '''simple docstring'''




         pass







@is_pipeline_test
@require_vision
@require_torch
class _lowerCAmelCase    (				unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Optional[Any]:

       '''simple docstring'''




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

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







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						,     UpperCamelCase__						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				str				 =   object_detector(examples[0]						,     threshold=0.0						)

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







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

       '''simple docstring'''




       pass







   @require_torch
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




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

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

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     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}},
           ]						,     )

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

       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     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 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Tuple				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     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}},
           ]						,     )

       snake_case   :				List[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(UpperCamelCase__						,     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 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       pass







   @require_torch
   @slow
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				Optional[Any]				 =   0.2
       snake_case   :				List[str]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				List[Any]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     threshold=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     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 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   2
       snake_case   :				Optional[Any]				 =   pipeline("zero-shot-object-detection"						)

       snake_case   :				Optional[int]				 =   object_detector(
           "http://images.cocodataset.org/val2017/000000039769.jpg"						,     candidate_labels=["cat", "remote", "couch"]						,     top_k=UpperCamelCase__						,     )
       self.assertEqual(
           nested_simplify(UpperCamelCase__						,     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}},
           ]						,     )





