

def 			lowerCamelCase__			(       _a				,      _a):
	return 1 if input_a == input_a else 0

def 			lowerCamelCase__			(       ):
	assert xnor_gate(0				,      0) == 1
	assert xnor_gate(0				,      1) == 0
	assert xnor_gate(1				,      0) == 0
	assert xnor_gate(1				,      1) == 1


if __name__ == "__main__":
			print(xnor_gate(0, 0))
			print(xnor_gate(0, 1))
			print(xnor_gate(1, 0))
			print(xnor_gate(1, 1))


'''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    snake_case     :






											"""simple docstring"""



											@staticmethod
											def   a__					(    *_lowercase,     **_lowercase       )     ->							Tuple:
																		pass






@is_pipeline_test
@require_vision
@require_torch
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

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



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				object_detector(examples[0],     threshold=0.0       )

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



				@require_tf
				@unittest.skip('Zero Shot Object Detection not implemented in TF'       )
				def   a__					(    self       )     ->							Union[str, Any]:
											pass



				@require_torch
				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				=				pipeline(
											    'zero-shot-object-detection',     model='hf-internal-testing/tiny-random-owlvit-object-detection'       )

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

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											        {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											        {'score': 0.6_456, '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': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											    ],     )

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

											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.7_235, 'label': 'cat', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_218, 'label': 'remote', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.7_184, 'label': 'couch', 'box': {'xmin': 204, 'ymin': 167, 'xmax': 232, 'ymax': 190}},
											            {'score': 0.6_748, 'label': 'remote', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_656, 'label': 'cat', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_614, 'label': 'couch', 'box': {'xmin': 571, 'ymin': 83, 'xmax': 598, 'ymax': 103}},
											            {'score': 0.6_456, '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': 0.6_419, 'label': 'cat', 'box': {'xmin': 494, 'ymin': 105, 'xmax': 521, 'ymax': 127}},
											        ]
											    ],     )



				@require_torch
				@slow
				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											        {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											        {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											    ],     )

											SCREAMING_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(_lowercase,     decimals=4       ),     [
											        [
											            {'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											        [
											            {'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											            {'score': 0.1_474, 'label': 'remote', 'box': {'xmin': 335, 'ymin': 74, 'xmax': 371, 'ymax': 187}},
											            {'score': 0.1_208, 'label': 'couch', 'box': {'xmin': 4, 'ymin': 0, 'xmax': 642, 'ymax': 476}},
											        ],
											    ],     )



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



				@require_torch
				@slow
				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				0.2
											SCREAMING_SNAKE_CASE_				=				pipeline('zero-shot-object-detection'       )

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     threshold=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, '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': 0.2_537, 'label': 'cat', 'box': {'xmin': 1, 'ymin': 55, 'xmax': 315, 'ymax': 472}},
											    ],     )





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

											SCREAMING_SNAKE_CASE_				=				object_detector(
											    'http://images.cocodataset.org/val2017/000000039769.jpg',     candidate_labels=['cat', 'remote', 'couch'],     top_k=_lowercase,     )
											self.assertEqual(
											    nested_simplify(_lowercase,     decimals=4       ),     [
											        {'score': 0.2_868, '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}},
											    ],     )





