
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__		:







						'''simple docstring'''





						@staticmethod
						def   __snake_case				(			*UpperCamelCase__				,       **UpperCamelCase__		):
										pass







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







	'''simple docstring'''
	_lowerCAmelCase							       =				MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	Tuple         =							pipeline(
					    '''zero-shot-object-detection'''				,       model='''hf-internal-testing/tiny-random-owlvit-object-detection'''		)

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





	def   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					A__  :	List[Any]         =							object_detector(examples[0]				,       threshold=0.0		)

					A__  :	Union[str, Any]         =							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   __snake_case				(			self		):
					pass





	@require_torch
	def   __snake_case				(			self		):
					A__  :	List[Any]         =							pipeline(
					    '''zero-shot-object-detection'''				,       model='''hf-internal-testing/tiny-random-owlvit-object-detection'''		)

					A__  :	Any         =							object_detector(
					    '''./tests/fixtures/tests_samples/COCO/000000039769.png'''				,       candidate_labels=['''cat''', '''remote''', '''couch''']				,       threshold=0.6_4				,       )

					self.assertEqual(
					    nested_simplify(A_				,       decimals=4		)				,       [
					        {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
					        {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
					        {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
					        {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
					        {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
					        {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
					        {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
					        {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
					        {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
					    ]				,       )

					A__  :	Any         =							object_detector(
					    [
					        {
					            '''image''': '''./tests/fixtures/tests_samples/COCO/000000039769.png''',
					            '''candidate_labels''': ['''cat''', '''remote''', '''couch'''],
					        }
					    ]				,       threshold=0.6_4				,       )

					self.assertEqual(
					    nested_simplify(A_				,       decimals=4		)				,       [
					        [
					            {'''score''': 0.7_2_3_5, '''label''': '''cat''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
					            {'''score''': 0.7_2_1_8, '''label''': '''remote''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
					            {'''score''': 0.7_1_8_4, '''label''': '''couch''', '''box''': {'''xmin''': 204, '''ymin''': 167, '''xmax''': 232, '''ymax''': 190}},
					            {'''score''': 0.6_7_4_8, '''label''': '''remote''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
					            {'''score''': 0.6_6_5_6, '''label''': '''cat''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
					            {'''score''': 0.6_6_1_4, '''label''': '''couch''', '''box''': {'''xmin''': 571, '''ymin''': 83, '''xmax''': 598, '''ymax''': 103}},
					            {'''score''': 0.6_4_5_6, '''label''': '''remote''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
					            {'''score''': 0.6_4_2, '''label''': '''remote''', '''box''': {'''xmin''': 67, '''ymin''': 274, '''xmax''': 93, '''ymax''': 297}},
					            {'''score''': 0.6_4_1_9, '''label''': '''cat''', '''box''': {'''xmin''': 494, '''ymin''': 105, '''xmax''': 521, '''ymax''': 127}},
					        ]
					    ]				,       )





	@require_torch
	@slow
	def   __snake_case				(			self		):
					A__  :	Union[str, Any]         =							pipeline('''zero-shot-object-detection'''		)

					A__  :	Optional[int]         =							object_detector(
					    '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,       candidate_labels=['''cat''', '''remote''', '''couch''']				,       )
					self.assertEqual(
					    nested_simplify(A_				,       decimals=4		)				,       [
					        {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
					        {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
					        {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
					        {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
					        {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
					    ]				,       )

					A__  :	Optional[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(A_				,       decimals=4		)				,       [
					        [
					            {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
					            {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
					            {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
					            {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
					            {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
					        ],
					        [
					            {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
					            {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
					            {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
					            {'''score''': 0.1_4_7_4, '''label''': '''remote''', '''box''': {'''xmin''': 335, '''ymin''': 74, '''xmax''': 371, '''ymax''': 187}},
					            {'''score''': 0.1_2_0_8, '''label''': '''couch''', '''box''': {'''xmin''': 4, '''ymin''': 0, '''xmax''': 642, '''ymax''': 476}},
					        ],
					    ]				,       )





	@require_tf
	@unittest.skip('''Zero Shot Object Detection not implemented in TF'''		)
	def   __snake_case				(			self		):
					pass





	@require_torch
	@slow
	def   __snake_case				(			self		):
					A__  :	Dict         =							0.2
					A__  :	Any         =							pipeline('''zero-shot-object-detection'''		)

					A__  :	List[str]         =							object_detector(
					    '''http://images.cocodataset.org/val2017/000000039769.jpg'''				,       candidate_labels=['''cat''', '''remote''', '''couch''']				,       threshold=A_				,       )
					self.assertEqual(
					    nested_simplify(A_				,       decimals=4		)				,       [
					        {'''score''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
					        {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
					        {'''score''': 0.2_5_3_7, '''label''': '''cat''', '''box''': {'''xmin''': 1, '''ymin''': 55, '''xmax''': 315, '''ymax''': 472}},
					    ]				,       )





	@require_torch
	@slow
	def   __snake_case				(			self		):
					A__  :	Optional[Any]         =							2
					A__  :	List[str]         =							pipeline('''zero-shot-object-detection'''		)

					A__  :	Union[str, Any]         =							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''': 0.2_8_6_8, '''label''': '''cat''', '''box''': {'''xmin''': 324, '''ymin''': 20, '''xmax''': 640, '''ymax''': 373}},
					        {'''score''': 0.2_7_7, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 72, '''xmax''': 177, '''ymax''': 115}},
					    ]				,       )

from numpy import exp, pi, sqrt

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Dict							,						__UpperCamelCase :						float = 0.0							,						__UpperCamelCase :						float = 1.0       ) ->       int:

				"""simple docstring"""







				return 1 / sqrt(2 * pi * sigma**2       ) * exp(-((x - mu) ** 2) / (2 * sigma**2)       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()