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 					lowercase__						:


											@staticmethod
											def        __A       (       *UpperCamelCase__	:				Union[str, Any]    ,		**UpperCamelCase__	:				Optional[Any]						):





																	'''simple docstring'''





																	pass





@is_pipeline_test
@require_vision
@require_torch
class 					lowercase__						(   unittest.TestCase):
						UpperCamelCase_     =		MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Tuple    ,		UpperCamelCase__	:				List[Any]    ,		UpperCamelCase__	:				int						):





												'''simple docstring'''





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

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


						def        __A       (       self	:				Union[str, Any]    ,		UpperCamelCase__	:				Any    ,		UpperCamelCase__	:				Optional[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Optional[int]							=							object_detector(examples[0]    ,		threshold=0.0						)

												SCREAMING_SNAKE_CASE							:    Tuple							=							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        __A       (       self	:				Optional[Any]						):





												'''simple docstring'''





												pass


						@require_torch
						def        __A       (       self	:				Dict						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							pipeline(
												    '''zero-shot-object-detection'''    ,		model='''hf-internal-testing/tiny-random-owlvit-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    str							=							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}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    str							=							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        __A       (       self	:				Tuple						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    int							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							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}},
												    ]    ,		)

												SCREAMING_SNAKE_CASE							:    int							=							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        __A       (       self	:				str						):





												'''simple docstring'''





												pass


						@require_torch
						@slow
						def        __A       (       self	:				List[str]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    Tuple							=							0.2
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    Dict							=							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        __A       (       self	:				List[Any]						):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:    List[str]							=							2
												SCREAMING_SNAKE_CASE							:    Optional[Any]							=							pipeline('''zero-shot-object-detection'''						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							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}},
												    ]    ,		)






def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





