


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   :






						"""simple docstring"""





						@staticmethod
						def 		snake_case__			(				*lowercase	:       Optional[Any]			,     **lowercase	:       Optional[Any]   )						->					List[Any]:
												"""simple docstring"""







												pass
@is_pipeline_test
@require_vision
@require_torch
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			lowercase__      :  List[str]      =			MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING





			def 		snake_case__			(				self	:       Tuple			,     lowercase	:       List[str]			,     lowercase	:       Optional[Any]			,     lowercase	:       Any   )						->					Tuple:
									"""simple docstring"""







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

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





			def 		snake_case__			(				self	:       Dict			,     lowercase	:       Any			,     lowercase	:       Any   )						->					List[Any]:
									"""simple docstring"""







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

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





			@require_tf
			@unittest.skip("""Zero Shot Object Detection not implemented in TF"""   )
			def 		snake_case__			(				self	:       str   )						->					Any:
									"""simple docstring"""







									pass





			@require_torch
			def 		snake_case__			(				self	:       Union[str, Any]   )						->					int:
									"""simple docstring"""







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

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

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

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

									self.assertEqual(
									    nested_simplify(_lowerCAmelCase			,     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 		snake_case__			(				self	:       Any   )						->					Any:
									"""simple docstring"""







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

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

									__lowercase					=				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(_lowerCAmelCase			,     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 		snake_case__			(				self	:       Any   )						->					Any:
									"""simple docstring"""







									pass





			@require_torch
			@slow
			def 		snake_case__			(				self	:       Optional[Any]   )						->					Optional[Any]:
									"""simple docstring"""







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

									__lowercase					=				object_detector(
									    """http://images.cocodataset.org/val2017/000000039769.jpg"""			,     candidate_labels=["""cat""", """remote""", """couch"""]			,     threshold=_lowerCAmelCase			,     )
									self.assertEqual(
									    nested_simplify(_lowerCAmelCase			,     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 		snake_case__			(				self	:       str   )						->					Tuple:
									"""simple docstring"""







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

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










import unittest

import numpy as np






def  UpperCAmelCase__    (    lowercase__						,       lowercase__						,       lowercase__						,       lowercase__ = None						,       ) ->      np.ndarray:
						__lowercase					=				np.shape(lowercase__   )
						__lowercase					=				np.shape(lowercase__   )
						__lowercase					=				np.shape(lowercase__   )

						if shape_a[0] != shape_b[0]:
												__lowercase					=				(
												    """Expected the same number of rows for A and B. """
												    F"Instead found A of size {shape_a} and B of size {shape_b}"
												)
												raise ValueError(lowercase__   )

						if shape_b[1] != shape_c[1]:
												__lowercase					=				(
												    """Expected the same number of columns for B and C. """
												    F"Instead found B of size {shape_b} and C of size {shape_c}"
												)
												raise ValueError(lowercase__   )

						__lowercase					=				pseudo_inv
						if a_inv is None:
												try:
																		__lowercase					=				np.linalg.inv(lowercase__   )
												except np.linalg.LinAlgError:
																		raise ValueError(
																		    """Input matrix A is not invertible. Cannot compute Schur complement."""   )

						return mat_c - mat_b.T @ a_inv @ mat_b
class 			_lowerCAmelCase   ( unittest.TestCase			):






			"""simple docstring"""





			def 		snake_case__			(				self	:       Dict   )						->					None:
									"""simple docstring"""







									__lowercase					=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
									__lowercase					=				np.array([[0, 3], [3, 0], [2, 3]]   )
									__lowercase					=				np.array([[2, 1], [6, 3]]   )

									__lowercase					=				schur_complement(lowercase			,     lowercase			,     lowercase   )

									__lowercase					=				np.block([[a, b], [b.T, c]]   )

									__lowercase					=				np.linalg.det(lowercase   )
									__lowercase					=				np.linalg.det(lowercase   )
									__lowercase					=				np.linalg.det(lowercase   )

									self.assertAlmostEqual(lowercase			,     det_a * det_s   )





			def 		snake_case__			(				self	:       Tuple   )						->					None:
									"""simple docstring"""







									__lowercase					=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
									__lowercase					=				np.array([[0, 3], [3, 0], [2, 3]]   )
									__lowercase					=				np.array([[2, 1], [6, 3]]   )

									with self.assertRaises(lowercase   ):
															schur_complement(lowercase			,     lowercase			,     lowercase   )







			def 		snake_case__			(				self	:       Tuple   )						->					None:
									"""simple docstring"""







									__lowercase					=				np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]   )
									__lowercase					=				np.array([[0, 3], [3, 0], [2, 3]]   )
									__lowercase					=				np.array([[2, 1, 3], [6, 3, 5]]   )

									with self.assertRaises(lowercase   ):
															schur_complement(lowercase			,     lowercase			,     lowercase   )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			unittest.main()





