






"""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      UpperCamelCase_			:





				@staticmethod
				def 	_SCREAMING_SNAKE_CASE ( *lowerCAmelCase_   :					Optional[int]      ,      **lowerCAmelCase_   :					List[Any]		)		->     List[str]:
							pass







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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Dict      ,      lowerCAmelCase_   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int]      ,      lowerCAmelCase_   :					Optional[int]		)		->     List[Any]:
				UpperCAmelCase_	:					Optional[int]     	=   pipeline(
				    "zero-shot-object-detection"      ,      model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					int      ,      lowerCAmelCase_   :					str		)		->     List[Any]:
				UpperCAmelCase_	:					Tuple     	=   object_detector(examples[0]      ,      threshold=0.0		)

				UpperCAmelCase_	:					Optional[int]     	=   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 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Union[str, Any]:
				pass





	@require_torch
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Dict:
				UpperCAmelCase_	:					int     	=   pipeline(
				    "zero-shot-object-detection"      ,      model="hf-internal-testing/tiny-random-owlvit-object-detection"		)

				UpperCAmelCase_	:					int     	=   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}},
				    ]      ,      )

				UpperCAmelCase_	:					Union[str, 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 	_SCREAMING_SNAKE_CASE ( self   :					Tuple		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[Any]     	=   pipeline("zero-shot-object-detection"		)

				UpperCAmelCase_	:					Tuple     	=   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}},
				    ]      ,      )

				UpperCAmelCase_	:					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 	_SCREAMING_SNAKE_CASE ( self   :					Any		)		->     Union[str, Any]:
				pass





	@require_torch
	@slow
	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[int]		)		->     Dict:
				UpperCAmelCase_	:					Union[str, Any]     	=   0.2
				UpperCAmelCase_	:					Optional[int]     	=   pipeline("zero-shot-object-detection"		)

				UpperCAmelCase_	:					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 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]		)		->     Optional[Any]:
				UpperCAmelCase_	:					List[str]     	=   2
				UpperCAmelCase_	:					List[Any]     	=   pipeline("zero-shot-object-detection"		)

				UpperCAmelCase_	:					List[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}},
				    ]      ,      )








'''simple docstring'''





import random
from typing import Any

def 			a_	(				_lowerCAmelCase			) -> list[Any]:
  for _ in range(len(_lowerCAmelCase			)			):
    __lowerCamelCase			:			Optional[Any]       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			:			str       			=					random.randint(0			,len(_lowerCAmelCase			) - 1			)
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					data[b], data[a]
  return data


if __name__ == "__main__":
   _UpperCamelCase											=		[0, 1, 2, 3, 4, 5, 6, 7]
   _UpperCamelCase											=		['python', 'says', 'hello', '!']
   print('Fisher-Yates Shuffle:')
   print('List', integers, strings)
   print('FY Shuffle', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))






