

import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    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							(      *snake_case   ,     **snake_case ):
															pass







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				lowercase       ( unittest.TestCase    ):
				__SCREAMING_SNAKE_CASE      :				Union[str, Any]     =    MODEL_FOR_OBJECT_DETECTION_MAPPING




				def 					a							(      self   ,     snake_case   ,     snake_case   ,     snake_case ):
											snake_case_           =					ObjectDetectionPipeline(model=snake_case   ,     image_processor=snake_case )
											return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




				def 					a							(      self   ,     snake_case   ,     snake_case ):
											snake_case_           =					object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'   ,     threshold=0.0 )

											self.assertGreater(len(snake_case )   ,     0 )
											for detected_object in outputs:
																		self.assertEqual(
																		    snake_case   ,     {
																		        'score': ANY(snake_case ),
																		        'label': ANY(snake_case ),
																		        'box': {'xmin': ANY(snake_case ), 'ymin': ANY(snake_case ), 'xmax': ANY(snake_case ), 'ymax': ANY(snake_case )},
																		    }   ,     )

											import datasets

											snake_case_           =					datasets.load_dataset('hf-internal-testing/fixtures_image_utils'   ,     'image'   ,     split='test' )

											snake_case_           =					[
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' ),
											    'http://images.cocodataset.org/val2017/000000039769.jpg',
											    # RGBA
											    dataset[0]['file'],
											    # LA
											    dataset[1]['file'],
											    # L
											    dataset[2]['file'],
											]
											snake_case_           =					object_detector(snake_case   ,     threshold=0.0 )

											self.assertEqual(len(snake_case )   ,     len(snake_case ) )
											for outputs in batch_outputs:
																		self.assertGreater(len(snake_case )   ,     0 )
																		for detected_object in outputs:
																									self.assertEqual(
																									    snake_case   ,     {
																									        'score': ANY(snake_case ),
																									        'label': ANY(snake_case ),
																									        'box': {'xmin': ANY(snake_case ), 'ymin': ANY(snake_case ), 'xmax': ANY(snake_case ), 'ymax': ANY(snake_case )},
																									    }   ,     )




				@require_tf
				@unittest.skip('Object detection not implemented in TF' )
				def 					a							(      self ):
											pass




				@require_torch
				def 					a							(      self ):
											snake_case_           =					'hf-internal-testing/tiny-detr-mobilenetsv3'

											snake_case_           =					AutoModelForObjectDetection.from_pretrained(snake_case )
											snake_case_           =					AutoFeatureExtractor.from_pretrained(snake_case )
											snake_case_           =					ObjectDetectionPipeline(model=snake_case   ,     feature_extractor=snake_case )

											snake_case_           =					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'   ,     threshold=0.0 )

											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											        {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											    ]   ,     )

											snake_case_           =					object_detector(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]   ,     threshold=0.0   ,     )

											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        [
											            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											        ],
											        [
											            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											            {'score': 0.33_76, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
											        ],
											    ]   ,     )




				@require_torch
				@slow
				def 					a							(      self ):
											snake_case_           =					'facebook/detr-resnet-50'

											snake_case_           =					AutoModelForObjectDetection.from_pretrained(snake_case )
											snake_case_           =					AutoFeatureExtractor.from_pretrained(snake_case )
											snake_case_           =					ObjectDetectionPipeline(model=snake_case   ,     feature_extractor=snake_case )

											snake_case_           =					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											        {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											        {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											        {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											        {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											    ]   ,     )

											snake_case_           =					object_detector(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ] )
											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        [
											            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											        [
											            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											    ]   ,     )




				@require_torch
				@slow
				def 					a							(      self ):
											snake_case_           =					'facebook/detr-resnet-50'

											snake_case_           =					pipeline('object-detection'   ,     model=snake_case )

											snake_case_           =					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' )
											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											        {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											        {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											        {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											        {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											    ]   ,     )

											snake_case_           =					object_detector(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ] )
											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        [
											            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											        [
											            {'score': 0.99_82, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
											            {'score': 0.99_60, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
											            {'score': 0.99_55, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
											            {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											            {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											        ],
											    ]   ,     )




				@require_torch
				@slow
				def 					a							(      self ):
											snake_case_           =					0.99_85
											snake_case_           =					'facebook/detr-resnet-50'

											snake_case_           =					pipeline('object-detection'   ,     model=snake_case )

											snake_case_           =					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'   ,     threshold=snake_case )
											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        {'score': 0.99_88, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
											        {'score': 0.99_87, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
											    ]   ,     )




				@require_torch
				@require_pytesseract
				@slow
				def 					a							(      self ):
											snake_case_           =					'Narsil/layoutlmv3-finetuned-funsd'
											snake_case_           =					0.99_93

											snake_case_           =					pipeline('object-detection'   ,     model=snake_case   ,     threshold=snake_case )

											snake_case_           =					object_detector(
											    'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png' )
											self.assertEqual(
											    nested_simplify(snake_case   ,     decimals=4 )   ,     [
											        {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
											        {'score': 0.99_93, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
											    ]   ,     )







def       __lowerCamelCase			(				UpperCamelCase__     ,							UpperCamelCase__   ):







							'''simple docstring'''






							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					len(UpperCamelCase__   )
							snake_case_           =					(
							    first_str_length if first_str_length > second_str_length else second_str_length
							)
							snake_case_           =					[]
							for char_count in range(UpperCamelCase__   ):
														if char_count < first_str_length:
																					output_list.append(first_str[char_count]   )
														if char_count < second_str_length:
																					output_list.append(second_str[char_count]   )
							return "".join(UpperCamelCase__   )


if __name__ == "__main__":
					print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)




