





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				:



								"""simple docstring"""

								@staticmethod
								def 		__UpperCAmelCase       (    *lowerCamelCase_			:		Any ,				**lowerCamelCase_			:		List[Any]					):





												'''simple docstring'''







												pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			lowercase				(							unittest.TestCase     ):



						"""simple docstring"""

						_UpperCamelCase  :	Tuple								=	MODEL_FOR_OBJECT_DETECTION_MAPPING







						def 		__UpperCAmelCase       (    self			:		Optional[int] ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		Any ,				lowerCamelCase_			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   ObjectDetectionPipeline(model=lowerCamelCase_ ,				image_processor=lowerCamelCase_					)
										return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







						def 		__UpperCAmelCase       (    self			:		Union[str, Any] ,				lowerCamelCase_			:		Optional[Any] ,				lowerCamelCase_			:		List[Any]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png' ,				threshold=0.0					)

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

										import datasets

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

										_snake_case :			str  						=   [
										    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 :			str  						=   object_detector(lowerCamelCase_ ,				threshold=0.0					)

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







						@require_tf
						@unittest.skip('Object detection not implemented in TF'					)
						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										pass







						@require_torch
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Tuple  						=   'hf-internal-testing/tiny-detr-mobilenetsv3'

										_snake_case :			Any  						=   AutoModelForObjectDetection.from_pretrained(lowerCamelCase_					)
										_snake_case :			List[str]  						=   AutoFeatureExtractor.from_pretrained(lowerCamelCase_					)
										_snake_case :			List[str]  						=   ObjectDetectionPipeline(model=lowerCamelCase_ ,				feature_extractor=lowerCamelCase_					)

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

										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
										        {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
										    ] ,				)

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

										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        [
										            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
										            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
										        ],
										        [
										            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
										            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
										        ],
										    ] ,				)







						@require_torch
						@slow
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







										_snake_case :			Optional[int]  						=   'facebook/detr-resnet-50'

										_snake_case :			Tuple  						=   AutoModelForObjectDetection.from_pretrained(lowerCamelCase_					)
										_snake_case :			Optional[int]  						=   AutoFeatureExtractor.from_pretrained(lowerCamelCase_					)
										_snake_case :			Tuple  						=   ObjectDetectionPipeline(model=lowerCamelCase_ ,				feature_extractor=lowerCamelCase_					)

										_snake_case :			Optional[Any]  						=   object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
										        {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
										        {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
										        {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
										        {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
										    ] ,				)

										_snake_case :			Tuple  						=   object_detector(
										    [
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										    ]					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        [
										            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
										            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
										            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
										            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
										            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
										        ],
										        [
										            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
										            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
										            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
										            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
										            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
										        ],
										    ] ,				)







						@require_torch
						@slow
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			str  						=   'facebook/detr-resnet-50'

										_snake_case :			Any  						=   pipeline('object-detection' ,				model=lowerCamelCase_					)

										_snake_case :			Dict  						=   object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
										        {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
										        {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
										        {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
										        {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
										    ] ,				)

										_snake_case :			Optional[int]  						=   object_detector(
										    [
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										        'http://images.cocodataset.org/val2017/000000039769.jpg',
										    ]					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        [
										            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
										            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
										            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
										            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
										            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
										        ],
										        [
										            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
										            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
										            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
										            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
										            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
										        ],
										    ] ,				)







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





										'''simple docstring'''







										_snake_case :			str  						=   0.9985
										_snake_case :			Optional[Any]  						=   'facebook/detr-resnet-50'

										_snake_case :			str  						=   pipeline('object-detection' ,				model=lowerCamelCase_					)

										_snake_case :			Any  						=   object_detector('http://images.cocodataset.org/val2017/000000039769.jpg' ,				threshold=lowerCamelCase_					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
										        {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
										    ] ,				)







						@require_torch
						@require_pytesseract
						@slow
						def 		__UpperCAmelCase       (    self			:		Optional[Any]					):





										'''simple docstring'''







										_snake_case :			Optional[Any]  						=   'Narsil/layoutlmv3-finetuned-funsd'
										_snake_case :			Optional[int]  						=   0.9993

										_snake_case :			List[Any]  						=   pipeline('object-detection' ,				model=lowerCamelCase_ ,				threshold=lowerCamelCase_					)

										_snake_case :			List[Any]  						=   object_detector(
										    'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'					)
										self.assertEqual(
										    nested_simplify(lowerCamelCase_ ,				decimals=4					) ,				[
										        {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
										        {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
										    ] ,				)













from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowercase_		:				Optional[int]				=						{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_		:				Any				=						[
		    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MegaForCausalLM''',
		    '''MegaForMaskedLM''',
		    '''MegaForMultipleChoice''',
		    '''MegaForQuestionAnswering''',
		    '''MegaForSequenceClassification''',
		    '''MegaForTokenClassification''',
		    '''MegaModel''',
		    '''MegaPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mega import (
				    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MegaForCausalLM,
				    MegaForMaskedLM,
				    MegaForMultipleChoice,
				    MegaForQuestionAnswering,
				    MegaForSequenceClassification,
				    MegaForTokenClassification,
				    MegaModel,
				    MegaPreTrainedModel,
				)

else:
		import sys

		lowercase_		:				str				=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






