






"""simple docstring"""







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      UpperCamelCase		:






										@staticmethod
										def      __snake_case      (		*__magic_name__  :Union[str, Any]			, **__magic_name__  :List[Any]				)      ->Any:
														pass


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class      UpperCamelCase		(unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		MODEL_FOR_OBJECT_DETECTION_MAPPING






						def      __snake_case      (		self  :Dict			, __magic_name__  :Tuple			, __magic_name__  :str			, __magic_name__  :List[str]				)      ->Tuple:
										lowercase    :      Any       = ObjectDetectionPipeline(model=lowercase_			, image_processor=lowercase_				)
										return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






						def      __snake_case      (		self  :Tuple			, __magic_name__  :Dict			, __magic_name__  :Optional[int]				)      ->Optional[int]:
										lowercase    :      Union[str, Any]       = object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			, threshold=0.0				)

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

										import datasets

										lowercase    :      int       = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""			, """image"""			, split="""test"""				)

										lowercase    :      List[Any]       = [
										    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"""],
										]
										lowercase    :      Union[str, Any]       = object_detector(lowercase_			, threshold=0.0				)

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






						@require_tf
						@unittest.skip("""Object detection not implemented in TF"""				)
						def      __snake_case      (		self  :Any				)      ->str:
										pass






						@require_torch
						def      __snake_case      (		self  :Optional[int]				)      ->Dict:
										lowercase    :      Optional[Any]       = """hf-internal-testing/tiny-detr-mobilenetsv3"""

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

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

										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
										        {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
										    ]			, )

										lowercase    :      Tuple       = object_detector(
										    [
										        """http://images.cocodataset.org/val2017/000000039769.jpg""",
										        """http://images.cocodataset.org/val2017/000000039769.jpg""",
										    ]			, threshold=0.0			, )

										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        [
										            {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
										            {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
										        ],
										        [
										            {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
										            {"""score""": 0.3376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
										        ],
										    ]			, )






						@require_torch
						@slow
						def      __snake_case      (		self  :str				)      ->Optional[Any]:
										lowercase    :      Dict       = """facebook/detr-resnet-50"""

										lowercase    :      Any       = AutoModelForObjectDetection.from_pretrained(lowercase_				)
										lowercase    :      int       = AutoFeatureExtractor.from_pretrained(lowercase_				)
										lowercase    :      str       = ObjectDetectionPipeline(model=lowercase_			, feature_extractor=lowercase_				)

										lowercase    :      Tuple       = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
										        {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
										        {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
										        {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
										        {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
										    ]			, )

										lowercase    :      List[Any]       = object_detector(
										    [
										        """http://images.cocodataset.org/val2017/000000039769.jpg""",
										        """http://images.cocodataset.org/val2017/000000039769.jpg""",
										    ]				)
										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        [
										            {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
										            {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
										            {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
										            {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
										            {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
										        ],
										        [
										            {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
										            {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
										            {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
										            {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
										            {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
										        ],
										    ]			, )






						@require_torch
						@slow
						def      __snake_case      (		self  :Optional[int]				)      ->List[Any]:
										lowercase    :      Union[str, Any]       = """facebook/detr-resnet-50"""

										lowercase    :      Dict       = pipeline("""object-detection"""			, model=lowercase_				)

										lowercase    :      Dict       = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
										        {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
										        {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
										        {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
										        {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
										    ]			, )

										lowercase    :      List[str]       = object_detector(
										    [
										        """http://images.cocodataset.org/val2017/000000039769.jpg""",
										        """http://images.cocodataset.org/val2017/000000039769.jpg""",
										    ]				)
										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        [
										            {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
										            {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
										            {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
										            {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
										            {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
										        ],
										        [
										            {"""score""": 0.9982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
										            {"""score""": 0.9960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
										            {"""score""": 0.9955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
										            {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
										            {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
										        ],
										    ]			, )






						@require_torch
						@slow
						def      __snake_case      (		self  :Any				)      ->Tuple:
										lowercase    :      Union[str, Any]       = 0.9985
										lowercase    :      List[Any]       = """facebook/detr-resnet-50"""

										lowercase    :      Dict       = pipeline("""object-detection"""			, model=lowercase_				)

										lowercase    :      str       = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""			, threshold=lowercase_				)
										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        {"""score""": 0.9988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
										        {"""score""": 0.9987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
										    ]			, )






						@require_torch
						@require_pytesseract
						@slow
						def      __snake_case      (		self  :int				)      ->Union[str, Any]:
										lowercase    :      Optional[Any]       = """Narsil/layoutlmv3-finetuned-funsd"""
										lowercase    :      List[Any]       = 0.9993

										lowercase    :      str       = pipeline("""object-detection"""			, model=lowercase_			, threshold=lowercase_				)

										lowercase    :      List[str]       = object_detector(
										    """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""				)
										self.assertEqual(
										    nested_simplify(lowercase_			, decimals=4				)			, [
										        {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
										        {"""score""": 0.9993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
										    ]			, )




from typing import TYPE_CHECKING

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


_lowerCAmelCase:						Optional[Any]   				=       {
    'configuration_swiftformer': [
        'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SwiftFormerConfig',
        'SwiftFormerOnnxConfig',
    ]
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCAmelCase:						List[str]   				=       [
	    'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'SwiftFormerForImageClassification',
	    'SwiftFormerModel',
	    'SwiftFormerPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_swiftformer import (
	    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    SwiftFormerConfig,
	    SwiftFormerOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_swiftformer import (
		    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SwiftFormerForImageClassification,
		    SwiftFormerModel,
		    SwiftFormerPreTrainedModel,
		)

else:
	import sys

	_lowerCAmelCase:						List[Any]   				=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

