



from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCAmelCase							 =					logging.get_logger(__name__)

class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							UpperCamelCase__				:      List[Any]         =					["""pixel_values"""]


							def __init__(							self  ,  a_ = True  ,  a_ = None  ,  a_ = PILImageResampling.BICUBIC  ,  a_ = True  ,  a_ = None  ,  a_ = True  ,  a_ = 1 / 255  ,  a_ = True  ,  a_ = IMAGENET_DEFAULT_MEAN  ,  a_ = IMAGENET_DEFAULT_STD  ,  **a_  ,  ):
											super().__init__(**a_		)
											a__				      =		size if size is not None else {"""shortest_edge""": 224}
											a__				      =		get_size_dict(a_  ,  default_to_square=a_		)
											a__				      =		crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
											a__				      =		get_size_dict(a_  ,  param_name="""crop_size"""		)

											a__				      =		do_resize
											a__				      =		size
											a__				      =		resample
											a__				      =		do_center_crop
											a__				      =		crop_size
											a__				      =		do_rescale
											a__				      =		rescale_factor
											a__				      =		do_normalize
											a__				      =		image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
											a__				      =		image_std if image_std is not None else IMAGENET_DEFAULT_STD


							def _a      (							self  ,  a_  ,  a_  ,  a_ = PILImageResampling.BICUBIC  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(a_  ,  default_to_square=a_		)
											# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
											if "shortest_edge" in size:
															a__				      =		int((256 / 224) * size["""shortest_edge"""]		)
															a__				      =		get_resize_output_image_size(a_  ,  size=a_  ,  default_to_square=a_		)
															a__				      =		{"""height""": output_size[0], """width""": output_size[1]}
											if "height" not in size_dict or "width" not in size_dict:
															raise ValueError(
															    F'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'''		)
											return resize(
											    a_  ,  size=(size_dict["""height"""], size_dict["""width"""])  ,  resample=a_  ,  data_format=a_  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											a__				      =		get_size_dict(a_		)
											if "height" not in size or "width" not in size:
															raise ValueError(F'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
											return center_crop(a_  ,  size=(size["""height"""], size["""width"""])  ,  data_format=a_  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											return rescale(a_  ,  scale=a_  ,  data_format=a_  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_  ,  a_  ,  a_ = None  ,  **a_  ,  ):
											return normalize(a_  ,  mean=a_  ,  std=a_  ,  data_format=a_  ,  **a_		)







							def _a      (							self  ,  a_  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = None  ,  a_ = ChannelDimension.FIRST  ,  **a_  ,  ):
											a__				      =		do_resize if do_resize is not None else self.do_resize
											a__				      =		resample if resample is not None else self.resample
											a__				      =		do_center_crop if do_center_crop is not None else self.do_center_crop
											a__				      =		do_rescale if do_rescale is not None else self.do_rescale
											a__				      =		rescale_factor if rescale_factor is not None else self.rescale_factor
											a__				      =		do_normalize if do_normalize is not None else self.do_normalize
											a__				      =		image_mean if image_mean is not None else self.image_mean
											a__				      =		image_std if image_std is not None else self.image_std

											a__				      =		size if size is not None else self.size
											a__				      =		get_size_dict(a_  ,  default_to_square=a_		)
											a__				      =		crop_size if crop_size is not None else self.crop_size
											a__				      =		get_size_dict(a_  ,  param_name="""crop_size"""		)

											a__				      =		make_list_of_images(a_		)

											if not valid_images(a_		):
															raise ValueError(
															    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
															    """torch.Tensor, tf.Tensor or jax.ndarray."""		)

											if do_resize and size is None:
															raise ValueError("""Size must be specified if do_resize is True."""		)

											if do_center_crop and crop_size is None:
															raise ValueError("""Crop size must be specified if do_center_crop is True."""		)

											if do_rescale and rescale_factor is None:
															raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

											if do_normalize and (image_mean is None or image_std is None):
															raise ValueError("""Image mean and std must be specified if do_normalize is True."""		)

											# All transformations expect numpy arrays.
											a__				      =		[to_numpy_array(a_		) for image in images]

											if do_resize:
															a__				      =		[self.resize(a_  ,  a_  ,  a_		) for image in images]

											if do_center_crop:
															a__				      =		[self.center_crop(a_  ,  a_		) for image in images]

											if do_rescale:
															a__				      =		[self.rescale(a_  ,  a_		) for image in images]

											if do_normalize:
															a__				      =		[self.normalize(a_  ,  a_  ,  a_		) for image in images]

											a__				      =		[to_channel_dimension_format(a_  ,  a_		) for image in images]

											a__				      =		{"""pixel_values""": images}
											return BatchFeature(data=a_  ,  tensor_type=a_		)






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  __snake_case      :







								'''simple docstring'''






								@staticmethod
								def _a      (							*a_  ,  **a_		):
												pass

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







							'''simple docstring'''






							UpperCamelCase__				:      Dict         =					MODEL_FOR_OBJECT_DETECTION_MAPPING


							def _a      (							self  ,  a_  ,  a_  ,  a_		):
											a__				      =		ObjectDetectionPipeline(model=a_  ,  image_processor=a_		)
											return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


							def _a      (							self  ,  a_  ,  a_		):
											a__				      =		object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  ,  threshold=0.0		)

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

											import datasets

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

											a__				      =		[
											    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"""],
											]
											a__				      =		object_detector(a_  ,  threshold=0.0		)

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


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


							@require_torch
							def _a      (							self		):
											a__				      =		"""hf-internal-testing/tiny-detr-mobilenetsv3"""

											a__				      =		AutoModelForObjectDetection.from_pretrained(a_		)
											a__				      =		AutoFeatureExtractor.from_pretrained(a_		)
											a__				      =		ObjectDetectionPipeline(model=a_  ,  feature_extractor=a_		)

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

											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											        {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											    ]  ,  )

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

											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        [
											            {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											            {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											        ],
											        [
											            {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											            {"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
											        ],
											    ]  ,  )


							@require_torch
							@slow
							def _a      (							self		):
											a__				      =		"""facebook/detr-resnet-50"""

											a__				      =		AutoModelForObjectDetection.from_pretrained(a_		)
											a__				      =		AutoFeatureExtractor.from_pretrained(a_		)
											a__				      =		ObjectDetectionPipeline(model=a_  ,  feature_extractor=a_		)

											a__				      =		object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											        {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											        {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											        {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											        {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											    ]  ,  )

											a__				      =		object_detector(
											    [
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											    ]		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											    ]  ,  )


							@require_torch
							@slow
							def _a      (							self		):
											a__				      =		"""facebook/detr-resnet-50"""

											a__				      =		pipeline("""object-detection"""  ,  model=a_		)

											a__				      =		object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											        {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											        {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											        {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											        {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											    ]  ,  )

											a__				      =		object_detector(
											    [
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											        """http://images.cocodataset.org/val2017/000000039769.jpg""",
											    ]		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											        [
											            {"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
											            {"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
											            {"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
											            {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											            {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											        ],
											    ]  ,  )


							@require_torch
							@slow
							def _a      (							self		):
											a__				      =		0.9_985
											a__				      =		"""facebook/detr-resnet-50"""

											a__				      =		pipeline("""object-detection"""  ,  model=a_		)

											a__				      =		object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg"""  ,  threshold=a_		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
											        {"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
											    ]  ,  )







							@require_torch
							@require_pytesseract
							@slow
							def _a      (							self		):
											a__				      =		"""Narsil/layoutlmv3-finetuned-funsd"""
											a__				      =		0.9_993

											a__				      =		pipeline("""object-detection"""  ,  model=a_  ,  threshold=a_		)

											a__				      =		object_detector(
											    """https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"""		)
											self.assertEqual(
											    nested_simplify(a_  ,  decimals=4		)  ,  [
											        {"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
											        {"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
											    ]  ,  )

