
from typing import Dict, 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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
			import PIL


_A   =     logging.get_logger(__name__)






def    lowerCamelCase__				(				__lowerCAmelCase  :  Tuple				):




			"""simple docstring"""


			if isinstance(__lowerCAmelCase    ,							(list, tuple)				) and isinstance(videos[0]    ,							(list, tuple)				) and is_valid_image(videos[0][0]				):
						return videos

			elif isinstance(__lowerCAmelCase    ,							(list, tuple)				) and is_valid_image(videos[0]				):
						return [videos]

			elif is_valid_image(__lowerCAmelCase				):
						return [[videos]]

			raise ValueError(F"""Could not make batched video from {videos}"""				)





class 	_lowerCAmelCase					(	__a       ):
				_lowercase					=['''pixel_values''']




				def __init__(					self				,				_UpperCamelCase = True				,				_UpperCamelCase = None				,				_UpperCamelCase = PILImageResampling.BILINEAR				,				_UpperCamelCase = True				,				_UpperCamelCase = None				,				_UpperCamelCase = True				,				_UpperCamelCase = 1 / 255				,				_UpperCamelCase = True				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      None:
							super().__init__(**_UpperCamelCase     )
							lowerCAmelCase_						=	size if size is not None else {"shortest_edge": 224}
							lowerCAmelCase_						=	get_size_dict(_UpperCamelCase				,				default_to_square=_UpperCamelCase     )
							lowerCAmelCase_						=	crop_size if crop_size is not None else {"height": 224, "width": 224}
							lowerCAmelCase_						=	get_size_dict(_UpperCamelCase				,				param_name="crop_size"     )

							lowerCAmelCase_						=	do_resize
							lowerCAmelCase_						=	size
							lowerCAmelCase_						=	do_center_crop
							lowerCAmelCase_						=	crop_size
							lowerCAmelCase_						=	resample
							lowerCAmelCase_						=	do_rescale
							lowerCAmelCase_						=	rescale_factor
							lowerCAmelCase_						=	do_normalize
							lowerCAmelCase_						=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							lowerCAmelCase_						=	image_std if image_std is not None else IMAGENET_STANDARD_STD




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = PILImageResampling.BILINEAR				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      np.ndarray:
							lowerCAmelCase_						=	get_size_dict(_UpperCamelCase				,				default_to_square=_UpperCamelCase     )
							if "shortest_edge" in size:
										lowerCAmelCase_						=	get_resize_output_image_size(_UpperCamelCase				,				size["shortest_edge"]				,				default_to_square=_UpperCamelCase     )
							elif "height" in size and "width" in size:
										lowerCAmelCase_						=	(size["height"], size["width"])
							else:
										raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""     )
							return resize(_UpperCamelCase				,				size=_UpperCamelCase				,				resample=_UpperCamelCase				,				data_format=_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      np.ndarray:
							lowerCAmelCase_						=	get_size_dict(_UpperCamelCase     )
							if "height" not in size or "width" not in size:
										raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""     )
							return center_crop(_UpperCamelCase				,				size=(size["height"], size["width"])				,				data_format=_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      List[str]:
							return rescale(_UpperCamelCase				,				scale=_UpperCamelCase				,				data_format=_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase = None				,				**_UpperCamelCase				,				)				->      np.ndarray:
							return normalize(_UpperCamelCase				,				mean=_UpperCamelCase				,				std=_UpperCamelCase				,				data_format=_UpperCamelCase				,				**_UpperCamelCase     )




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = ChannelDimension.FIRST				,				)				->      np.ndarray:
							if do_resize and size is None or resample is None:
										raise ValueError("Size and resample 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.
							lowerCAmelCase_						=	to_numpy_array(_UpperCamelCase     )

							if do_resize:
										lowerCAmelCase_						=	self.resize(image=_UpperCamelCase				,				size=_UpperCamelCase				,				resample=_UpperCamelCase     )

							if do_center_crop:
										lowerCAmelCase_						=	self.center_crop(_UpperCamelCase				,				size=_UpperCamelCase     )

							if do_rescale:
										lowerCAmelCase_						=	self.rescale(image=_UpperCamelCase				,				scale=_UpperCamelCase     )

							if do_normalize:
										lowerCAmelCase_						=	self.normalize(image=_UpperCamelCase				,				mean=_UpperCamelCase				,				std=_UpperCamelCase     )

							lowerCAmelCase_						=	to_channel_dimension_format(_UpperCamelCase				,				_UpperCamelCase     )
							return image




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = None				,				_UpperCamelCase = ChannelDimension.FIRST				,				**_UpperCamelCase				,				)				->      PIL.Image.Image:
							lowerCAmelCase_						=	do_resize if do_resize is not None else self.do_resize
							lowerCAmelCase_						=	resample if resample is not None else self.resample
							lowerCAmelCase_						=	do_center_crop if do_center_crop is not None else self.do_center_crop
							lowerCAmelCase_						=	do_rescale if do_rescale is not None else self.do_rescale
							lowerCAmelCase_						=	rescale_factor if rescale_factor is not None else self.rescale_factor
							lowerCAmelCase_						=	do_normalize if do_normalize is not None else self.do_normalize
							lowerCAmelCase_						=	image_mean if image_mean is not None else self.image_mean
							lowerCAmelCase_						=	image_std if image_std is not None else self.image_std

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

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

							lowerCAmelCase_						=	make_batched(_UpperCamelCase     )

							lowerCAmelCase_						=	[
							    [
							        self._preprocess_image(
							            image=_UpperCamelCase				,				do_resize=_UpperCamelCase				,				size=_UpperCamelCase				,				resample=_UpperCamelCase				,				do_center_crop=_UpperCamelCase				,				crop_size=_UpperCamelCase				,				do_rescale=_UpperCamelCase				,				rescale_factor=_UpperCamelCase				,				do_normalize=_UpperCamelCase				,				image_mean=_UpperCamelCase				,				image_std=_UpperCamelCase				,				data_format=_UpperCamelCase				,				)
							        for img in video
							    ]
							    for video in videos
							]

							lowerCAmelCase_						=	{"pixel_values": videos}
							return BatchFeature(data=_UpperCamelCase				,				tensor_type=_UpperCamelCase     )





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 	_lowerCAmelCase					:




							@staticmethod
							def      __a						(					*_UpperCamelCase				,				**_UpperCamelCase     )				->      str:
										pass





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 	_lowerCAmelCase					(	unittest.TestCase       ):
				_lowercase					=MODEL_FOR_OBJECT_DETECTION_MAPPING




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase				,				_UpperCamelCase     )				->      str:
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				image_processor=_UpperCamelCase     )
							return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




				def      __a						(					self				,				_UpperCamelCase				,				_UpperCamelCase     )				->      Any:
							lowerCAmelCase_						=	object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png"				,				threshold=0.0     )

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

							import datasets

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

							lowerCAmelCase_						=	[
							    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"],
							]
							lowerCAmelCase_						=	object_detector(_UpperCamelCase				,				threshold=0.0     )

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




				@require_tf
				@unittest.skip("Object detection not implemented in TF"     )
				def      __a						(					self     )				->      Optional[int]:
							pass




				@require_torch
				def      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"hf-internal-testing/tiny-detr-mobilenetsv3"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

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

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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}},
							    ]				,				)

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

							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	AutoModelForObjectDetection.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	AutoFeatureExtractor.from_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	ObjectDetectionPipeline(model=_UpperCamelCase				,				feature_extractor=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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      __a						(					self     )				->      str:
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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}},
							    ]				,				)

							lowerCAmelCase_						=	object_detector(
							    [
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							        "http://images.cocodataset.org/val2017/000000039769.jpg",
							    ]     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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      __a						(					self     )				->      Optional[Any]:
							lowerCAmelCase_						=	0.9985
							lowerCAmelCase_						=	"facebook/detr-resnet-50"

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"				,				threshold=_UpperCamelCase     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	"Narsil/layoutlmv3-finetuned-funsd"
							lowerCAmelCase_						=	0.9993

							lowerCAmelCase_						=	pipeline("object-detection"				,				model=_UpperCamelCase				,				threshold=_UpperCamelCase     )

							lowerCAmelCase_						=	object_detector(
							    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"     )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase				,				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}},
							    ]				,				)



