





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       :







									"""simple docstring"""




									@staticmethod
									def  A_     ( *_UpperCAmelCase     :					Dict,			**_UpperCAmelCase     :					Any						)				->			str:





																"""simple docstring"""
																pass


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







							"""simple docstring"""




							UpperCAmelCase_      						=   MODEL_FOR_OBJECT_DETECTION_MAPPING






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[Any],			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						ObjectDetectionPipeline(model=_a,			image_processor=_a						)
														return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]






							def  A_     ( self     :					str,			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					Union[str, Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						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

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

														SCREAMING_SNAKE_CASE__       :      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"],
														]
														SCREAMING_SNAKE_CASE__       :      List[str]     =						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     :					int						)				->			Optional[Any]:





														"""simple docstring"""
														pass






							@require_torch
							def  A_     ( self     :					int						)				->			Union[str, Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						"hf-internal-testing/tiny-detr-mobilenetsv3"

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						AutoModelForObjectDetection.from_pretrained(_a						)
														SCREAMING_SNAKE_CASE__       :      Any     =						AutoFeatureExtractor.from_pretrained(_a						)
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						ObjectDetectionPipeline(model=_a,			feature_extractor=_a						)

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

														self.assertEqual(
														    nested_simplify(_a,			decimals=4						),			[
														        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
														        {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
														    ],			)

														SCREAMING_SNAKE_CASE__       :      str     =						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": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
														            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
														        ],
														        [
														            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
														            {"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_5_9, "ymin": 1_2_0, "xmax": 4_8_0, "ymax": 3_5_9}},
														        ],
														    ],			)






							@require_torch
							@slow
							def  A_     ( self     :					List[str]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						"facebook/detr-resnet-50"

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						AutoModelForObjectDetection.from_pretrained(_a						)
														SCREAMING_SNAKE_CASE__       :      str     =						AutoFeatureExtractor.from_pretrained(_a						)
														SCREAMING_SNAKE_CASE__       :      Any     =						ObjectDetectionPipeline(model=_a,			feature_extractor=_a						)

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
														self.assertEqual(
														    nested_simplify(_a,			decimals=4						),			[
														        {"score": 0.9982, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
														        {"score": 0.9960, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
														        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
														        {"score": 0.9988, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
														        {"score": 0.9987, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
														    ],			)

														SCREAMING_SNAKE_CASE__       :      Any     =						object_detector(
														    [
														        "http://images.cocodataset.org/val2017/000000039769.jpg",
														        "http://images.cocodataset.org/val2017/000000039769.jpg",
														    ]						)
														self.assertEqual(
														    nested_simplify(_a,			decimals=4						),			[
														        [
														            {"score": 0.9982, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
														            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
														            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
														            {"score": 0.9988, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
														            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
														        ],
														        [
														            {"score": 0.9982, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
														            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
														            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
														            {"score": 0.9988, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
														            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
														        ],
														    ],			)






							@require_torch
							@slow
							def  A_     ( self     :					List[Any]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Dict     =						"facebook/detr-resnet-50"

														SCREAMING_SNAKE_CASE__       :      Any     =						pipeline("object-detection",			model=_a						)

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						object_detector("http://images.cocodataset.org/val2017/000000039769.jpg"						)
														self.assertEqual(
														    nested_simplify(_a,			decimals=4						),			[
														        {"score": 0.9982, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
														        {"score": 0.9960, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
														        {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
														        {"score": 0.9988, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
														        {"score": 0.9987, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
														    ],			)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						object_detector(
														    [
														        "http://images.cocodataset.org/val2017/000000039769.jpg",
														        "http://images.cocodataset.org/val2017/000000039769.jpg",
														    ]						)
														self.assertEqual(
														    nested_simplify(_a,			decimals=4						),			[
														        [
														            {"score": 0.9982, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
														            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
														            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
														            {"score": 0.9988, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
														            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
														        ],
														        [
														            {"score": 0.9982, "label": "remote", "box": {"xmin": 4_0, "ymin": 7_0, "xmax": 1_7_5, "ymax": 1_1_7}},
														            {"score": 0.9960, "label": "remote", "box": {"xmin": 3_3_3, "ymin": 7_2, "xmax": 3_6_8, "ymax": 1_8_7}},
														            {"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_3_9, "ymax": 4_7_3}},
														            {"score": 0.9988, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
														            {"score": 0.9987, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
														        ],
														    ],			)






							@require_torch
							@slow
							def  A_     ( self     :					Optional[int]						)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						0.9985
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						"facebook/detr-resnet-50"

														SCREAMING_SNAKE_CASE__       :      int     =						pipeline("object-detection",			model=_a						)

														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						object_detector("http://images.cocodataset.org/val2017/000000039769.jpg",			threshold=_a						)
														self.assertEqual(
														    nested_simplify(_a,			decimals=4						),			[
														        {"score": 0.9988, "label": "cat", "box": {"xmin": 1_3, "ymin": 5_2, "xmax": 3_1_4, "ymax": 4_7_0}},
														        {"score": 0.9987, "label": "cat", "box": {"xmin": 3_4_5, "ymin": 2_3, "xmax": 6_4_0, "ymax": 3_6_8}},
														    ],			)






							@require_torch
							@require_pytesseract
							@slow
							def  A_     ( self     :					List[Any]						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						"Narsil/layoutlmv3-finetuned-funsd"
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						0.9993

														SCREAMING_SNAKE_CASE__       :      str     =						pipeline("object-detection",			model=_a,			threshold=_a						)

														SCREAMING_SNAKE_CASE__       :      List[Any]     =						object_detector(
														    "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"						)
														self.assertEqual(
														    nested_simplify(_a,			decimals=4						),			[
														        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
														        {"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_9_4, "ymin": 2_5_4, "xmax": 3_4_3, "ymax": 2_6_4}},
														    ],			)













class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						name
														SCREAMING_SNAKE_CASE__       :      Tuple     =						val






							def __str__( self     :					str						)				->			List[Any]:





														"""simple docstring"""
														return F'''{self.__class__.__name__}({self.name}, {self.val})'''






							def __lt__( self     :					str,			_UpperCAmelCase     :					Dict						)				->			Union[str, Any]:





														"""simple docstring"""
														return self.val < other.val


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Optional[Any],			_UpperCAmelCase     :					str						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						{}
														SCREAMING_SNAKE_CASE__       :      str     =						{}
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						self.build_heap(_UpperCAmelCase						)






							def __getitem__( self     :					Union[str, Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return self.get_value(_UpperCAmelCase						)






							def  A_     ( self     :					int,			_UpperCAmelCase     :					Optional[int]						)				->			Any:





														"""simple docstring"""
														return (idx - 1) // 2






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Any						)				->			Dict:





														"""simple docstring"""
														return idx * 2 + 1






							def  A_     ( self     :					str,			_UpperCAmelCase     :					List[str]						)				->			Union[str, Any]:





														"""simple docstring"""
														return idx * 2 + 2






							def  A_     ( self     :					Tuple,			_UpperCAmelCase     :					int						)				->			Optional[int]:





														"""simple docstring"""
														return self.heap_dict[key]






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Dict						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						len(_UpperCAmelCase						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.get_parent_idx(_UpperCAmelCase						)

														for idx, i in enumerate(_UpperCAmelCase						):
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						idx
																					SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						i.val

														for i in range(_UpperCAmelCase,			-1,			-1						):
																					self.sift_down(_UpperCAmelCase,			_UpperCAmelCase						)
														return array






							def  A_     ( self     :					List[Any],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					Tuple						)				->			Tuple:





														"""simple docstring"""
														while True:
																					SCREAMING_SNAKE_CASE__       :      str     =						self.get_left_child_idx(_UpperCAmelCase						)  # noqa: E741
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.get_right_child_idx(_UpperCAmelCase						)

																					SCREAMING_SNAKE_CASE__       :      Tuple     =						idx
																					if l < len(_UpperCAmelCase						) and array[l] < array[idx]:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						l
																					if r < len(_UpperCAmelCase						) and array[r] < array[smallest]:
																												SCREAMING_SNAKE_CASE__       :      int     =						r

																					if smallest != idx:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						array[smallest], array[idx]
																												(
																												    (
																												    SCREAMING_SNAKE_CASE__
																												)   ,(
																												    SCREAMING_SNAKE_CASE__
																												)   ,
																												)       :      Optional[Any]     =						(
																												    self.idx_of_element[array[smallest]],
																												    self.idx_of_element[array[idx]],
																												)
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						smallest
																					else:
																												break






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Any:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						self.get_parent_idx(_UpperCAmelCase						)
														while p >= 0 and self.heap[p] > self.heap[idx]:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Any     =						self.heap[idx], self.heap[p]
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						(
																					    self.idx_of_element[self.heap[idx]],
																					    self.idx_of_element[self.heap[p]],
																					)
																					SCREAMING_SNAKE_CASE__       :      Dict     =						p
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						self.get_parent_idx(_UpperCAmelCase						)






							def  A_     ( self     :					str						)				->			List[str]:





														"""simple docstring"""
														return self.heap[0]






							def  A_     ( self     :					Tuple						)				->			List[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self.heap[-1], self.heap[0]
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[Any]     =						(
														    self.idx_of_element[self.heap[-1]],
														    self.idx_of_element[self.heap[0]],
														)

														SCREAMING_SNAKE_CASE__       :      Any     =						self.heap.pop()
														del self.idx_of_element[x]
														self.sift_down(0,			self.heap						)
														return x






							def  A_     ( self     :					Union[str, Any],			_UpperCAmelCase     :					str						)				->			Optional[Any]:





														"""simple docstring"""
														self.heap.append(_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      str     =						len(self.heap						) - 1
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						node.val
														self.sift_up(len(self.heap						) - 1						)






							def  A_     ( self     :					Optional[int]						)				->			int:





														"""simple docstring"""
														return len(self.heap						) == 0






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Tuple,			_UpperCAmelCase     :					str						)				->			Dict:





														"""simple docstring"""
														assert (
														    self.heap[self.idx_of_element[node]].val > new_value
														), "newValue must be less that current value"
														SCREAMING_SNAKE_CASE__       :      Tuple     =						new_value
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						new_value
														self.sift_up(self.idx_of_element[node]						)


_lowerCamelCase :    Tuple  							= Node('''R''', -1)
_lowerCamelCase :    int  							= Node('''B''', 6)
_lowerCamelCase :    str  							= Node('''A''', 3)
_lowerCamelCase :    Optional[Any]  							= Node('''X''', 1)
_lowerCamelCase :    str  							= Node('''E''', 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
_lowerCamelCase :    int  							= MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print('''Min Heap - before decrease key''')
for i in my_min_heap.heap:
		print(i)

print('''Min Heap - After decrease key of node [B -> -17]''')
my_min_heap.decrease_key(b, -1_7)

# After
for i in my_min_heap.heap:
		print(i)

if __name__ == "__main__":
		import doctest

		doctest.testmod()






