


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    :

								"""simple docstring"""







								@staticmethod
								def      _UpperCAmelCase					(						*a__				,   **a__   )	->				Optional[int]:
									pass






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

				"""simple docstring"""







				lowerCAmelCase 		= MODEL_FOR_OBJECT_DETECTION_MAPPING

				def      _UpperCAmelCase					(						self				,   a__				,   a__				,   a__   )	->				Optional[int]:
					A								=				ObjectDetectionPipeline(model=a__				,   image_processor=a__   )
					return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]

				def      _UpperCAmelCase					(						self				,   a__				,   a__   )	->				Tuple:
					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      _UpperCAmelCase					(						self   )	->				List[Any]:
					pass

				@require_torch
				def      _UpperCAmelCase					(						self   )	->				Any:
					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""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
					        {"""score""": 0.33_76, """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""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
					            {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
					        ],
					        [
					            {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
					            {"""score""": 0.33_76, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
					        ],
					    ]				,   )

				@require_torch
				@slow
				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					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""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
					        {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
					        {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
					        {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
					        {"""score""": 0.99_87, """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""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
					            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
					            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
					            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
					            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
					        ],
					        [
					            {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
					            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
					            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
					            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
					            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
					        ],
					    ]				,   )

				@require_torch
				@slow
				def      _UpperCAmelCase					(						self   )	->				int:
					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""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
					        {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
					        {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
					        {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
					        {"""score""": 0.99_87, """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""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
					            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
					            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
					            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
					            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
					        ],
					        [
					            {"""score""": 0.99_82, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
					            {"""score""": 0.99_60, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
					            {"""score""": 0.99_55, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
					            {"""score""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
					            {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
					        ],
					    ]				,   )

				@require_torch
				@slow
				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				0.99_85
					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""": 0.99_88, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
					        {"""score""": 0.99_87, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
					    ]				,   )

				@require_torch
				@require_pytesseract
				@slow
				def      _UpperCAmelCase					(						self   )	->				Optional[int]:
					A								=				"""Narsil/layoutlmv3-finetuned-funsd"""
					A								=				0.99_93

					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""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
					        {"""score""": 0.99_93, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
					    ]				,   )







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







	A								=				zip(UpperCamelCase__							,					UpperCamelCase__						)
	# List of distances of all points from the point to be classified
	A								=				[]
	for data_point in data:
		A								=				euclidean_distance(data_point[0]							,					UpperCamelCase__						)
		distances.append((distance, data_point[1])						)
	# Choosing 'k' points with the least distances.
	A								=				[i[1] for i in sorted(UpperCamelCase__						)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	A								=				Counter(UpperCamelCase__						).most_common(1						)[0][0]
	return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



