


"""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 :






					'''simple docstring'''

					@staticmethod
					def 				SCREAMING_SNAKE_CASE  (*a_				,       **a_					):





						'''simple docstring'''

						pass



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






		'''simple docstring'''

		lowerCamelCase__				=MODEL_FOR_OBJECT_DETECTION_MAPPING



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Any						=				ObjectDetectionPipeline(model=a_				,       image_processor=a_					)
			return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]



		def 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				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

			__snake_case	:						Dict						=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''				,       '''image'''				,       split='''test'''					)

			__snake_case	:						Tuple						=				[
			    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'''],
			]
			__snake_case	:						List[Any]						=				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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			pass



		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

			__snake_case	:						Union[str, Any]						=				AutoModelForObjectDetection.from_pretrained(a_					)
			__snake_case	:						Dict						=				AutoFeatureExtractor.from_pretrained(a_					)
			__snake_case	:						Optional[Any]						=				ObjectDetectionPipeline(model=a_				,       feature_extractor=a_					)

			__snake_case	:						Dict						=				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_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			        {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			    ]				,       )

			__snake_case	:						Dict						=				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_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			        ],
			        [
			            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			            {'''score''': 0.3376, '''label''': '''LABEL_0''', '''box''': {'''xmin''': 1_59, '''ymin''': 1_20, '''xmax''': 4_80, '''ymax''': 3_59}},
			        ],
			    ]				,       )



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Dict						=				'''facebook/detr-resnet-50'''

			__snake_case	:						int						=				AutoModelForObjectDetection.from_pretrained(a_					)
			__snake_case	:						int						=				AutoFeatureExtractor.from_pretrained(a_					)
			__snake_case	:						List[Any]						=				ObjectDetectionPipeline(model=a_				,       feature_extractor=a_					)

			__snake_case	:						Union[str, Any]						=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			    ]				,       )

			__snake_case	:						Optional[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''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			        [
			            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			    ]				,       )



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				'''facebook/detr-resnet-50'''

			__snake_case	:						Any						=				pipeline('''object-detection'''				,       model=a_					)

			__snake_case	:						Dict						=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
			self.assertEqual(
			    nested_simplify(a_				,       decimals=4					)				,       [
			        {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			        {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			        {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			        {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			    ]				,       )

			__snake_case	:						Optional[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''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			        [
			            {'''score''': 0.9982, '''label''': '''remote''', '''box''': {'''xmin''': 40, '''ymin''': 70, '''xmax''': 1_75, '''ymax''': 1_17}},
			            {'''score''': 0.9960, '''label''': '''remote''', '''box''': {'''xmin''': 3_33, '''ymin''': 72, '''xmax''': 3_68, '''ymax''': 1_87}},
			            {'''score''': 0.9955, '''label''': '''couch''', '''box''': {'''xmin''': 0, '''ymin''': 1, '''xmax''': 6_39, '''ymax''': 4_73}},
			            {'''score''': 0.9988, '''label''': '''cat''', '''box''': {'''xmin''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			            {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			        ],
			    ]				,       )



		@require_torch
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				0.9985
			__snake_case	:						Optional[int]						=				'''facebook/detr-resnet-50'''

			__snake_case	:						Any						=				pipeline('''object-detection'''				,       model=a_					)

			__snake_case	:						Optional[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''': 13, '''ymin''': 52, '''xmax''': 3_14, '''ymax''': 4_70}},
			        {'''score''': 0.9987, '''label''': '''cat''', '''box''': {'''xmin''': 3_45, '''ymin''': 23, '''xmax''': 6_40, '''ymax''': 3_68}},
			    ]				,       )



		@require_torch
		@require_pytesseract
		@slow
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				'''Narsil/layoutlmv3-finetuned-funsd'''
			__snake_case	:						Optional[Any]						=				0.9993

			__snake_case	:						int						=				pipeline('''object-detection'''				,       model=a_				,       threshold=a_					)

			__snake_case	:						Dict						=				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_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
			        {'''score''': 0.9993, '''label''': '''I-ANSWER''', '''box''': {'''xmin''': 2_94, '''ymin''': 2_54, '''xmax''': 3_43, '''ymax''': 2_64}},
			    ]				,       )









"""simple docstring"""
import numpy as np






def 		lowercase							(     _snake_case						:      int     ,   _snake_case						:      Optional[Any]     ,   _snake_case						:      Optional[int]     ,   _snake_case						:      int     ,   _snake_case						:      Union[str, Any]     )     ->Dict:
	"""simple docstring"""





	__snake_case	:						Union[str, Any]						=				int(np.ceil((x_end - xa) / h     )     )
	__snake_case	:						Dict						=				np.zeros((n + 1,)     )
	__snake_case	:						List[Any]						=				ya
	__snake_case	:						int						=				xa

	for k in range(_snake_case     ):
		__snake_case	:						Any						=				f(_snake_case     ,   y[k]     )
		__snake_case	:						List[Any]						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						int						=				f(x + 0.5 * h     ,   y[k] + 0.5 * h * ka     )
		__snake_case	:						Optional[int]						=				f(x + h     ,   y[k] + h * ka     )
		__snake_case	:						Optional[int]						=				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
		x += h

	return y


if __name__ == "__main__":
			import doctest

			doctest.testmod()





