

from __future__ import annotations

from collections.abc import MutableSequence





class 	snake_case_   :







			'''simple docstring'''

			def __init__(     self :				Union[str, Any]							,   _UpperCamelCase :				int							,   _UpperCamelCase :				MutableSequence[float]							)      ->int:
									if len(lowercase_							) != degree + 1:
															raise ValueError(
															    '''The number of coefficients should be equal to the degree + 1.'''							)

									snake_case_   =	list(lowercase_							)
									snake_case_   =	degree







			def __add__(     self :				int							,   _UpperCamelCase :				Polynomial							)      ->Tuple:

									if self.degree > polynomial_a.degree:
															snake_case_   =	self.coefficients[:]
															for i in range(polynomial_a.degree + 1							):
																					coefficients[i] += polynomial_a.coefficients[i]
															return Polynomial(self.degree							,   lowercase_							)
									else:
															snake_case_   =	polynomial_a.coefficients[:]
															for i in range(self.degree + 1							):
																					coefficients[i] += self.coefficients[i]
															return Polynomial(polynomial_a.degree							,   lowercase_							)







			def __sub__(     self :				str							,   _UpperCamelCase :				Polynomial							)      ->List[str]:
									return self + polynomial_a * Polynomial(0							,   [-1]							)







			def __neg__(     self :				Dict							)      ->Union[str, Any]:
									return Polynomial(self.degree							,   [-c for c in self.coefficients]							)







			def __mul__(     self :				Optional[Any]							,   _UpperCamelCase :				Polynomial							)      ->str:
									snake_case_   =	[0] * (self.degree + polynomial_a.degree + 1)
									for i in range(self.degree + 1							):
															for j in range(polynomial_a.degree + 1							):
																					coefficients[i + j] += (
																					    self.coefficients[i] * polynomial_a.coefficients[j]
																					)

									return Polynomial(self.degree + polynomial_a.degree							,   lowercase_							)







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				int | float							)      ->Union[str, Any]:
									snake_case_   =	0
									for i in range(self.degree + 1							):
															result += self.coefficients[i] * (substitution**i)
									return result







			def __str__(     self :				Optional[int]							)      ->Optional[int]:
									snake_case_   =	''''''
									for i in range(self.degree							,   -1							,   -1							):
															if self.coefficients[i] == 0:
																					continue
															elif self.coefficients[i] > 0:
																					if polynomial:
																											polynomial += " + "
															else:
																					polynomial += " - "

															if i == 0:
																					polynomial += str(abs(self.coefficients[i]							)							)
															elif i == 1:
																					polynomial += str(abs(self.coefficients[i]							)							) + "x"
															else:
																					polynomial += str(abs(self.coefficients[i]							)							) + "x^" + str(lowercase_							)

									return polynomial







			def __repr__(     self :				Tuple							)      ->Optional[Any]:
									return self.__str__()







			def 				snake_case__(     self :				int							)      ->List[Any]:
									snake_case_   =	[0] * self.degree
									for i in range(self.degree							):
															snake_case_   =	self.coefficients[i + 1] * (i + 1)
									return Polynomial(self.degree - 1							,   lowercase_							)







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				int | float = 0							)      ->str:
									snake_case_   =	[0] * (self.degree + 2)
									snake_case_   =	constant
									for i in range(self.degree + 1							):
															snake_case_   =	self.coefficients[i] / (i + 1)
									return Polynomial(self.degree + 1							,   lowercase_							)







			def __eq__(     self :				Union[str, Any]							,   _UpperCamelCase :				object							)      ->str:
									if not isinstance(lowercase_							,   lowercase_							):
															return False

									if self.degree != polynomial_a.degree:
															return False

									for i in range(self.degree + 1							):
															if self.coefficients[i] != polynomial_a.coefficients[i]:
																					return False

									return True







			def __ne__(     self :				Optional[Any]							,   _UpperCamelCase :				object							)      ->Optional[int]:
									return not self.__eq__(lowercase_							)





'''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 						a				:


											@staticmethod
											def   A_ (		*lowercase_    :					int       ,				**lowercase_    :					str					):
																pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						a				(   unittest.TestCase     ):
				snake_case_      =		MODEL_FOR_OBJECT_DETECTION_MAPPING


				def   A_ (		self    :					Any       ,				lowercase_    :					List[Any]       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]					):
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				image_processor=lowercase_					)
									return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


				def   A_ (		self    :					Dict       ,				lowercase_    :					List[Any]       ,				lowercase_    :					int					):
									snake_case_									=				object_detector('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       ,				threshold=0.0					)

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

									import datasets

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

									snake_case_									=				[
									    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_									=				object_detector(lowercase_       ,				threshold=0.0					)

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


				@require_tf
				@unittest.skip('''Object detection not implemented in TF'''					)
				def   A_ (		self    :					int					):
									pass


				@require_torch
				def   A_ (		self    :					Tuple					):
									snake_case_									=				'''hf-internal-testing/tiny-detr-mobilenetsv3'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

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

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

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

									self.assertEqual(
									    nested_simplify(lowercase_       ,				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[int]					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				AutoModelForObjectDetection.from_pretrained(lowercase_					)
									snake_case_									=				AutoFeatureExtractor.from_pretrained(lowercase_					)
									snake_case_									=				ObjectDetectionPipeline(model=lowercase_       ,				feature_extractor=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

									snake_case_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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    :					Tuple					):
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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}},
									    ]       ,				)

									snake_case_									=				object_detector(
									    [
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									        '''http://images.cocodataset.org/val2017/000000039769.jpg''',
									    ]					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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					):
									snake_case_									=				0.9985
									snake_case_									=				'''facebook/detr-resnet-50'''

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_					)

									snake_case_									=				object_detector('''http://images.cocodataset.org/val2017/000000039769.jpg'''       ,				threshold=lowercase_					)
									self.assertEqual(
									    nested_simplify(lowercase_       ,				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    :					Dict					):
									snake_case_									=				'''Narsil/layoutlmv3-finetuned-funsd'''
									snake_case_									=				0.9993

									snake_case_									=				pipeline('''object-detection'''       ,				model=lowercase_       ,				threshold=lowercase_					)

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

