



import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor

class      snake_case__       (unittest.TestCase	):







						"""simple docstring"""




						def __init__(     self      ,     __lowercase      ,     __lowercase=7      ,     __lowercase=3      ,     __lowercase=3_0      ,     __lowercase=4_0_0      ,     __lowercase=True      ,     __lowercase=None      ,     __lowercase=True      ,     __lowercase=[0.5, 0.5, 0.5]      ,     __lowercase=[0.5, 0.5, 0.5]      ,     __lowercase=False      ,     )      ->   Optional[int]:
									"""simple docstring"""




									a__     :		int		 =						size if size is not None else {"""height""": 3_0, """width""": 3_0}
									a__     :		Any		 =						parent
									a__     :		Union[str, Any]		 =						batch_size
									a__     :		int		 =						num_channels
									a__     :		Any		 =						min_resolution
									a__     :		int		 =						max_resolution
									a__     :		Dict		 =						do_resize
									a__     :		Optional[int]		 =						size
									a__     :		Optional[int]		 =						do_normalize
									a__     :		Dict		 =						image_mean
									a__     :		Optional[Any]		 =						image_std
									a__     :		Optional[Any]		 =						do_reduce_labels

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[str]:
									"""simple docstring"""




									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_normalize": self.do_normalize,
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_reduce_labels": self.do_reduce_labels,
									}




def 			lowerCAmelCase_  (						)							-> int:







			"""simple docstring"""
			a__     :		Optional[int]		 =						load_dataset("""hf-internal-testing/fixtures_ade20k"""		,     split="""test""")

			a__     :		Union[str, Any]		 =						Image.open(dataset[0]["""file"""])
			a__     :		Any		 =						Image.open(dataset[1]["""file"""])

			return image, map

def 			lowerCAmelCase_  (						)							-> Optional[int]:







			"""simple docstring"""
			a__     :		List[str]		 =						load_dataset("""hf-internal-testing/fixtures_ade20k"""		,     split="""test""")

			a__     :		str		 =						Image.open(dataset[0]["""file"""])
			a__     :		str		 =						Image.open(dataset[1]["""file"""])
			a__     :		Any		 =						Image.open(dataset[2]["""file"""])
			a__     :		int		 =						Image.open(dataset[3]["""file"""])

			return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class      snake_case__       (A__      ,			unittest.TestCase	):







						"""simple docstring"""




						__lowerCAmelCase  :Optional[Any]			      =  SegformerImageProcessor if is_vision_available() else None

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									a__     :		Any		 =						SegformerImageProcessingTester(self						)

						@property
						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   str:
									"""simple docstring"""




									return self.image_processor_tester.prepare_image_processor_dict()

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									a__     :		Optional[int]		 =						self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(__lowercase      ,     """do_resize"""						)						)
									self.assertTrue(hasattr(__lowercase      ,     """size"""						)						)
									self.assertTrue(hasattr(__lowercase      ,     """do_normalize"""						)						)
									self.assertTrue(hasattr(__lowercase      ,     """image_mean"""						)						)
									self.assertTrue(hasattr(__lowercase      ,     """image_std"""						)						)
									self.assertTrue(hasattr(__lowercase      ,     """do_reduce_labels"""						)						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   List[Any]:
									"""simple docstring"""




									a__     :		str		 =						self.image_processing_class.from_dict(self.image_processor_dict						)
									self.assertEqual(image_processor.size      ,     {"""height""": 3_0, """width""": 3_0}						)
									self.assertEqual(image_processor.do_reduce_labels      ,     __lowercase						)

									a__     :		str		 =						self.image_processing_class.from_dict(self.image_processor_dict      ,     size=4_2      ,     reduce_labels=__lowercase						)
									self.assertEqual(image_processor.size      ,     {"""height""": 4_2, """width""": 4_2}						)
									self.assertEqual(image_processor.do_reduce_labels      ,     __lowercase						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[int]:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Dict:
									"""simple docstring"""




									a__     :		Tuple		 =						self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									a__     :		List[str]		 =						prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=__lowercase						)
									for image in image_inputs:
												self.assertIsInstance(__lowercase      ,     Image.Image						)

									# Test not batched input
									a__     :		Optional[int]		 =						image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )

									# Test batched
									a__     :		Optional[int]		 =						image_processing(__lowercase      ,     return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   str:
									"""simple docstring"""




									a__     :		List[Any]		 =						self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									a__     :		Any		 =						prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=__lowercase      ,     numpify=__lowercase						)
									for image in image_inputs:
												self.assertIsInstance(__lowercase      ,     np.ndarray						)

									# Test not batched input
									a__     :		int		 =						image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )

									# Test batched
									a__     :		Any		 =						image_processing(__lowercase      ,     return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Optional[Any]:
									"""simple docstring"""




									a__     :		Any		 =						self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									a__     :		List[str]		 =						prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=__lowercase      ,     torchify=__lowercase						)
									for image in image_inputs:
												self.assertIsInstance(__lowercase      ,     torch.Tensor						)

									# Test not batched input
									a__     :		List[Any]		 =						image_processing(image_inputs[0]      ,     return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )

									# Test batched
									a__     :		Optional[int]		 =						image_processing(__lowercase      ,     return_tensors="""pt"""						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   Tuple:
									"""simple docstring"""




									a__     :		Dict		 =						self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									a__     :		Dict		 =						prepare_image_inputs(self.image_processor_tester      ,     equal_resolution=__lowercase      ,     torchify=__lowercase						)
									a__     :		Any		 =						[]
									for image in image_inputs:
												self.assertIsInstance(__lowercase      ,     torch.Tensor						)
												maps.append(torch.zeros(image.shape[-2:]						).long()						)

									# Test not batched input
									a__     :		List[str]		 =						image_processing(image_inputs[0]      ,     maps[0]      ,     return_tensors="""pt"""						)
									self.assertEqual(
									    encoding["""pixel_values"""].shape      ,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(
									    encoding["""labels"""].shape      ,     (
									        1,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
									self.assertTrue(encoding["""labels"""].min().item() >= 0						)
									self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5						)

									# Test batched
									a__     :		Union[str, Any]		 =						image_processing(__lowercase      ,     __lowercase      ,     return_tensors="""pt"""						)
									self.assertEqual(
									    encoding["""pixel_values"""].shape      ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(
									    encoding["""labels"""].shape      ,     (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
									self.assertTrue(encoding["""labels"""].min().item() >= 0						)
									self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5						)

									# Test not batched input (PIL images)
									a__     :		Optional[int]		 =						prepare_semantic_single_inputs()

									a__     :		str		 =						image_processing(__lowercase      ,     __lowercase      ,     return_tensors="""pt"""						)
									self.assertEqual(
									    encoding["""pixel_values"""].shape      ,     (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(
									    encoding["""labels"""].shape      ,     (
									        1,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
									self.assertTrue(encoding["""labels"""].min().item() >= 0						)
									self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5						)

									# Test batched input (PIL images)
									a__     :		List[Any]		 =						prepare_semantic_batch_inputs()

									a__     :		Union[str, Any]		 =						image_processing(__lowercase      ,     __lowercase      ,     return_tensors="""pt"""						)
									self.assertEqual(
									    encoding["""pixel_values"""].shape      ,     (
									        2,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(
									    encoding["""labels"""].shape      ,     (
									        2,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )      ,     )
									self.assertEqual(encoding["""labels"""].dtype      ,     torch.long						)
									self.assertTrue(encoding["""labels"""].min().item() >= 0						)
									self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5						)

						def 							SCREAMING_SNAKE_CASE__(     self						)      ->   str:
									"""simple docstring"""




									a__     :		Optional[int]		 =						self.image_processing_class(**self.image_processor_dict						)

									# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
									a__     :		int		 =						prepare_semantic_single_inputs()
									a__     :		List[str]		 =						image_processing(__lowercase      ,     __lowercase      ,     return_tensors="""pt"""						)
									self.assertTrue(encoding["""labels"""].min().item() >= 0						)
									self.assertTrue(encoding["""labels"""].max().item() <= 1_5_0						)

									a__     :		Optional[Any]		 =						True
									a__     :		Tuple		 =						image_processing(__lowercase      ,     __lowercase      ,     return_tensors="""pt"""						)
									self.assertTrue(encoding["""labels"""].min().item() >= 0						)
									self.assertTrue(encoding["""labels"""].max().item() <= 2_5_5						)









from collections.abc import Callable

_lowercase	:   int    	=Callable[[float], float]  # type alias for a real -> real function

def 			lowerCAmelCase_  (						_lowercase       :     RealFunc		,     _lowercase       :     RealFunc		,     _lowercase       :     int		,     )							-> float:







			"""simple docstring"""
			a__     :		Optional[int]		 =						float(_lowercase)
			while True:
						try:
									a__     :		Union[str, Any]		 =						prev_guess - function(_lowercase) / derivative(_lowercase)
						except ZeroDivisionError:
									raise ZeroDivisionError("""Could not find root""") from None
						if abs(prev_guess - next_guess) < 10**-5:
									return next_guess
						a__     :		Optional[Any]		 =						next_guess

def 			lowerCAmelCase_  (						_lowercase       :     float)							-> float:







			"""simple docstring"""
			return (x**3) - (2 * x) - 5

def 			lowerCAmelCase_  (						_lowercase       :     float)							-> float:







			"""simple docstring"""
			return 3 * (x**2) - 2


if __name__ == "__main__":
		print(newton(f, fa, 3))




