



import unittest

import numpy as np

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 ViTImageProcessor

class 						lowerCamelCase_     (			unittest.TestCase    ):






						'''simple docstring'''







						def __init__(						self   :      Dict						,						__lowerCamelCase   :      Union[str, Any]						,						__lowerCamelCase   :      str=7						,						__lowerCamelCase   :      int=3						,						__lowerCamelCase   :      List[Any]=18						,						__lowerCamelCase   :      Optional[Any]=30						,						__lowerCamelCase   :      Optional[Any]=4_00						,						__lowerCamelCase   :      str=True						,						__lowerCamelCase   :      str=None						,						__lowerCamelCase   :      Dict=True						,						__lowerCamelCase   :      Union[str, Any]=[0.5, 0.5, 0.5]						,						__lowerCamelCase   :      Dict=[0.5, 0.5, 0.5]						,						)   ->					Optional[int]:
									A				:       int														=    size if size is not None else {"height": 18, "width": 18}
									A				:       int														=    parent
									A				:       Union[str, Any]														=    batch_size
									A				:       List[str]														=    num_channels
									A				:       Tuple														=    image_size
									A				:       int														=    min_resolution
									A				:       Optional[int]														=    max_resolution
									A				:       Any														=    do_resize
									A				:       int														=    size
									A				:       Optional[Any]														=    do_normalize
									A				:       Optional[Any]														=    image_mean
									A				:       Optional[int]														=    image_std







						def       SCREAMING_SNAKE_CASE__		(						self   :      Any			)   ->					Union[str, Any]:
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}







@require_torch
@require_vision
class 						lowerCamelCase_     (			_A    ,unittest.TestCase    ):






						'''simple docstring'''
						a__ =	ViTImageProcessor if is_vision_available() else None







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[Any]			)   ->					Tuple:
									A				:       Optional[int]														=    ViTImageProcessingTester(self			)







						@property
						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Union[str, Any]:
									return self.image_processor_tester.prepare_image_processor_dict()







						def       SCREAMING_SNAKE_CASE__		(						self   :      Optional[int]			)   ->					str:
									A				:       Tuple														=    self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"image_mean"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"image_std"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"do_normalize"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"do_resize"			)			)
									self.assertTrue(hasattr(__lowerCamelCase						,						"size"			)			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      int			)   ->					List[str]:
									A				:       List[Any]														=    self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size						,						{"height": 18, "width": 18}			)

									A				:       Any														=    self.image_processing_class.from_dict(self.image_processor_dict						,						size=42			)
									self.assertEqual(image_processor.size						,						{"height": 42, "width": 42}			)







						def       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[int]:
									pass







						def       SCREAMING_SNAKE_CASE__		(						self   :      Union[str, Any]			)   ->					List[str]:
									# Initialize image_processing
									A				:       Dict														=    self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									A				:       Tuple														=    prepare_image_inputs(self.image_processor_tester						,						equal_resolution=__lowerCamelCase			)
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase						,						Image.Image			)

									# Test not batched input
									A				:       str														=    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[Any]														=    image_processing(__lowerCamelCase						,						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   :      Union[str, Any]			)   ->					List[Any]:
									# Initialize image_processing
									A				:       List[Any]														=    self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									A				:       Optional[Any]														=    prepare_image_inputs(self.image_processor_tester						,						equal_resolution=__lowerCamelCase						,						numpify=__lowerCamelCase			)
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase						,						np.ndarray			)

									# Test not batched input
									A				:       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(__lowerCamelCase						,						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   :      List[str]			)   ->					List[Any]:
									# Initialize image_processing
									A				:       Any														=    self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									A				:       List[Any]														=    prepare_image_inputs(self.image_processor_tester						,						equal_resolution=__lowerCamelCase						,						torchify=__lowerCamelCase			)
									for image in image_inputs:
												self.assertIsInstance(__lowerCamelCase						,						torch.Tensor			)

									# 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				:       int														=    image_processing(__lowerCamelCase						,						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"],
									    )						,						)




from collections.abc import Callable

__SCREAMING_SNAKE_CASE     =	Callable[[float], float]  # type alias for a real -> real function




def 						UpperCAmelCase (    _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     ):
			A				:       int														=    float(_lowerCamelCase     )
			while True:
						try:
									A				:       Optional[int]														=    prev_guess - function(_lowerCamelCase     ) / derivative(_lowerCamelCase     )
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"     ) from None
						if abs(prev_guess - next_guess     ) < 10**-5:
									return next_guess
						A				:       Any														=    next_guess




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return (x**3) - (2 * x) - 5




def 						UpperCAmelCase (    _lowerCamelCase     ):
			return 3 * (x**2) - 2


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