



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 PoolFormerImageProcessor







class     __lowerCAmelCase					(    unittest.TestCase		):



			def __init__(		self     :						List[str]				,	A     :						str				,	A     :						Dict=7				,	A     :						List[Any]=3				,	A     :						Any=30				,	A     :						Tuple=4_00				,	A     :						Optional[Any]=True				,	A     :						Optional[int]=None				,	A     :						str=0.9				,	A     :						Optional[Any]=None				,	A     :						Optional[Any]=True				,	A     :						Optional[int]=[0.5, 0.5, 0.5]				,	A     :						Tuple=[0.5, 0.5, 0.5]				,	)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     size if size is not None else {'shortest_edge': 30}
						_UpperCAmelCase	 =     crop_size if crop_size is not None else {'height': 30, 'width': 30}
						_UpperCAmelCase	 =     parent
						_UpperCAmelCase	 =     batch_size
						_UpperCAmelCase	 =     num_channels
						_UpperCAmelCase	 =     min_resolution
						_UpperCAmelCase	 =     max_resolution
						_UpperCAmelCase	 =     do_resize_and_center_crop
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     crop_pct
						_UpperCAmelCase	 =     crop_size
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     image_mean
						_UpperCAmelCase	 =     image_std



			def 					_lowerCamelCase (		self     :						str)     ->					Tuple:



						"""simple docstring"""






						return {
						    "size": self.size,
						    "do_resize_and_center_crop": self.do_resize_and_center_crop,
						    "crop_pct": self.crop_pct,
						    "crop_size": self.crop_size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						}







@require_torch
@require_vision
class     __lowerCAmelCase					(    __snake_case       ,		unittest.TestCase		):
			UpperCamelCase           =  PoolFormerImageProcessor if is_vision_available() else None



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     PoolFormerImageProcessingTester(self)



			@property
			def 					_lowerCamelCase (		self     :						Tuple)     ->					str:



						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



			def 					_lowerCamelCase (		self     :						Dict)     ->					Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						self.assertTrue(hasattr(__UpperCamelCase				,	'do_resize_and_center_crop'))
						self.assertTrue(hasattr(__UpperCamelCase				,	'size'))
						self.assertTrue(hasattr(__UpperCamelCase				,	'crop_pct'))
						self.assertTrue(hasattr(__UpperCamelCase				,	'do_normalize'))
						self.assertTrue(hasattr(__UpperCamelCase				,	'image_mean'))
						self.assertTrue(hasattr(__UpperCamelCase				,	'image_std'))



			def 					_lowerCamelCase (		self     :						Any)     ->					Any:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict)
						self.assertEqual(image_processor.size				,	{'shortest_edge': 30})
						self.assertEqual(image_processor.crop_size				,	{'height': 30, 'width': 30})

						_UpperCAmelCase	 =     self.image_processing_class.from_dict(self.image_processor_dict				,	size=42				,	crop_size=84)
						self.assertEqual(image_processor.size				,	{'shortest_edge': 42})
						self.assertEqual(image_processor.crop_size				,	{'height': 84, 'width': 84})



			def 					_lowerCamelCase (		self     :						Union[str, Any])     ->					List[str]:



						"""simple docstring"""






						pass



			def 					_lowerCamelCase (		self     :						Optional[Any])     ->					Tuple:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PIL images
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=__UpperCamelCase)
						for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase				,	Image.Image)

						# Test not batched input
						_UpperCAmelCase	 =     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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(__UpperCamelCase				,	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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )				,	)



			def 					_lowerCamelCase (		self     :						Any)     ->					int:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random numpy tensors
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=__UpperCamelCase				,	numpify=__UpperCamelCase)
						for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase				,	np.ndarray)

						# Test not batched input
						_UpperCAmelCase	 =     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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(__UpperCamelCase				,	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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )				,	)



			def 					_lowerCamelCase (		self     :						Tuple)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     self.image_processing_class(**self.image_processor_dict)
						# create random PyTorch tensors
						_UpperCAmelCase	 =     prepare_image_inputs(self.image_processor_tester				,	equal_resolution=__UpperCamelCase				,	torchify=__UpperCamelCase)
						for image in image_inputs:
									self.assertIsInstance(__UpperCamelCase				,	torch.Tensor)

						# Test not batched input
						_UpperCAmelCase	 =     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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )				,	)

						# Test batched
						_UpperCAmelCase	 =     image_processing(__UpperCamelCase				,	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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )				,	)





from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403





def 							A			(  _UpperCAmelCase       :     str							,     _UpperCAmelCase       :     complex							,     _UpperCAmelCase       :     str = "x"							,     _UpperCAmelCase       :     float = 10**-10							,     _UpperCAmelCase       :     int = 1							,     )       ->				complex:







			'''simple docstring'''
			_UpperCAmelCase	 =     symbols(_UpperCAmelCase					)
			_UpperCAmelCase	 =     lambdify(_UpperCAmelCase							,     _UpperCAmelCase					)
			_UpperCAmelCase	 =     lambdify(_UpperCAmelCase							,     diff(_UpperCAmelCase							,     _UpperCAmelCase					)					)

			_UpperCAmelCase	 =     starting_point

			while True:
						if diff_function(_UpperCAmelCase					) != 0:
									_UpperCAmelCase	 =     prev_guess - multiplicity * func(_UpperCAmelCase					) / diff_function(
									    _UpperCAmelCase					)
						else:
									raise ZeroDivisionError('Could not find root'					) from None

						# Precision is checked by comparing the difference of consecutive guesses
						if abs(next_guess - prev_guess					) < precision:
									return next_guess

						_UpperCAmelCase	 =     next_guess


# Let's Execute
if __name__ == "__main__":
							# Find root of trigonometric function
							# Find value of pi
							print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

							# Find root of polynomial
							# Find fourth Root of 5
							print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

							# Find value of e
							print(
							    "The root of log(y) - 1 = 0 is ",
							    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
							)

							# Exponential Roots
							print(
							    "The root of exp(x) - 1 = 0 is",
							    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
							)

							# Find root of cos(x)
							print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")
