


import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

	if is_vision_available():
		from transformers import MaskaFormerImageProcessor

if is_vision_available():
	from PIL import Image






class     UpperCamelCase:


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2     ,    SCREAMING_SNAKE_CASE	:		int=True     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1_0     ,    SCREAMING_SNAKE_CASE	:		Dict=3     ,    SCREAMING_SNAKE_CASE	:		Any=3_2 * 8     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=3_2 * 8     ,    SCREAMING_SNAKE_CASE	:		Tuple=4     ,    SCREAMING_SNAKE_CASE	:		int=6_4     ,    )			->	Optional[Any]:




											'''simple docstring'''

											__snake_case				=		parent
											__snake_case				=		batch_size
											__snake_case				=		is_training
											__snake_case				=		use_auxiliary_loss
											__snake_case				=		num_queries
											__snake_case				=		num_channels
											__snake_case				=		min_size
											__snake_case				=		max_size
											__snake_case				=		num_labels
											__snake_case				=		hidden_dim
											__snake_case				=		hidden_dim


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	str:




											'''simple docstring'''

											__snake_case				=		floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]						).to(
											    SCREAMING_SNAKE_CASE						)

											__snake_case				=		torch.ones([self.batch_size, self.min_size, self.max_size]     ,    device=SCREAMING_SNAKE_CASE						)

											__snake_case				=		(
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]     ,    device=SCREAMING_SNAKE_CASE						) > 0.5
											).float()
											__snake_case				=		(torch.rand((self.batch_size, self.num_labels)     ,    device=SCREAMING_SNAKE_CASE						) > 0.5).long()

											__snake_case				=		self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels


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




											'''simple docstring'''

											__snake_case				=		MaskaFormerConfig(
											    hidden_size=self.hidden_dim     ,    )
											__snake_case				=		self.num_queries
											__snake_case				=		self.num_labels

											__snake_case				=		[1, 1, 1, 1]
											__snake_case				=		self.num_channels

											__snake_case				=		6_4
											__snake_case				=		1_2_8
											__snake_case				=		self.hidden_dim
											__snake_case				=		self.hidden_dim
											__snake_case				=		self.hidden_dim
											return config


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	Dict:




											'''simple docstring'''

											__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case				=		self.prepare_config_and_inputs()
											__snake_case				=		{"pixel_values": pixel_values, "pixel_mask": pixel_mask}
											return config, inputs_dict


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]						)			->	int:




											'''simple docstring'''

											__snake_case				=		output.encoder_hidden_states
											__snake_case				=		output.pixel_decoder_hidden_states
											__snake_case				=		output.transformer_decoder_hidden_states

											self.parent.assertTrue(len(SCREAMING_SNAKE_CASE						)     ,    len(config.backbone_config.depths						)						)
											self.parent.assertTrue(len(SCREAMING_SNAKE_CASE						)     ,    len(config.backbone_config.depths						)						)
											self.parent.assertTrue(len(SCREAMING_SNAKE_CASE						)     ,    config.decoder_layers						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=False						)			->	List[Any]:




											'''simple docstring'''

											with torch.no_grad():
																	__snake_case				=		MaskaFormerModel(config=SCREAMING_SNAKE_CASE						)
																	model.to(SCREAMING_SNAKE_CASE						)
																	model.eval()

																	__snake_case				=		model(pixel_values=SCREAMING_SNAKE_CASE     ,    pixel_mask=SCREAMING_SNAKE_CASE						)
																	__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    output_hidden_states=SCREAMING_SNAKE_CASE						)

											self.parent.assertEqual(
											    output.transformer_decoder_last_hidden_state.shape     ,    (self.batch_size, self.num_queries, self.hidden_dim)     ,    )
											# let's ensure the other two hidden state exists
											self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None						)
											self.parent.assertTrue(output.encoder_last_hidden_state is not None						)

											if output_hidden_states:
																	self.check_output_hidden_state(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Tuple     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		Optional[int]						)			->	int:




											'''simple docstring'''

											__snake_case				=		MaskaFormerForUniversalSegmentation(config=SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.eval()

											def comm_check_on_output(SCREAMING_SNAKE_CASE	:		Optional[Any]						):
																	# let's still check that all the required stuff is there
																	self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None						)
																	self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None						)
																	self.parent.assertTrue(result.encoder_last_hidden_state is not None						)
																	# okay, now we need to check the logits shape
																	# due to the encoder compression, masks have a //4 spatial size
																	self.parent.assertEqual(
																	    result.masks_queries_logits.shape     ,    (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)     ,    )
																	# + 1 for null class
																	self.parent.assertEqual(
																	    result.class_queries_logits.shape     ,    (self.batch_size, self.num_queries, self.num_labels + 1)						)

											with torch.no_grad():
																	__snake_case				=		model(pixel_values=SCREAMING_SNAKE_CASE     ,    pixel_mask=SCREAMING_SNAKE_CASE						)
																	__snake_case				=		model(SCREAMING_SNAKE_CASE						)

																	comm_check_on_output(SCREAMING_SNAKE_CASE						)

																	__snake_case				=		model(
																	    pixel_values=SCREAMING_SNAKE_CASE     ,    pixel_mask=SCREAMING_SNAKE_CASE     ,    mask_labels=SCREAMING_SNAKE_CASE     ,    class_labels=SCREAMING_SNAKE_CASE						)

											comm_check_on_output(SCREAMING_SNAKE_CASE						)

											self.parent.assertTrue(result.loss is not None						)
											self.parent.assertEqual(result.loss.shape     ,    torch.Size([1]						)						)






@require_torch
class     UpperCamelCase(  _a     ,							_a     ,							unittest.TestCase   ):
					snake_case_		:				Tuple										=	(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
					snake_case_		:				Optional[Any]										=	{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

					snake_case_		:				List[str]										=	False
					snake_case_		:				Union[str, Any]										=	False
					snake_case_		:				Dict										=	False
					snake_case_		:				Optional[Any]										=	False


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	List[Any]:




											'''simple docstring'''

											__snake_case				=		MaskaFormerModelTester(self						)
											__snake_case				=		ConfigTester(self     ,    config_class=SCREAMING_SNAKE_CASE     ,    has_text_modality=SCREAMING_SNAKE_CASE						)


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




											'''simple docstring'''

											self.config_tester.run_common_tests()


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[str]:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    output_hidden_states=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	Union[str, Any]:




											'''simple docstring'''

											__snake_case				=		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*SCREAMING_SNAKE_CASE						)


					@unittest.skip(reason="Mask2Former does not use inputs_embeds"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[int]						)			->	List[Any]:




											'''simple docstring'''

											pass


					@unittest.skip(reason="Mask2Former does not have a get_input_embeddings method"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	List[str]:




											'''simple docstring'''

											pass


					@unittest.skip(reason="Mask2Former is not a generative model"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	List[Any]:




											'''simple docstring'''

											pass


					@unittest.skip(reason="Mask2Former does not use token embeddings"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Union[str, Any]:




											'''simple docstring'''

											pass


					@require_torch_multi_gpu
					@unittest.skip(
					    reason="Mask2Former has some layers using `add_module` which doesn't work well with `nn.DataParallel`"						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Optional[Any]						)			->	int:




											'''simple docstring'''

											pass


					@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Dict:




											'''simple docstring'''

											pass


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




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	__snake_case				=		model_class(SCREAMING_SNAKE_CASE						)
																	__snake_case				=		inspect.signature(model.forward						)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	__snake_case				=		[*signature.parameters.keys()]

																	__snake_case				=		["pixel_values"]
																	self.assertListEqual(arg_names[:1]     ,    SCREAMING_SNAKE_CASE						)


					@slow
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	int:




											'''simple docstring'''

											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																	__snake_case				=		MaskaFormerModel.from_pretrained(SCREAMING_SNAKE_CASE						)
																	self.assertIsNotNone(SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	int:




											'''simple docstring'''

											__snake_case				=		(self.model_tester.min_size,) * 2
											__snake_case				=		{
											    "pixel_values": torch.randn((2, 3, *size)     ,    device=SCREAMING_SNAKE_CASE						),
											    "mask_labels": torch.randn((2, 1_0, *size)     ,    device=SCREAMING_SNAKE_CASE						),
											    "class_labels": torch.zeros(2     ,    1_0     ,    device=SCREAMING_SNAKE_CASE						).long(),
											}
											__snake_case				=		self.model_tester.get_config()

											__snake_case				=		MaskaFormerForUniversalSegmentation(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		model(**SCREAMING_SNAKE_CASE						)
											self.assertTrue(outputs.loss is not None						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str						)			->	Any:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(SCREAMING_SNAKE_CASE     ,    **SCREAMING_SNAKE_CASE     ,    output_hidden_states=SCREAMING_SNAKE_CASE						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]						)			->	Any:




											'''simple docstring'''

											__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																	__snake_case				=		model_class(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE     ,    output_attentions=SCREAMING_SNAKE_CASE						)
																	self.assertTrue(outputs.attentions is not None						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	Tuple:




											'''simple docstring'''

											if not self.model_tester.is_training:
																	return

											__snake_case				=		self.all_model_classes[1]
											__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs()

											__snake_case				=		model_class(SCREAMING_SNAKE_CASE						)
											model.to(SCREAMING_SNAKE_CASE						)
											model.train()

											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    mask_labels=SCREAMING_SNAKE_CASE     ,    class_labels=SCREAMING_SNAKE_CASE						).loss
											loss.backward()






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		self.all_model_classes[1]
											__snake_case		,					__snake_case		,					__snake_case		,					__snake_case		,					__snake_case				=		self.model_tester.prepare_config_and_inputs()
											__snake_case				=		True
											__snake_case				=		True

											__snake_case				=		model_class(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
											model.train()

											__snake_case				=		model(SCREAMING_SNAKE_CASE     ,    mask_labels=SCREAMING_SNAKE_CASE     ,    class_labels=SCREAMING_SNAKE_CASE						)

											__snake_case				=		outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											__snake_case				=		outputs.pixel_decoder_hidden_states[0]
											pixel_decoder_hidden_states.retain_grad()

											__snake_case				=		outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											__snake_case				=		outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=SCREAMING_SNAKE_CASE						)

											self.assertIsNotNone(encoder_hidden_states.grad						)
											self.assertIsNotNone(pixel_decoder_hidden_states.grad						)
											self.assertIsNotNone(transformer_decoder_hidden_states.grad						)
											self.assertIsNotNone(attentions.grad						)




A							:					Dict     						=     1E-4





def 	_lowerCAmelCase       (    )						->	List[str]:


						'''simple docstring'''






						__snake_case				=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
						return image






@require_vision
@slow
class     UpperCamelCase(  unittest.TestCase   ):


					@cached_property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Any						)			->	List[Any]:




											'''simple docstring'''

											return "facebook/mask2former-swin-small-coco-instance"


					@cached_property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		int						)			->	Optional[Any]:




											'''simple docstring'''

											return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints						) if is_vision_available() else None


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




											'''simple docstring'''

											__snake_case				=		MaskaFormerModel.from_pretrained(self.model_checkpoints						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		self.default_image_processor
											__snake_case				=		prepare_img()
											__snake_case				=		image_processor(SCREAMING_SNAKE_CASE     ,    return_tensors="pt"						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		inputs["pixel_values"].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0						)
											# check size
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    (1, 3, 3_8_4, 3_8_4)						)

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		torch.tensor(
											    [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]						).to(SCREAMING_SNAKE_CASE						)
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3]     ,    SCREAMING_SNAKE_CASE     ,    atol=SCREAMING_SNAKE_CASE						)						)

											__snake_case				=		torch.tensor(
											    [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]						).to(SCREAMING_SNAKE_CASE						)
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]     ,    SCREAMING_SNAKE_CASE     ,    atol=SCREAMING_SNAKE_CASE						)						)

											__snake_case				=		torch.tensor(
											    [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]						).to(SCREAMING_SNAKE_CASE						)
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3]     ,    SCREAMING_SNAKE_CASE     ,    atol=SCREAMING_SNAKE_CASE						)						)


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Dict:




											'''simple docstring'''

											__snake_case				=		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(SCREAMING_SNAKE_CASE						).eval()
											__snake_case				=		self.default_image_processor
											__snake_case				=		prepare_img()
											__snake_case				=		image_processor(SCREAMING_SNAKE_CASE     ,    return_tensors="pt"						).to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		inputs["pixel_values"].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0						)
											# check size
											self.assertEqual(SCREAMING_SNAKE_CASE     ,    (1, 3, 3_8_4, 3_8_4)						)

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE						)
											# masks_queries_logits
											__snake_case				=		outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape     ,    (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)						)
											__snake_case				=		[
											    [-8.7839, -9.0056, -8.8121],
											    [-7.4104, -7.0313, -6.5401],
											    [-6.6105, -6.3427, -6.4675],
											]
											__snake_case				=		torch.tensor(SCREAMING_SNAKE_CASE						).to(SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]     ,    SCREAMING_SNAKE_CASE     ,    atol=SCREAMING_SNAKE_CASE						)						)
											# class_queries_logits
											__snake_case				=		outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape     ,    (1, model.config.num_queries, model.config.num_labels + 1)						)
											__snake_case				=		torch.tensor(
											    [
											        [1.8324, -8.0835, -4.1922],
											        [0.8450, -9.0050, -3.6053],
											        [0.3045, -7.7293, -3.0275],
											    ]						).to(SCREAMING_SNAKE_CASE						)
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]     ,    SCREAMING_SNAKE_CASE     ,    atol=SCREAMING_SNAKE_CASE						)						)






					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict						)			->	List[str]:




											'''simple docstring'''

											__snake_case				=		MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints						).to(SCREAMING_SNAKE_CASE						).eval()
											__snake_case				=		self.default_image_processor

											__snake_case				=		image_processor(
											    [np.zeros((3, 8_0_0, 1_3_3_3)						), np.zeros((3, 8_0_0, 1_3_3_3)						)]     ,    segmentation_maps=[np.zeros((3_8_4, 3_8_4)						).astype(np.floataa						), np.zeros((3_8_4, 3_8_4)						).astype(np.floataa						)]     ,    return_tensors="pt"     ,    )

											__snake_case				=		inputs["pixel_values"].to(SCREAMING_SNAKE_CASE						)
											__snake_case				=		[el.to(SCREAMING_SNAKE_CASE						) for el in inputs["mask_labels"]]
											__snake_case				=		[el.to(SCREAMING_SNAKE_CASE						) for el in inputs["class_labels"]]

											with torch.no_grad():
																	__snake_case				=		model(**SCREAMING_SNAKE_CASE						)

											self.assertTrue(outputs.loss is not None						)









from __future__ import annotations

from decimal import Decimal
from math import *  # noqa: F403

from sympy import diff





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase = 10**-10 )						->	float:


						'''simple docstring'''






						__snake_case				=		a
						while True:
												__snake_case				=		Decimal(_lowerCAmelCase ) - (
												    Decimal(eval(_lowerCAmelCase ) ) / Decimal(eval(str(diff(_lowerCAmelCase ) ) ) )  # noqa: S307
												)
												# This number dictates the accuracy of the answer
												if abs(eval(_lowerCAmelCase ) ) < precision:  # noqa: S307
																		return float(_lowerCAmelCase )


# 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
	print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
	# Find Square Root of 5
	print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
	# Exponential Roots
	print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')





