
'''simple docstring'''
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.linear_model import LinearRegression

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures

# Importing the dataset
__lowerCAmelCase												=	pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__lowerCAmelCase												=	dataset.iloc[:, 1:2].values
__lowerCAmelCase												=	dataset.iloc[:, 2].values


__lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase							,    __lowerCAmelCase												=	train_test_split(X, y, test_size=0.2, random_state=0)


__lowerCAmelCase												=	PolynomialFeatures(degree=4)
__lowerCAmelCase												=	poly_reg.fit_transform(X)
__lowerCAmelCase												=	LinearRegression()
pol_reg.fit(X_poly, y)






def  __SCREAMING_SNAKE_CASE				(  ):
							plt.scatter(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, color="""red"""				)
							plt.plot(_SCREAMING_SNAKE_CASE							, pol_reg.predict(poly_reg.fit_transform(_SCREAMING_SNAKE_CASE				)				)							, color="""blue"""				)
							plt.title("""Truth or Bluff (Linear Regression)"""				)
							plt.xlabel("""Position level"""				)
							plt.ylabel("""Salary"""				)
							plt.show()


if __name__ == "__main__":
				viz_polymonial()

				# Predicting a new result with Polymonial Regression
				pol_reg.predict(poly_reg.fit_transform([[5.5]]))
				# output should be 132148.43750003

'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase												=	{
    'configuration_efficientformer': [
        'EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientFormerConfig',
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	['EfficientFormerImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'EfficientFormerForImageClassification',
				    'EfficientFormerForImageClassificationWithTeacher',
				    'EfficientFormerModel',
				    'EfficientFormerPreTrainedModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__lowerCAmelCase												=	[
				    'TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'TFEfficientFormerForImageClassification',
				    'TFEfficientFormerForImageClassificationWithTeacher',
				    'TFEfficientFormerModel',
				    'TFEfficientFormerPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_efficientformer import EfficientFormerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_efficientformer import (
								    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EfficientFormerForImageClassification,
								    EfficientFormerForImageClassificationWithTeacher,
								    EfficientFormerModel,
								    EfficientFormerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_efficientformer import (
								    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEfficientFormerForImageClassification,
								    TFEfficientFormerForImageClassificationWithTeacher,
								    TFEfficientFormerModel,
								    TFEfficientFormerPreTrainedModel,
								)

else:
				import sys

				__lowerCAmelCase												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)