


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


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


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


def 			UpperCAmelCase_				(      ):
					plt.scatter(UpperCAmelCase__						,							UpperCAmelCase__						,							color="""red"""  )
					plt.plot(UpperCAmelCase__						,							pol_reg.predict(poly_reg.fit_transform(UpperCAmelCase__  )  )						,							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






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


a					  =     {
    'configuration_trocr': ['TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TrOCRConfig'],
    'processing_trocr': ['TrOCRProcessor'],
}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a					  =     [
							    'TROCR_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TrOCRForCausalLM',
							    'TrOCRPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
							from .processing_trocr import TrOCRProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
							import sys

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


