


"""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
__SCREAMING_SNAKE_CASE							:    Optional[int]		=    pd.read_csv(
    'https://s3.us-west-2.amazonaws.com/public.gamelab.fun/dataset/'
    'position_salaries.csv'
)
__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    dataset.iloc[:, 1:2].values
__SCREAMING_SNAKE_CASE							:    str		=    dataset.iloc[:, 2].values


__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE		,			__SCREAMING_SNAKE_CASE							:    Tuple		=    train_test_split(X, y, test_size=0.2, random_state=0)


__SCREAMING_SNAKE_CASE							:    List[str]		=    PolynomialFeatures(degree=4)
__SCREAMING_SNAKE_CASE							:    Tuple		=    poly_reg.fit_transform(X)
__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    LinearRegression()
pol_reg.fit(X_poly, y)







def 						_a				(      )    ->				List[Any]:
							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_torch_available


__SCREAMING_SNAKE_CASE							:    Optional[Any]		=    {
    'configuration_megatron_bert': ['MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegatronBertConfig'],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__SCREAMING_SNAKE_CASE							:    Tuple		=    [
				    'MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'MegatronBertForCausalLM',
				    'MegatronBertForMaskedLM',
				    'MegatronBertForMultipleChoice',
				    'MegatronBertForNextSentencePrediction',
				    'MegatronBertForPreTraining',
				    'MegatronBertForQuestionAnswering',
				    'MegatronBertForSequenceClassification',
				    'MegatronBertForTokenClassification',
				    'MegatronBertModel',
				    'MegatronBertPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_megatron_bert import (
								    MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    MegatronBertForCausalLM,
								    MegatronBertForMaskedLM,
								    MegatronBertForMultipleChoice,
								    MegatronBertForNextSentencePrediction,
								    MegatronBertForPreTraining,
								    MegatronBertForQuestionAnswering,
								    MegatronBertForSequenceClassification,
								    MegatronBertForTokenClassification,
								    MegatronBertModel,
								    MegatronBertPreTrainedModel,
								)

else:
				import sys

				__SCREAMING_SNAKE_CASE							:    int		=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



