





import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


_A					:				Optional[int]				=			'__DUMMY_TRANSFORMERS_USER__'
_A					:				Optional[int]				=			'Dummy User'
_A					:				Dict				=			'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'

_A					:				Tuple				=			'https://hub-ci.huggingface.co'
_A					:				List[Any]				=			CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
_A					:				Tuple				=			CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
_A					:				int				=			Path("""~/.huggingface/hub_ci_token""").expanduser()


@pytest.fixture
def 							__snake_case							(    lowerCAmelCase_	)				->						Any:
    monkeypatch.setattr(
        '''huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE''' ,							__snake_case	)


@pytest.fixture
def 							__snake_case							(    lowerCAmelCase_	)				->						Tuple:
    monkeypatch.setattr('''datasets.config.HF_ENDPOINT''' ,							__snake_case	)
    monkeypatch.setattr('''datasets.config.HUB_DATASETS_URL''' ,							__snake_case	)


@pytest.fixture
def 							__snake_case							(    lowerCAmelCase_	)				->						str:
    monkeypatch.setattr('''huggingface_hub.hf_api.HfFolder.path_token''' ,							__snake_case	)


@pytest.fixture
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_	)				->						List[str]:
    HfFolder.save_token(__snake_case	)
    yield
    HfFolder.delete_token()


@pytest.fixture(scope='''session'''	)
def 							__snake_case							(    )				->						Tuple:
    return HfApi(endpoint=__snake_case	)


@pytest.fixture(scope='''session'''	)
def 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    SCREAMING_SNAKE_CASE__											=			HfFolder.get_token()
    HfFolder.save_token(__snake_case	)
    yield CI_HUB_USER_TOKEN
    if previous_token is not None:
        HfFolder.save_token(__snake_case	)


@pytest.fixture
def 							__snake_case							(    lowerCAmelCase_	)				->						List[Any]:
    def _cleanup_repo(lowerCAmelCase_	):
        hf_api.delete_repo(__snake_case ,							token=__snake_case ,							repo_type='''dataset'''	)

    return _cleanup_repo


@pytest.fixture
def 							__snake_case							(    lowerCAmelCase_	)				->						Union[str, Any]:
    @contextmanager
    def _temporary_repo(lowerCAmelCase_	):
        try:
            yield repo_id
        finally:
            cleanup_repo(__snake_case	)

    return _temporary_repo


@pytest.fixture(scope='''session'''	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Any:
    SCREAMING_SNAKE_CASE__											=			f'''repo_txt_data-{int(time.time() * 10e3	)}'''
    SCREAMING_SNAKE_CASE__											=			f'''{CI_HUB_USER}/{repo_name}'''
    hf_api.create_repo(__snake_case ,							token=__snake_case ,							repo_type='''dataset''' ,							private=__snake_case	)
    hf_api.upload_file(
        token=__snake_case ,							path_or_fileobj=str(__snake_case	) ,							path_in_repo='''data/text_data.txt''' ,							repo_id=__snake_case ,							repo_type='''dataset''' ,							)
    yield repo_id
    try:
        hf_api.delete_repo(__snake_case ,							token=__snake_case ,							repo_type='''dataset'''	)
    except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
        pass


@pytest.fixture()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    return hf_private_dataset_repo_txt_data_


@pytest.fixture(scope='''session'''	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			f'''repo_zipped_txt_data-{int(time.time() * 10e3	)}'''
    SCREAMING_SNAKE_CASE__											=			f'''{CI_HUB_USER}/{repo_name}'''
    hf_api.create_repo(__snake_case ,							token=__snake_case ,							repo_type='''dataset''' ,							private=__snake_case	)
    hf_api.upload_file(
        token=__snake_case ,							path_or_fileobj=str(__snake_case	) ,							path_in_repo='''data.zip''' ,							repo_id=__snake_case ,							repo_type='''dataset''' ,							)
    yield repo_id
    try:
        hf_api.delete_repo(__snake_case ,							token=__snake_case ,							repo_type='''dataset'''	)
    except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
        pass


@pytest.fixture()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    return hf_private_dataset_repo_zipped_txt_data_


@pytest.fixture(scope='''session'''	)
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Optional[Any]:
    SCREAMING_SNAKE_CASE__											=			f'''repo_zipped_img_data-{int(time.time() * 10e3	)}'''
    SCREAMING_SNAKE_CASE__											=			f'''{CI_HUB_USER}/{repo_name}'''
    hf_api.create_repo(__snake_case ,							token=__snake_case ,							repo_type='''dataset''' ,							private=__snake_case	)
    hf_api.upload_file(
        token=__snake_case ,							path_or_fileobj=str(__snake_case	) ,							path_in_repo='''data.zip''' ,							repo_id=__snake_case ,							repo_type='''dataset''' ,							)
    yield repo_id
    try:
        hf_api.delete_repo(__snake_case ,							token=__snake_case ,							repo_type='''dataset'''	)
    except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
        pass



@pytest.fixture()
def 							__snake_case							(    lowerCAmelCase_ ,							lowerCAmelCase_ ,							lowerCAmelCase_	)				->						Dict:
    return hf_private_dataset_repo_zipped_img_data_





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


__snake_case ,						__snake_case ,						__snake_case ,						__snake_case				:  int =       train_test_split(X, y, test_size=0.2, random_state=0)


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


def      _lowercase							(    )		->  List[Any]:
			plt.scatter(__snake_case   ,__snake_case   ,color="red"      )
			plt.plot(__snake_case   ,pol_reg.predict(poly_reg.fit_transform(__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