




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
 import PIL


_lowerCamelCase      :  List[str]      						=						logging.get_logger(__name__)
class        __snake_case  (UpperCAmelCase__		):
    lowerCAmelCase__				=     ["pixel_values"]



    def __init__(      self      :		Optional[Any]		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Dict[str, int] = None		,	_UpperCAmelCase      :		PILImageResampling = PILImageResampling.BICUBIC		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Union[int, float] = 1 / 255		,	_UpperCAmelCase      :		bool = True		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		bool = True		,	**_UpperCAmelCase      :		Optional[int]		,	)  ->    None:





        '''simple docstring'''




        super().__init__(**lowerCamelCase__      )
        _lowerCAmelCase    :    Optional[int]										=      size if size is not None else {"height": 384, "width": 384}
        _lowerCAmelCase    :    str										=      get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__      )

        _lowerCAmelCase    :    Dict										=      do_resize
        _lowerCAmelCase    :    List[str]										=      size
        _lowerCAmelCase    :    List[str]										=      resample
        _lowerCAmelCase    :    Optional[Any]										=      do_rescale
        _lowerCAmelCase    :    List[str]										=      rescale_factor
        _lowerCAmelCase    :    Optional[int]										=      do_normalize
        _lowerCAmelCase    :    Union[str, Any]										=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        _lowerCAmelCase    :    Tuple										=      image_std if image_std is not None else OPENAI_CLIP_STD
        _lowerCAmelCase    :    Tuple										=      do_convert_rgb



    def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Dict[str, int]		,	_UpperCAmelCase      :		PILImageResampling = PILImageResampling.BICUBIC		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		str		,	)  ->    np.ndarray:





        '''simple docstring'''




        _lowerCAmelCase    :    Optional[int]										=      get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__      )
        if "height" not in size or "width" not in size:
            raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"      )
        _lowerCAmelCase    :    Optional[Any]										=      (size["height"], size["width"])
        return resize(lowerCamelCase__		,	size=lowerCamelCase__		,	resample=lowerCamelCase__		,	data_format=lowerCamelCase__		,	**lowerCamelCase__      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[int, float]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		Tuple		,	)  ->    str:





        '''simple docstring'''




        return rescale(lowerCamelCase__		,	scale=lowerCamelCase__		,	data_format=lowerCamelCase__		,	**lowerCamelCase__      )



    def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		np.ndarray		,	_UpperCAmelCase      :		Union[float, List[float]]		,	_UpperCAmelCase      :		Union[float, List[float]]		,	_UpperCAmelCase      :		Optional[Union[str, ChannelDimension]] = None		,	**_UpperCAmelCase      :		List[str]		,	)  ->    np.ndarray:





        '''simple docstring'''




        return normalize(lowerCamelCase__		,	mean=lowerCamelCase__		,	std=lowerCamelCase__		,	data_format=lowerCamelCase__		,	**lowerCamelCase__      )



    def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]		,	_UpperCAmelCase      :		ImageInput		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Dict[str, int]] = None		,	_UpperCAmelCase      :		PILImageResampling = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[float] = None		,	_UpperCAmelCase      :		Optional[bool] = None		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		Optional[Union[float, List[float]]] = None		,	_UpperCAmelCase      :		Optional[Union[str, TensorType]] = None		,	_UpperCAmelCase      :		bool = None		,	_UpperCAmelCase      :		ChannelDimension = ChannelDimension.FIRST		,	**_UpperCAmelCase      :		Dict		,	)  ->    PIL.Image.Image:





        '''simple docstring'''




        _lowerCAmelCase    :    Any										=      do_resize if do_resize is not None else self.do_resize
        _lowerCAmelCase    :    Any										=      resample if resample is not None else self.resample
        _lowerCAmelCase    :    Any										=      do_rescale if do_rescale is not None else self.do_rescale
        _lowerCAmelCase    :    Tuple										=      rescale_factor if rescale_factor is not None else self.rescale_factor
        _lowerCAmelCase    :    List[Any]										=      do_normalize if do_normalize is not None else self.do_normalize
        _lowerCAmelCase    :    Union[str, Any]										=      image_mean if image_mean is not None else self.image_mean
        _lowerCAmelCase    :    Dict										=      image_std if image_std is not None else self.image_std
        _lowerCAmelCase    :    Optional[Any]										=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        _lowerCAmelCase    :    List[Any]										=      size if size is not None else self.size
        _lowerCAmelCase    :    Dict										=      get_size_dict(lowerCamelCase__		,	default_to_square=lowerCamelCase__      )

        _lowerCAmelCase    :    List[Any]										=      make_list_of_images(lowerCamelCase__      )

        if not valid_images(lowerCamelCase__      ):
            raise ValueError(
                """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                """torch.Tensor, tf.Tensor or jax.ndarray."""      )

        if do_resize and size is None or resample is None:
            raise ValueError("""Size and resample must be specified if do_resize is True."""      )

        if do_rescale and rescale_factor is None:
            raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

        if do_normalize and (image_mean is None or image_std is None):
            raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
            _lowerCAmelCase    :    List[Any]										=      [convert_to_rgb(lowerCamelCase__      ) for image in images]

        # All transformations expect numpy arrays.
        _lowerCAmelCase    :    Any										=      [to_numpy_array(lowerCamelCase__      ) for image in images]

        if do_resize:
            _lowerCAmelCase    :    Union[str, Any]										=      [self.resize(image=lowerCamelCase__		,	size=lowerCamelCase__		,	resample=lowerCamelCase__      ) for image in images]

        if do_rescale:
            _lowerCAmelCase    :    List[Any]										=      [self.rescale(image=lowerCamelCase__		,	scale=lowerCamelCase__      ) for image in images]

        if do_normalize:
            _lowerCAmelCase    :    Dict										=      [self.normalize(image=lowerCamelCase__		,	mean=lowerCamelCase__		,	std=lowerCamelCase__      ) for image in images]

        _lowerCAmelCase    :    List[str]										=      [to_channel_dimension_format(lowerCamelCase__		,	lowerCamelCase__      ) for image in images]

        _lowerCAmelCase    :    List[str]										=      BatchFeature(data={"""pixel_values""": images}		,	tensor_type=lowerCamelCase__      )

        return encoded_outputs







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


_lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase						, _lowerCamelCase      :  Optional[int]      						=						train_test_split(X, y, test_size=0.2, random_state=0)


_lowerCamelCase      :  Optional[Any]      						=						PolynomialFeatures(degree=4)
_lowerCamelCase      :  Optional[Any]      						=						poly_reg.fit_transform(X)
_lowerCamelCase      :  Dict      						=						LinearRegression()
pol_reg.fit(X_poly, y)




def 							_UpperCAmelCase  ():
				'''simple docstring'''
				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

