"""simple docstring"""







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 (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    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


_UpperCamelCase					      =  logging.get_logger(__name__)


if is_vision_available():
  import PIL







class  lowerCamelCase__ (	_lowerCAmelCase			):
    SCREAMING_SNAKE_CASE		=      ["pixel_values"]






    def __init__(			self     ,A = True     ,A = None     ,A = PILImageResampling.BICUBIC     ,A = True     ,A = None     ,A = True     ,A = 1 / 255     ,A = True     ,A = None     ,A = None     ,A = True     ,**A     ,):
          super().__init__(**_lowercase )
          UpperCAmelCase          =	size if size is not None else {"""shortest_edge""": 224}
          UpperCAmelCase          =	get_size_dict(_lowercase     ,default_to_square=_lowercase )
          UpperCAmelCase          =	crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
          UpperCAmelCase          =	get_size_dict(_lowercase     ,default_to_square=_lowercase     ,param_name="""crop_size""" )

          UpperCAmelCase          =	do_resize
          UpperCAmelCase          =	size
          UpperCAmelCase          =	resample
          UpperCAmelCase          =	do_center_crop
          UpperCAmelCase          =	crop_size
          UpperCAmelCase          =	do_rescale
          UpperCAmelCase          =	rescale_factor
          UpperCAmelCase          =	do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          UpperCAmelCase          =	image_std if image_std is not None else OPENAI_CLIP_STD
          UpperCAmelCase          =	do_convert_rgb






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = PILImageResampling.BICUBIC     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(_lowercase     ,default_to_square=_lowercase )
          if "shortest_edge" not in size:
                raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
          UpperCAmelCase          =	get_resize_output_image_size(_lowercase     ,size=size["""shortest_edge"""]     ,default_to_square=_lowercase )
          return resize(_lowercase     ,size=_lowercase     ,resample=_lowercase     ,data_format=_lowercase     ,**_lowercase )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A     ,):
          UpperCAmelCase          =	get_size_dict(_lowercase )
          if "height" not in size or "width" not in size:
                raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}''' )
          return center_crop(_lowercase     ,size=(size["""height"""], size["""width"""])     ,data_format=_lowercase     ,**_lowercase )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A = None     ,**A     ,):
          return rescale(_lowercase     ,scale=_lowercase     ,data_format=_lowercase     ,**_lowercase )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A     ,A = None     ,**A     ,):
          return normalize(_lowercase     ,mean=_lowercase     ,std=_lowercase     ,data_format=_lowercase     ,**_lowercase )






    def     _UpperCamelCase							(			self     ,A     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = None     ,A = ChannelDimension.FIRST     ,**A     ,):
          UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
          UpperCAmelCase          =	size if size is not None else self.size
          UpperCAmelCase          =	get_size_dict(_lowercase     ,param_name="""size"""     ,default_to_square=_lowercase )
          UpperCAmelCase          =	resample if resample is not None else self.resample
          UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
          UpperCAmelCase          =	get_size_dict(_lowercase     ,param_name="""crop_size"""     ,default_to_square=_lowercase )
          UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
          UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
          UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
          UpperCAmelCase          =	image_std if image_std is not None else self.image_std
          UpperCAmelCase          =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          UpperCAmelCase          =	make_list_of_images(_lowercase )

          if not valid_images(_lowercase ):
                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:
                raise ValueError("""Size must be specified if do_resize is True.""" )

          if do_center_crop and crop_size is None:
                raise ValueError("""Crop size must be specified if do_center_crop 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:
                UpperCAmelCase          =	[convert_to_rgb(_lowercase ) for image in images]

          # All transformations expect numpy arrays.
          UpperCAmelCase          =	[to_numpy_array(_lowercase ) for image in images]

          if do_resize:
                UpperCAmelCase          =	[self.resize(image=_lowercase     ,size=_lowercase     ,resample=_lowercase ) for image in images]

          if do_center_crop:
                UpperCAmelCase          =	[self.center_crop(image=_lowercase     ,size=_lowercase ) for image in images]

          if do_rescale:
                UpperCAmelCase          =	[self.rescale(image=_lowercase     ,scale=_lowercase ) for image in images]

          if do_normalize:
                UpperCAmelCase          =	[self.normalize(image=_lowercase     ,mean=_lowercase     ,std=_lowercase ) for image in images]

          UpperCAmelCase          =	[to_channel_dimension_format(_lowercase     ,_lowercase ) for image in images]

          UpperCAmelCase          =	{"""pixel_values""": images}
          return BatchFeature(data=_lowercase     ,tensor_type=_lowercase )











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


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


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

def _a			(		):







      """simple docstring"""






      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



