



"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_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


UpperCAmelCase						:		str			= logging.get_logger(__name__)


class    SCREAMING_SNAKE_CASE__ (							__UpperCAmelCase   ):
				lowercase__      =   ["pixel_values"]

				def __init__(				self			:				int ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Dict[str, int] = None ,  lowerCAmelCase_			:				float = None ,  lowerCAmelCase_			:				PILImageResampling = PILImageResampling.BILINEAR ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Union[int, float] = 1 / 2_5_5 ,  lowerCAmelCase_			:				bool = True ,  lowerCAmelCase_			:				Optional[Union[float, List[float]]] = None ,  lowerCAmelCase_			:				Optional[Union[float, List[float]]] = None ,  **lowerCAmelCase_			:				List[Any] ,  ):







						"""simple docstring"""







						super().__init__(**lowerCAmelCase_)
						lowercase_								= size if size is not None else {"""shortest_edge""": 3_8_4}
						lowercase_								= get_size_dict(lowerCAmelCase_ ,  default_to_square=lowerCAmelCase_)

						lowercase_								= do_resize
						lowercase_								= size
						# Default value set here for backwards compatibility where the value in config is None
						lowercase_								= crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
						lowercase_								= resample
						lowercase_								= do_rescale
						lowercase_								= rescale_factor
						lowercase_								= do_normalize
						lowercase_								= image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowercase_								= image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 					_UpperCAmelCase	(				self			:				Union[str, Any] ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Dict[str, int] ,  lowerCAmelCase_			:				float ,  lowerCAmelCase_			:				PILImageResampling = PILImageResampling.BICUBIC ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:				Optional[int] ,  ):







						"""simple docstring"""







						lowercase_								= get_size_dict(lowerCAmelCase_ ,  default_to_square=lowerCAmelCase_)
						if "shortest_edge" not in size:
								raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}''')
						lowercase_								= size["""shortest_edge"""]

						if shortest_edge < 3_8_4:
								# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
								lowercase_								= int(shortest_edge / crop_pct)
								lowercase_								= get_resize_output_image_size(lowerCAmelCase_ ,  size=lowerCAmelCase_ ,  default_to_square=lowerCAmelCase_)
								lowercase_								= resize(image=lowerCAmelCase_ ,  size=lowerCAmelCase_ ,  resample=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_)
								# then crop to (shortest_edge, shortest_edge)
								return center_crop(image=lowerCAmelCase_ ,  size=(shortest_edge, shortest_edge) ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_)
						else:
								# warping (no cropping) when evaluated at 384 or larger
								return resize(
								    lowerCAmelCase_ ,  size=(shortest_edge, shortest_edge) ,  resample=lowerCAmelCase_ ,  data_format=lowerCAmelCase_ ,  **lowerCAmelCase_)

				def 					_UpperCAmelCase	(				self			:				List[Any] ,  lowerCAmelCase_			:				np.ndarray ,  lowerCAmelCase_			:				Union[int, float] ,  lowerCAmelCase_			:				Optional[Union[str, ChannelDimension]] = None ,  **lowerCAmelCase_			:				Dict ,  ):







						"""simple docstring"""







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

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







						"""simple docstring"""







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




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







						"""simple docstring"""







						lowercase_								= do_resize if do_resize is not None else self.do_resize
						lowercase_								= crop_pct if crop_pct is not None else self.crop_pct
						lowercase_								= resample if resample is not None else self.resample
						lowercase_								= do_rescale if do_rescale is not None else self.do_rescale
						lowercase_								= rescale_factor if rescale_factor is not None else self.rescale_factor
						lowercase_								= do_normalize if do_normalize is not None else self.do_normalize
						lowercase_								= image_mean if image_mean is not None else self.image_mean
						lowercase_								= image_std if image_std is not None else self.image_std

						lowercase_								= size if size is not None else self.size
						lowercase_								= get_size_dict(lowerCAmelCase_ ,  default_to_square=lowerCAmelCase_)

						lowercase_								= 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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
								raise ValueError("""crop_pct must be specified if size < 384.""")

						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.""")

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

						if do_resize:
								lowercase_								= [self.resize(image=lowerCAmelCase_ ,  size=lowerCAmelCase_ ,  crop_pct=lowerCAmelCase_ ,  resample=lowerCAmelCase_) for image in images]

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

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

						lowercase_								= [to_channel_dimension_format(lowerCAmelCase_ ,  lowerCAmelCase_) for image in images]

						lowercase_								= {"""pixel_values""": images}
						return BatchFeature(data=lowerCAmelCase_ ,  tensor_type=lowerCAmelCase_)






"""simple docstring"""
import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple:







  '''simple docstring'''


  return (data["data"], data["target"])


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							) ->						np.ndarray:







  '''simple docstring'''


  lowercase_								= XGBRegressor(verbosity=0 ,  random_state=42							)
  xgb.fit(__lowerCAmelCase ,  __lowerCAmelCase							)
  # Predict target for test data
  lowercase_								= xgb.predict(__lowerCAmelCase							)
  lowercase_								= predictions.reshape(len(__lowerCAmelCase							) ,  1							)
  return predictions


def 			_SCREAMING_SNAKE_CASE						() ->						None:







  '''simple docstring'''


  lowercase_								= fetch_california_housing()
  lowercase_					,    lowercase_								= data_handling(__lowerCAmelCase							)
  lowercase_					,    lowercase_					,    lowercase_					,    lowercase_								= train_test_split(
      __lowerCAmelCase ,  __lowerCAmelCase ,  test_size=0.25 ,  random_state=1							)
  lowercase_								= xgboost(__lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase							)
  # Error printing
  print(F'''Mean Absolute Error : {mean_absolute_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)
  print(F'''Mean Square Error  : {mean_squared_error(__lowerCAmelCase ,  __lowerCAmelCase							)}'''							)


if __name__ == "__main__":
       import doctest

       doctest.testmod(verbose=True)
       main()

