



"""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, 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     =			logging.get_logger(__name__)
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				Tuple     						=					['pixel_values']



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


     '''simple docstring'''




     super().__init__(**lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=size if size is not None else {"""height""": 256, """width""": 256}
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=get_size_dict(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""		)

     SCREAMING_SNAKE_CASE_: List[str]     			=do_resize
     SCREAMING_SNAKE_CASE_: List[str]     			=size
     SCREAMING_SNAKE_CASE_: Optional[int]     			=resample
     SCREAMING_SNAKE_CASE_: Any     			=do_center_crop
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=crop_size
     SCREAMING_SNAKE_CASE_: Any     			=do_rescale
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=rescale_factor
     SCREAMING_SNAKE_CASE_: List[str]     			=do_normalize
     SCREAMING_SNAKE_CASE_: Tuple     			=image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image_std if image_std is not None else IMAGENET_STANDARD_STD



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					PILImageResampling = PIL.Image.BICUBIC       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Tuple       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=get_size_dict(lowerCAmelCase		)
     if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return resize(
         lowerCAmelCase       ,		size=(size["""height"""], size["""width"""])       ,		resample=lowerCAmelCase       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Dict[str, int]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Tuple       ,		)     ->					np.ndarray:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=get_size_dict(lowerCAmelCase		)
     if "height" not in size or "width" not in size:
        raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''		)
     return center_crop(lowerCAmelCase       ,		size=(size["""height"""], size["""width"""])       ,		data_format=lowerCAmelCase       ,		**lowerCAmelCase		)



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


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					np.ndarray       ,		lowerCAmelCase :					Union[float, List[float]]       ,		lowerCAmelCase :					Union[float, List[float]]       ,		lowerCAmelCase :					Optional[Union[str, ChannelDimension]] = None       ,		**lowerCAmelCase :					Tuple       ,		)     ->					np.ndarray:


     '''simple docstring'''




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





  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					ImageInput       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = None       ,		lowerCAmelCase :					Optional[int]=None       ,		lowerCAmelCase :					bool = None       ,		lowerCAmelCase :					Dict[str, int] = 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 :					int       ,		)     ->					PIL.Image.Image:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=size if size is not None else self.size
     SCREAMING_SNAKE_CASE_: List[str]     			=get_size_dict(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=crop_size if crop_size is not None else self.crop_size
     SCREAMING_SNAKE_CASE_: List[Any]     			=get_size_dict(lowerCAmelCase       ,		param_name="""crop_size"""		)

     SCREAMING_SNAKE_CASE_: int     			=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_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."""		)

     # All transformations expect numpy arrays.
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=[to_numpy_array(lowerCAmelCase		) for image in images]

     if do_resize:
        SCREAMING_SNAKE_CASE_: List[str]     			=[self.resize(image=lowerCAmelCase       ,		size=lowerCAmelCase       ,		resample=lowerCAmelCase		) for image in images]

     if do_center_crop:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=[self.center_crop(image=lowerCAmelCase       ,		size=lowerCAmelCase		) for image in images]

     if do_rescale:
        SCREAMING_SNAKE_CASE_: Tuple     			=[self.rescale(image=lowerCAmelCase       ,		scale=lowerCAmelCase		) for image in images]

     if do_normalize:
        SCREAMING_SNAKE_CASE_: str     			=[self.normalize(image=lowerCAmelCase       ,		mean=lowerCAmelCase       ,		std=lowerCAmelCase		) for image in images]

     SCREAMING_SNAKE_CASE_: List[str]     			=[to_channel_dimension_format(lowerCAmelCase       ,		lowerCAmelCase		) for image in images]

     SCREAMING_SNAKE_CASE_: List[Any]     			={"""pixel_values""": images}
     return BatchFeature(data=lowerCAmelCase       ,		tensor_type=lowerCAmelCase		)








"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier





def      __magic_name__				(							lowercase ):
   return (data["data"], data["target"])





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=XGBClassifier()
   classifier.fit(lowercase							,			lowercase )
   return classifier





def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=load_iris()
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=data_handling(lowercase )
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=train_test_split(
       lowercase							,			lowercase							,			test_size=0.25 )

   SCREAMING_SNAKE_CASE_: Tuple     			=iris["""target_names"""]

   # Create an XGBoost Classifier from the training data
   SCREAMING_SNAKE_CASE_: Optional[int]     			=xgboost(lowercase							,			lowercase )

   # Display the confusion matrix of the classifier with both training and test sets
   ConfusionMatrixDisplay.from_estimator(
       lowercase							,			lowercase							,			lowercase							,			display_labels=lowercase							,			cmap="""Blues"""							,			normalize="""true"""							,			)
   plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
   plt.show()


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   main()



