


'''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


a_  :    List[str]      =			logging.get_logger(__name__)


if is_vision_available():
    import PIL





class 					snake_case   (   _a      ):





  """simple docstring"""




  _lowerCamelCase		   =  ['''pixel_values''']






  def __init__(   self      ,  UpperCamelCase = True      ,  UpperCamelCase = None      ,  UpperCamelCase = PILImageResampling.BICUBIC      ,  UpperCamelCase = True      ,  UpperCamelCase = None      ,  UpperCamelCase = True      ,  UpperCamelCase = 1 / 255      ,  UpperCamelCase = True      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = True      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   super().__init__(**SCREAMING_SNAKE_CASE__					)
   lowerCamelCase_								=  size if size is not None else {"shortest_edge": 224}
   lowerCamelCase_								=  get_size_dict(SCREAMING_SNAKE_CASE__      ,  default_to_square=SCREAMING_SNAKE_CASE__					)
   lowerCamelCase_								=  crop_size if crop_size is not None else {"height": 224, "width": 224}
   lowerCamelCase_								=  get_size_dict(SCREAMING_SNAKE_CASE__					)

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






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = PILImageResampling.BICUBIC      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  get_size_dict(SCREAMING_SNAKE_CASE__      ,  default_to_square=SCREAMING_SNAKE_CASE__					)
   lowerCamelCase_								=  get_resize_output_image_size(
       SCREAMING_SNAKE_CASE__      ,  size=(size["height"], size["width"])      ,  default_to_square=SCREAMING_SNAKE_CASE__					)
   return resize(SCREAMING_SNAKE_CASE__      ,  size=SCREAMING_SNAKE_CASE__      ,  resample=SCREAMING_SNAKE_CASE__      ,  data_format=SCREAMING_SNAKE_CASE__      ,  **SCREAMING_SNAKE_CASE__					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  get_size_dict(SCREAMING_SNAKE_CASE__					)
   return center_crop(SCREAMING_SNAKE_CASE__      ,  size=(size["height"], size["width"])      ,  data_format=SCREAMING_SNAKE_CASE__      ,  **SCREAMING_SNAKE_CASE__					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   return rescale(SCREAMING_SNAKE_CASE__      ,  scale=SCREAMING_SNAKE_CASE__      ,  data_format=SCREAMING_SNAKE_CASE__      ,  **SCREAMING_SNAKE_CASE__					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   return normalize(SCREAMING_SNAKE_CASE__      ,  mean=SCREAMING_SNAKE_CASE__      ,  std=SCREAMING_SNAKE_CASE__      ,  data_format=SCREAMING_SNAKE_CASE__      ,  **SCREAMING_SNAKE_CASE__					)







  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = None      ,  UpperCamelCase = ChannelDimension.FIRST      ,  **UpperCamelCase      ,  ):







   """simple docstring"""
   lowerCamelCase_								=  do_resize if do_resize is not None else self.do_resize
   lowerCamelCase_								=  size if size is not None else self.size
   lowerCamelCase_								=  get_size_dict(SCREAMING_SNAKE_CASE__      ,  default_to_square=SCREAMING_SNAKE_CASE__					)
   lowerCamelCase_								=  resample if resample is not None else self.resample
   lowerCamelCase_								=  do_center_crop if do_center_crop is not None else self.do_center_crop
   lowerCamelCase_								=  crop_size if crop_size is not None else self.crop_size
   lowerCamelCase_								=  get_size_dict(SCREAMING_SNAKE_CASE__					)
   lowerCamelCase_								=  do_rescale if do_rescale is not None else self.do_rescale
   lowerCamelCase_								=  rescale_factor if rescale_factor is not None else self.rescale_factor
   lowerCamelCase_								=  do_normalize if do_normalize is not None else self.do_normalize
   lowerCamelCase_								=  image_mean if image_mean is not None else self.image_mean
   lowerCamelCase_								=  image_std if image_std is not None else self.image_std
   lowerCamelCase_								=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   lowerCamelCase_								=  make_list_of_images(SCREAMING_SNAKE_CASE__					)

   if not valid_images(SCREAMING_SNAKE_CASE__					):
    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:
    lowerCamelCase_								=  [convert_to_rgb(SCREAMING_SNAKE_CASE__					) for image in images]

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

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

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

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

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

   lowerCamelCase_								=  [to_channel_dimension_format(SCREAMING_SNAKE_CASE__      ,  SCREAMING_SNAKE_CASE__					) for image in images]

   lowerCamelCase_								=  {"pixel_values": images}
   return BatchFeature(data=SCREAMING_SNAKE_CASE__      ,  tensor_type=SCREAMING_SNAKE_CASE__					)







'''simple docstring'''

import numpy as np





class 					snake_case   :





  """simple docstring"""




  def __init__(   self      ,  UpperCamelCase=5      ,  UpperCamelCase=5					):







   """simple docstring"""
   lowerCamelCase_								=  depth
   lowerCamelCase_								=  0
   lowerCamelCase_								=  None
   lowerCamelCase_								=  None
   lowerCamelCase_								=  min_leaf_size
   lowerCamelCase_								=  None






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if labels.ndim != 1:
    print("Error: Input labels must be one dimensional"					)

   return np.mean((labels - prediction) ** 2					)






  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   if x.ndim != 1:
    print("Error: Input data set must be one dimensional"					)
    return
   if len(UpperCamelCase					) != len(UpperCamelCase					):
    print("Error: X and y have different lengths"					)
    return
   if y.ndim != 1:
    print("Error: Data set labels must be one dimensional"					)
    return

   if len(UpperCamelCase					) < 2 * self.min_leaf_size:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   if self.depth == 1:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)
    return

   lowerCamelCase_								=  0
   lowerCamelCase_								=  self.mean_squared_error(UpperCamelCase      ,  np.mean(UpperCamelCase					)					) * 2
   for i in range(len(UpperCamelCase					)					):
    if len(x[:i]					) < self.min_leaf_size:
     continue
    elif len(x[i:]					) < self.min_leaf_size:
     continue
    else:
     lowerCamelCase_								=  self.mean_squared_error(x[:i]      ,  np.mean(y[:i]					)					)
     lowerCamelCase_								=  self.mean_squared_error(x[i:]      ,  np.mean(y[i:]					)					)
     lowerCamelCase_								=  error_left + error_right
     if error < min_error:
      lowerCamelCase_								=  i
      lowerCamelCase_								=  error

   if best_split != 0:
    lowerCamelCase_								=  x[:best_split]
    lowerCamelCase_								=  y[:best_split]
    lowerCamelCase_								=  x[best_split:]
    lowerCamelCase_								=  y[best_split:]

    lowerCamelCase_								=  x[best_split]
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    lowerCamelCase_								=  DecisionTree(
        depth=self.depth - 1      ,  min_leaf_size=self.min_leaf_size					)
    self.left.train(UpperCamelCase      ,  UpperCamelCase					)
    self.right.train(UpperCamelCase      ,  UpperCamelCase					)
   else:
    lowerCamelCase_								=  np.mean(UpperCamelCase					)

   return







  def   snake_case		(   self      ,  UpperCamelCase					):







   """simple docstring"""
   if self.prediction is not None:
    return self.prediction
   elif self.left or self.right is not None:
    if x >= self.decision_boundary:
     return self.right.predict(UpperCamelCase					)
    else:
     return self.left.predict(UpperCamelCase					)
   else:
    print("Error: Decision tree not yet trained"					)
    return None





class 					snake_case   :





  """simple docstring"""




  @staticmethod
  def   snake_case		(   UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   lowerCamelCase_								=  float(0					)
   for label in labels:
    squared_error_sum += (label - prediction) ** 2

   return float(squared_error_sum / labels.size					)





def __snake_case (				):
 lowerCamelCase_								=  np.arange(-1.0 ,    1.0 ,    0.005		)
 lowerCamelCase_								=  np.sin(UpperCAmelCase_		)

 lowerCamelCase_								=  DecisionTree(depth=10 ,    min_leaf_size=10		)
 tree.train(UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  (np.random.rand(10		) * 2) - 1
 lowerCamelCase_								=  np.array([tree.predict(UpperCAmelCase_		) for x in test_cases]		)
 lowerCamelCase_								=  np.mean((predictions - test_cases) ** 2		)

 print("Test values: " + str(UpperCAmelCase_		)		)
 print("Predictions: " + str(UpperCAmelCase_		)		)
 print("Average error: " + str(UpperCAmelCase_		)		)


if __name__ == "__main__":
     main()
     import doctest

     doctest.testmod(name="""mean_squarred_error""", verbose=True)



