hunk.code_window:     """
       def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False, count_include_pad=True):
           kernel_size = tup_pair(kernel_size)
           stride = tup_pair(stride)
           super(MaxPool2dSame, self).__init__(kernel_size, stride, (0, 0), dilation, ceil_mode, count_include_pad)
       def forward(self, x):
  
hunk.labels: keep keep keep add keep keep keep
hunk.after_edit:         dilation = tup_pair(dilation)
  
hunk.file_path: timm/models/layers/pool2d_same.py
hunk.type: add
file: """ AutoAugment, RandAugment, and AugMix for PyTorch
  
  This code implements the searched ImageNet policies with various tweaks and improvements and
  does not include any of the search code.
  
  AA and RA Implementation adapted from:
      https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/autoaugment.py
  
  AugMix adapted from:
      https://github.com/google-research/augmix
  
  Papers:
      AutoAugment: Learning Augmentation Policies from Data - https://arxiv.org/abs/1805.09501
      Learning Data Augmentation Strategies for Object Detection - https://arxiv.org/abs/1906.11172
      RandAugment: Practical automated data augmentation... - https://arxiv.org/abs/1909.13719
      AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty - https://arxiv.org/abs/1912.02781
  
  Hacked together by Ross Wightman
  """
  import random
  import math
  import re
  from PIL import Image, ImageOps, ImageEnhance, ImageChops
  import PIL
  import numpy as np
  
  
  _PIL_VER = tuple([int(x) for x in PIL.__version__.split('.')[:2]])
  
  _FILL = (128, 128, 128)
  
  # This signifies the max integer that the controller RNN could predict for the
  # augmentation scheme.
  _MAX_LEVEL = 10.
  
  _HPARAMS_DEFAULT = dict(
      translate_const=250,
      img_mean=_FILL,
  )
  
  _RANDOM_INTERPOLATION = (Image.BILINEAR, Image.BICUBIC)
  
  
  def _interpolation(kwargs):
      interpolation = kwargs.pop('resample', Image.BILINEAR)
      if isinstance(interpolation, (list, tuple)):
          return random.choice(interpolation)
      else:
          return interpolation
  
  
  def _check_args_tf(kwargs):
      if 'fillcolor' in kwargs and _PIL_VER < (5, 0):
          kwargs.pop('fillcolor')
      kwargs['resample'] = _interpolation(kwargs)
  
  
  def shear_x(img, factor, **kwargs):
      _check_args_tf(kwargs)
      return img.transform(img.size, Image.AFFINE, (1, factor, 0, 0, 1, 0), **kwargs)
  
  
  def shear_y(img, factor, **kwargs):
      _check_args_tf(kwargs)
      return img.transform(img.size, Image.AFFINE, (1, 0, 0, factor, 1, 0), **kwargs)
  
  
  def translate_x_rel(img, pct, **kwargs):
      pixels = pct * img.size[0]
      _check_args_tf(kwargs)
      return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
  
  
  def translate_y_rel(img, pct, **kwargs):
      pixels = pct * img.size[1]
      _check_args_tf(kwargs)
      return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
  
  
  def translate_x_abs(img, pixels, **kwargs):
      _check_args_tf(kwargs)
      return img.transform(img.size, Image.AFFINE, (1, 0, pixels, 0, 1, 0), **kwargs)
  
  
  def translate_y_abs(img, pixels, **kwargs):
      _check_args_tf(kwargs)
      return img.transform(img.size, Image.AFFINE, (1, 0, 0, 0, 1, pixels), **kwargs)
  
  
  def rotate(img, degrees, **kwargs):
      _check_args_tf(kwargs)
      if _PIL_VER >= (5, 2):
          return img.rotate(degrees, **kwargs)
      elif _PIL_VER >= (5, 0):
          w, h = img.size
          post_trans = (0, 0)
          rotn_center = (w / 2.0, h / 2.0)
          angle = -math.radians(degrees)
          matrix = [
              round(math.cos(angle), 15),
              round(math.sin(angle), 15),
              0.0,
              round(-math.sin(angle), 15),
              round(math.cos(angle), 15),
              0.0,
          ]
  
          def transform(x, y, matrix):
              (a, b, c, d, e, f) = matrix
              return a * x + b * y + c, d * x + e * y + f
  
          matrix[2], matrix[5] = transform(
              -rotn_center[0] - post_trans[0], -rotn_center[1] - post_trans[1], matrix
          )
          matrix[2] += rotn_center[0]
          matrix[5] += rotn_center[1]
          return img.transform(img.size, Image.AFFINE, matrix, **kwargs)
      else:
          return img.rotate(degrees, resample=kwargs['resample'])
  
  
  def auto_contrast(img, **__):
      return ImageOps.autocontrast(img)
  
  
  def invert(img, **__):
      return ImageOps.invert(img)
  
  
  def equalize(img, **__):
      return ImageOps.equalize(img)
  
  
  def solarize(img, thresh, **__):
      return ImageOps.solarize(img, thresh)
  
  
  def solarize_add(img, add, thresh=128, **__):
      lut = []
      for i in range(256):
          if i < thresh:
              lut.append(min(255, i + add))
          else:
              lut.append(i)
      if img.mode in ("L", "RGB"):
          if img.mode == "RGB" and len(lut) == 256:
              lut = lut + lut + lut
          return img.point(lut)
      else:
          return img
  
  
  def posterize(img, bits_to_keep, **__):
      if bits_to_keep >= 8:
          return img
      return ImageOps.posterize(img, bits_to_keep)
  
  
  def contrast(img, factor, **__):
      return ImageEnhance.Contrast(img).enhance(factor)
  
  
  def color(img, factor, **__):
      return ImageEnhance.Color(img).enhance(factor)
  
  
  def brightness(img, factor, **__):
      return ImageEnhance.Brightness(img).enhance(factor)
  
  
  def sharpness(img, factor, **__):
      return ImageEnhance.Sharpness(img).enhance(factor)
  
  
  def _randomly_negate(v):
      """With 50% prob, negate the value"""
      return -v if random.random() > 0.5 else v
  
  
  def _rotate_level_to_arg(level, _hparams):
      # range [-30, 30]
      level = (level / _MAX_LEVEL) * 30.
      level = _randomly_negate(level)
      return level,
  
  
  def _enhance_level_to_arg(level, _hparams):
      # range [0.1, 1.9]
      return (level / _MAX_LEVEL) * 1.8 + 0.1,
  
  
  def _enhance_increasing_level_to_arg(level, _hparams):
      # the 'no change' level is 1.0, moving away from that towards 0. or 2.0 increases the enhancement blend
      # range [0.1, 1.9]
      level = (level / _MAX_LEVEL) * .9
      level = 1.0 + _randomly_negate(level)
      return level,
  
  
  def _shear_level_to_arg(level, _hparams):
      # range [-0.3, 0.3]
      level = (level / _MAX_LEVEL) * 0.3
      level = _randomly_negate(level)
      return level,
  
  
  def _translate_abs_level_to_arg(level, hparams):
      translate_const = hparams['translate_const']
      level = (level / _MAX_LEVEL) * float(translate_const)
      level = _randomly_negate(level)
      return level,
  
  
  def _translate_rel_level_to_arg(level, hparams):
      # default range [-0.45, 0.45]
      translate_pct = hparams.get('translate_pct', 0.45)
      level = (level / _MAX_LEVEL) * translate_pct
      level = _randomly_negate(level)
      return level,
  
  
  def _posterize_level_to_arg(level, _hparams):
      # As per Tensorflow TPU EfficientNet impl
      # range [0, 4], 'keep 0 up to 4 MSB of original image'
      # intensity/severity of augmentation decreases with level
      return int((level / _MAX_LEVEL) * 4),
  
  
  def _posterize_increasing_level_to_arg(level, hparams):
      # As per Tensorflow models research and UDA impl
      # range [4, 0], 'keep 4 down to 0 MSB of original image',
      # intensity/severity of augmentation increases with level
      return 4 - _posterize_level_to_arg(level, hparams)[0],
  
  
  def _posterize_original_level_to_arg(level, _hparams):
      # As per original AutoAugment paper description
      # range [4, 8], 'keep 4 up to 8 MSB of image'
      # intensity/severity of augmentation decreases with level
      return int((level / _MAX_LEVEL) * 4) + 4,
  
  
  def _solarize_level_to_arg(level, _hparams):
      # range [0, 256]
      # intensity/severity of augmentation decreases with level
      return int((level / _MAX_LEVEL) * 256),
  
  
  def _solarize_increasing_level_to_arg(level, _hparams):
      # range [0, 256]
      # intensity/severity of augmentation increases with level
      return 256 - _solarize_level_to_arg(level, _hparams)[0],
  
  
  def _solarize_add_level_to_arg(level, _hparams):
      # range [0, 110]
      return int((level / _MAX_LEVEL) * 110),
  
  
  LEVEL_TO_ARG = {
      'AutoContrast': None,
      'Equalize': None,
      'Invert': None,
      'Rotate': _rotate_level_to_arg,
      # There are several variations of the posterize level scaling in various Tensorflow/Google repositories/papers
      'Posterize': _posterize_level_to_arg,
      'PosterizeIncreasing': _posterize_increasing_level_to_arg,
      'PosterizeOriginal': _posterize_original_level_to_arg,
      'Solarize': _solarize_level_to_arg,
      'SolarizeIncreasing': _solarize_increasing_level_to_arg,
      'SolarizeAdd': _solarize_add_level_to_arg,
      'Color': _enhance_level_to_arg,
      'ColorIncreasing': _enhance_increasing_level_to_arg,
      'Contrast': _enhance_level_to_arg,
      'ContrastIncreasing': _enhance_increasing_level_to_arg,
      'Brightness': _enhance_level_to_arg,
      'BrightnessIncreasing': _enhance_increasing_level_to_arg,
      'Sharpness': _enhance_level_to_arg,
      'SharpnessIncreasing': _enhance_increasing_level_to_arg,
      'ShearX': _shear_level_to_arg,
      'ShearY': _shear_level_to_arg,
      'TranslateX': _translate_abs_level_to_arg,
      'TranslateY': _translate_abs_level_to_arg,
      'TranslateXRel': _translate_rel_level_to_arg,
      'TranslateYRel': _translate_rel_level_to_arg,
  }
  
  
  NAME_TO_OP = {
      'AutoContrast': auto_contrast,
      'Equalize': equalize,
      'Invert': invert,
      'Rotate': rotate,
      'Posterize': posterize,
      'PosterizeIncreasing': posterize,
      'PosterizeOriginal': posterize,
      'Solarize': solarize,
      'SolarizeIncreasing': solarize,
      'SolarizeAdd': solarize_add,
      'Color': color,
      'ColorIncreasing': color,
      'Contrast': contrast,
      'ContrastIncreasing': contrast,
      'Brightness': brightness,
      'BrightnessIncreasing': brightness,
      'Sharpness': sharpness,
      'SharpnessIncreasing': sharpness,
      'ShearX': shear_x,
      'ShearY': shear_y,
      'TranslateX': translate_x_abs,
      'TranslateY': translate_y_abs,
      'TranslateXRel': translate_x_rel,
      'TranslateYRel': translate_y_rel,
  }
  
  
  class AugmentOp:
  
      def __init__(self, name, prob=0.5, magnitude=10, hparams=None):
          hparams = hparams or _HPARAMS_DEFAULT
          self.aug_fn = NAME_TO_OP[name]
          self.level_fn = LEVEL_TO_ARG[name]
          self.prob = prob
          self.magnitude = magnitude
          self.hparams = hparams.copy()
          self.kwargs = dict(
              fillcolor=hparams['img_mean'] if 'img_mean' in hparams else _FILL,
              resample=hparams['interpolation'] if 'interpolation' in hparams else _RANDOM_INTERPOLATION,
          )
  
          # If magnitude_std is > 0, we introduce some randomness
          # in the usually fixed policy and sample magnitude from a normal distribution
          # with mean `magnitude` and std-dev of `magnitude_std`.
          # NOTE This is my own hack, being tested, not in papers or reference impls.
          self.magnitude_std = self.hparams.get('magnitude_std', 0)
  
      def __call__(self, img):
          if self.prob < 1.0 and random.random() > self.prob:
              return img
          magnitude = self.magnitude
          if self.magnitude_std and self.magnitude_std > 0:
              magnitude = random.gauss(magnitude, self.magnitude_std)
          magnitude = min(_MAX_LEVEL, max(0, magnitude))  # clip to valid range
          level_args = self.level_fn(magnitude, self.hparams) if self.level_fn is not None else tuple()
          return self.aug_fn(img, *level_args, **self.kwargs)
  
  
  def auto_augment_policy_v0(hparams):
      # ImageNet v0 policy from TPU EfficientNet impl, cannot find a paper reference.
      policy = [
          [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
          [('Color', 0.4, 9), ('Equalize', 0.6, 3)],
          [('Color', 0.4, 1), ('Rotate', 0.6, 8)],
          [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
          [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
          [('Color', 0.2, 0), ('Equalize', 0.8, 8)],
          [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
          [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
          [('Color', 0.6, 1), ('Equalize', 1.0, 2)],
          [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
          [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
          [('Color', 0.4, 7), ('Equalize', 0.6, 0)],
          [('Posterize', 0.4, 6), ('AutoContrast', 0.4, 7)],
          [('Solarize', 0.6, 8), ('Color', 0.6, 9)],
          [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
          [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
          [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
          [('ShearY', 0.8, 0), ('Color', 0.6, 4)],
          [('Color', 1.0, 0), ('Rotate', 0.6, 2)],
          [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
          [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
          [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
          [('Posterize', 0.8, 2), ('Solarize', 0.6, 10)],  # This results in black image with Tpu posterize
          [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
          [('Color', 0.8, 6), ('Rotate', 0.4, 5)],
      ]
      pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
      return pc
  
  
  def auto_augment_policy_v0r(hparams):
      # ImageNet v0 policy from TPU EfficientNet impl, with variation of Posterize used
      # in Google research implementation (number of bits discarded increases with magnitude)
      policy = [
          [('Equalize', 0.8, 1), ('ShearY', 0.8, 4)],
          [('Color', 0.4, 9), ('Equalize', 0.6, 3)],
          [('Color', 0.4, 1), ('Rotate', 0.6, 8)],
          [('Solarize', 0.8, 3), ('Equalize', 0.4, 7)],
          [('Solarize', 0.4, 2), ('Solarize', 0.6, 2)],
          [('Color', 0.2, 0), ('Equalize', 0.8, 8)],
          [('Equalize', 0.4, 8), ('SolarizeAdd', 0.8, 3)],
          [('ShearX', 0.2, 9), ('Rotate', 0.6, 8)],
          [('Color', 0.6, 1), ('Equalize', 1.0, 2)],
          [('Invert', 0.4, 9), ('Rotate', 0.6, 0)],
          [('Equalize', 1.0, 9), ('ShearY', 0.6, 3)],
          [('Color', 0.4, 7), ('Equalize', 0.6, 0)],
          [('PosterizeIncreasing', 0.4, 6), ('AutoContrast', 0.4, 7)],
          [('Solarize', 0.6, 8), ('Color', 0.6, 9)],
          [('Solarize', 0.2, 4), ('Rotate', 0.8, 9)],
          [('Rotate', 1.0, 7), ('TranslateYRel', 0.8, 9)],
          [('ShearX', 0.0, 0), ('Solarize', 0.8, 4)],
          [('ShearY', 0.8, 0), ('Color', 0.6, 4)],
          [('Color', 1.0, 0), ('Rotate', 0.6, 2)],
          [('Equalize', 0.8, 4), ('Equalize', 0.0, 8)],
          [('Equalize', 1.0, 4), ('AutoContrast', 0.6, 2)],
          [('ShearY', 0.4, 7), ('SolarizeAdd', 0.6, 7)],
          [('PosterizeIncreasing', 0.8, 2), ('Solarize', 0.6, 10)],
          [('Solarize', 0.6, 8), ('Equalize', 0.6, 1)],
          [('Color', 0.8, 6), ('Rotate', 0.4, 5)],
      ]
      pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
      return pc
  
  
  def auto_augment_policy_original(hparams):
      # ImageNet policy from https://arxiv.org/abs/1805.09501
      policy = [
          [('PosterizeOriginal', 0.4, 8), ('Rotate', 0.6, 9)],
          [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
          [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
          [('PosterizeOriginal', 0.6, 7), ('PosterizeOriginal', 0.6, 6)],
          [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
          [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
          [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
          [('PosterizeOriginal', 0.8, 5), ('Equalize', 1.0, 2)],
          [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
          [('Equalize', 0.6, 8), ('PosterizeOriginal', 0.4, 6)],
          [('Rotate', 0.8, 8), ('Color', 0.4, 0)],
          [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
          [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
          [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
          [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
          [('Rotate', 0.8, 8), ('Color', 1.0, 2)],
          [('Color', 0.8, 8), ('Solarize', 0.8, 7)],
          [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
          [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
          [('Color', 0.4, 0), ('Equalize', 0.6, 3)],
          [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
          [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
          [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
          [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
          [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
      ]
      pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
      return pc
  
  
  def auto_augment_policy_originalr(hparams):
      # ImageNet policy from https://arxiv.org/abs/1805.09501 with research posterize variation
      policy = [
          [('PosterizeIncreasing', 0.4, 8), ('Rotate', 0.6, 9)],
          [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
          [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
          [('PosterizeIncreasing', 0.6, 7), ('PosterizeIncreasing', 0.6, 6)],
          [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
          [('Equalize', 0.4, 4), ('Rotate', 0.8, 8)],
          [('Solarize', 0.6, 3), ('Equalize', 0.6, 7)],
          [('PosterizeIncreasing', 0.8, 5), ('Equalize', 1.0, 2)],
          [('Rotate', 0.2, 3), ('Solarize', 0.6, 8)],
          [('Equalize', 0.6, 8), ('PosterizeIncreasing', 0.4, 6)],
          [('Rotate', 0.8, 8), ('Color', 0.4, 0)],
          [('Rotate', 0.4, 9), ('Equalize', 0.6, 2)],
          [('Equalize', 0.0, 7), ('Equalize', 0.8, 8)],
          [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
          [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
          [('Rotate', 0.8, 8), ('Color', 1.0, 2)],
          [('Color', 0.8, 8), ('Solarize', 0.8, 7)],
          [('Sharpness', 0.4, 7), ('Invert', 0.6, 8)],
          [('ShearX', 0.6, 5), ('Equalize', 1.0, 9)],
          [('Color', 0.4, 0), ('Equalize', 0.6, 3)],
          [('Equalize', 0.4, 7), ('Solarize', 0.2, 4)],
          [('Solarize', 0.6, 5), ('AutoContrast', 0.6, 5)],
          [('Invert', 0.6, 4), ('Equalize', 1.0, 8)],
          [('Color', 0.6, 4), ('Contrast', 1.0, 8)],
          [('Equalize', 0.8, 8), ('Equalize', 0.6, 3)],
      ]
      pc = [[AugmentOp(*a, hparams=hparams) for a in sp] for sp in policy]
      return pc
  
  
  def auto_augment_policy(name='v0', hparams=None):
      hparams = hparams or _HPARAMS_DEFAULT
      if name == 'original':
          return auto_augment_policy_original(hparams)
      elif name == 'originalr':
          return auto_augment_policy_originalr(hparams)
      elif name == 'v0':
          return auto_augment_policy_v0(hparams)
      elif name == 'v0r':
          return auto_augment_policy_v0r(hparams)
      else:
          assert False, 'Unknown AA policy (%s)' % name
  
  
  class AutoAugment:
  
      def __init__(self, policy):
          self.policy = policy
  
      def __call__(self, img):
          sub_policy = random.choice(self.policy)
          for op in sub_policy:
              img = op(img)
          return img
  
  
  def auto_augment_transform(config_str, hparams):
      """
      Create a AutoAugment transform
  
      :param config_str: String defining configuration of auto augmentation. Consists of multiple sections separated by
      dashes ('-'). The first section defines the AutoAugment policy (one of 'v0', 'v0r', 'original', 'originalr').
      The remaining sections, not order sepecific determine
          'mstd' -  float std deviation of magnitude noise applied
      Ex 'original-mstd0.5' results in AutoAugment with original policy, magnitude_std 0.5
  
      :param hparams: Other hparams (kwargs) for the AutoAugmentation scheme
  
      :return: A PyTorch compatible Transform
      """
      config = config_str.split('-')
      policy_name = config[0]
      config = config[1:]
      for c in config:
          cs = re.split(r'(\d.*)', c)
          if len(cs) < 2:
              continue
          key, val = cs[:2]
          if key == 'mstd':
              # noise param injected via hparams for now
              hparams.setdefault('magnitude_std', float(val))
          else:
              assert False, 'Unknown AutoAugment config section'
      aa_policy = auto_augment_policy(policy_name, hparams=hparams)
      return AutoAugment(aa_policy)
  
  
  _RAND_TRANSFORMS = [
      'AutoContrast',
      'Equalize',
      'Invert',
      'Rotate',
      'Posterize',
      'Solarize',
      'SolarizeAdd',
      'Color',
      'Contrast',
      'Brightness',
      'Sharpness',
      'ShearX',
      'ShearY',
      'TranslateXRel',
      'TranslateYRel',
      #'Cutout'  # NOTE I've implement this as random erasing separately
  ]
  
  
  _RAND_INCREASING_TRANSFORMS = [
      'AutoContrast',
      'Equalize',
      'Invert',
      'Rotate',
      'PosterizeIncreasing',
      'SolarizeIncreasing',
      'SolarizeAdd',
      'ColorIncreasing',
      'ContrastIncreasing',
      'BrightnessIncreasing',
      'SharpnessIncreasing',
      'ShearX',
      'ShearY',
      'TranslateXRel',
      'TranslateYRel',
      #'Cutout'  # NOTE I've implement this as random erasing separately
  ]
  
  
  
  # These experimental weights are based loosely on the relative improvements mentioned in paper.
  # They may not result in increased performance, but could likely be tuned to so.
  _RAND_CHOICE_WEIGHTS_0 = {
      'Rotate': 0.3,
      'ShearX': 0.2,
      'ShearY': 0.2,
      'TranslateXRel': 0.1,
      'TranslateYRel': 0.1,
      'Color': .025,
      'Sharpness': 0.025,
      'AutoContrast': 0.025,
      'Solarize': .005,
      'SolarizeAdd': .005,
      'Contrast': .005,
      'Brightness': .005,
      'Equalize': .005,
      'Posterize': 0,
      'Invert': 0,
  }
  
  
  def _select_rand_weights(weight_idx=0, transforms=None):
      transforms = transforms or _RAND_TRANSFORMS
      assert weight_idx == 0  # only one set of weights currently
      rand_weights = _RAND_CHOICE_WEIGHTS_0
      probs = [rand_weights[k] for k in transforms]
      probs /= np.sum(probs)
      return probs
  
  
  def rand_augment_ops(magnitude=10, hparams=None, transforms=None):
      hparams = hparams or _HPARAMS_DEFAULT
      transforms = transforms or _RAND_TRANSFORMS
      return [AugmentOp(
          name, prob=0.5, magnitude=magnitude, hparams=hparams) for name in transforms]
  
  
  class RandAugment:
      def __init__(self, ops, num_layers=2, choice_weights=None):
          self.ops = ops
          self.num_layers = num_layers
          self.choice_weights = choice_weights
  
      def __call__(self, img):
          # no replacement when using weighted choice
          ops = np.random.choice(
              self.ops, self.num_layers, replace=self.choice_weights is None, p=self.choice_weights)
          for op in ops:
              img = op(img)
          return img
  
  
  def rand_augment_transform(config_str, hparams):
      """
      Create a RandAugment transform
  
      :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
      dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
      sections, not order sepecific determine
          'm' - integer magnitude of rand augment
          'n' - integer num layers (number of transform ops selected per image)
          'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)
          'mstd' -  float std deviation of magnitude noise applied
          'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
      Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5
      'rand-mstd1-w0' results in magnitude_std 1.0, weights 0, default magnitude of 10 and num_layers 2
  
      :param hparams: Other hparams (kwargs) for the RandAugmentation scheme
  
      :return: A PyTorch compatible Transform
      """
      magnitude = _MAX_LEVEL  # default to _MAX_LEVEL for magnitude (currently 10)
      num_layers = 2  # default to 2 ops per image
      weight_idx = None  # default to no probability weights for op choice
      transforms = _RAND_TRANSFORMS
      config = config_str.split('-')
      assert config[0] == 'rand'
      config = config[1:]
      for c in config:
          cs = re.split(r'(\d.*)', c)
          if len(cs) < 2:
              continue
          key, val = cs[:2]
          if key == 'mstd':
              # noise param injected via hparams for now
              hparams.setdefault('magnitude_std', float(val))
          elif key == 'inc':
              if bool(val):
                  transforms = _RAND_INCREASING_TRANSFORMS
          elif key == 'm':
              magnitude = int(val)
          elif key == 'n':
              num_layers = int(val)
          elif key == 'w':
              weight_idx = int(val)
          else:
              assert False, 'Unknown RandAugment config section'
      ra_ops = rand_augment_ops(magnitude=magnitude, hparams=hparams, transforms=transforms)
      choice_weights = None if weight_idx is None else _select_rand_weights(weight_idx)
      return RandAugment(ra_ops, num_layers, choice_weights=choice_weights)
  
  
  _AUGMIX_TRANSFORMS = [
      'AutoContrast',
      'ColorIncreasing',  # not in paper
      'ContrastIncreasing',  # not in paper
      'BrightnessIncreasing',  # not in paper
      'SharpnessIncreasing',  # not in paper
      'Equalize',
      'Rotate',
      'PosterizeIncreasing',
      'SolarizeIncreasing',
      'ShearX',
      'ShearY',
      'TranslateXRel',
      'TranslateYRel',
  ]
  
  
  def augmix_ops(magnitude=10, hparams=None, transforms=None):
      hparams = hparams or _HPARAMS_DEFAULT
      transforms = transforms or _AUGMIX_TRANSFORMS
      return [AugmentOp(
          name, prob=1.0, magnitude=magnitude, hparams=hparams) for name in transforms]
  
  
  class AugMixAugment:
      """ AugMix Transform
      Adapted and improved from impl here: https://github.com/google-research/augmix/blob/master/imagenet.py
      From paper: 'AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty -
      https://arxiv.org/abs/1912.02781
      """
      def __init__(self, ops, alpha=1., width=3, depth=-1, blended=False):
          self.ops = ops
          self.alpha = alpha
          self.width = width
          self.depth = depth
          self.blended = blended  # blended mode is faster but not well tested
  
      def _calc_blended_weights(self, ws, m):
          ws = ws * m
          cump = 1.
          rws = []
          for w in ws[::-1]:
              alpha = w / cump
              cump *= (1 - alpha)
              rws.append(alpha)
          return np.array(rws[::-1], dtype=np.float32)
  
      def _apply_blended(self, img, mixing_weights, m):
          # This is my first crack and implementing a slightly faster mixed augmentation. Instead
          # of accumulating the mix for each chain in a Numpy array and then blending with original,
          # it recomputes the blending coefficients and applies one PIL image blend per chain.
          # TODO the results appear in the right ballpark but they differ by more than rounding.
          img_orig = img.copy()
          ws = self._calc_blended_weights(mixing_weights, m)
          for w in ws:
              depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
              ops = np.random.choice(self.ops, depth, replace=True)
              img_aug = img_orig  # no ops are in-place, deep copy not necessary
              for op in ops:
                  img_aug = op(img_aug)
              img = Image.blend(img, img_aug, w)
          return img
  
      def _apply_basic(self, img, mixing_weights, m):
          # This is a literal adaptation of the paper/official implementation without normalizations and
          # PIL <-> Numpy conversions between every op. It is still quite CPU compute heavy compared to the
          # typical augmentation transforms, could use a GPU / Kornia implementation.
          img_shape = img.size[0], img.size[1], len(img.getbands())
          mixed = np.zeros(img_shape, dtype=np.float32)
          for mw in mixing_weights:
              depth = self.depth if self.depth > 0 else np.random.randint(1, 4)
              ops = np.random.choice(self.ops, depth, replace=True)
              img_aug = img  # no ops are in-place, deep copy not necessary
              for op in ops:
                  img_aug = op(img_aug)
              mixed += mw * np.asarray(img_aug, dtype=np.float32)
          np.clip(mixed, 0, 255., out=mixed)
          mixed = Image.fromarray(mixed.astype(np.uint8))
          return Image.blend(img, mixed, m)
  
      def __call__(self, img):
          mixing_weights = np.float32(np.random.dirichlet([self.alpha] * self.width))
          m = np.float32(np.random.beta(self.alpha, self.alpha))
          if self.blended:
              mixed = self._apply_blended(img, mixing_weights, m)
          else:
              mixed = self._apply_basic(img, mixing_weights, m)
          return mixed
  
  
  def augment_and_mix_transform(config_str, hparams):
      """ Create AugMix PyTorch transform
  
      :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
      dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
      sections, not order sepecific determine
          'm' - integer magnitude (severity) of augmentation mix (default: 3)
          'w' - integer width of augmentation chain (default: 3)
          'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1)
          'b' - integer (bool), blend each branch of chain into end result without a final blend, less CPU (default: 0)
          'mstd' -  float std deviation of magnitude noise applied (default: 0)
      Ex 'augmix-m5-w4-d2' results in AugMix with severity 5, chain width 4, chain depth 2
  
      :param hparams: Other hparams (kwargs) for the Augmentation transforms
  
      :return: A PyTorch compatible Transform
      """
      magnitude = 3
      width = 3
      depth = -1
      alpha = 1.
      blended = False
      config = config_str.split('-')
      assert config[0] == 'augmix'
      config = config[1:]
      for c in config:
          cs = re.split(r'(\d.*)', c)
          if len(cs) < 2:
              continue
          key, val = cs[:2]
          if key == 'mstd':
              # noise param injected via hparams for now
              hparams.setdefault('magnitude_std', float(val))
          elif key == 'm':
              magnitude = int(val)
          elif key == 'w':
              width = int(val)
          elif key == 'd':
              depth = int(val)
          elif key == 'a':
              alpha = float(val)
          elif key == 'b':
              blended = bool(val)
          else:
              assert False, 'Unknown AugMix config section'
      ops = augmix_ops(magnitude=magnitude, hparams=hparams)
      return AugMixAugment(ops, alpha=alpha, width=width, depth=depth, blended=blended)
  