# Path: preprocess.py
# def get_preprocess_fn(pp_pipeline, is_training):
#   """Returns preprocessing function.
# 
#   The minilanguage is as follows:
# 
#     fn1|fn2(arg, arg2,...)|fn3(key1=val1, key2=val2, ...)|...
# 
#   And describes the successive application of the various `fn`s to the input,
#   where each function can optionally have one or more arguments, which are
#   either positional or key/value, as dictated by the `fn`.
# 
#   Args:
#     pp_pipeline: A string describing the pre-processing pipeline.
#     is_training: Whether this should be run in train or eval mode.
#   Returns:
#     preprocessing function
# 
#   Raises:
#     ValueError: if preprocessing function name is unknown
#   """
# 
#   def _fn(data):
#     def parse_fn(fn_call):
#       """Parses the fn(arg1,arg2,...) and fn(a=1,b=2) structures.
# 
#       Args:
#         fn_call: string, the function call as a string.
# 
#       Returns:
#         The function name, and either a list (possibly empty) or a dict,
#         depending on the syntax of the function call.
#       """
#       if "(" in fn_call:
#         fn_name, fn_args = fn_call.split("(")
#         if "=" in fn_args:
#           fn_args = dict(kv.split("=") for kv in fn_args[:-1].split(","))
#           return fn_name, {k.strip(): v.strip() for k, v in fn_args.items()}
#         else:
#           return fn_name, [a.strip() for a in fn_args[:-1].split(",")]
#       else:
#         return fn_call, []
# 
#     def get(list_, index, default):
#       """Return element at `index` in `list_` or the `default`."""
#       try:
#         return list_[index]
#       except IndexError:
#         return default
# 
#     def expand(fn_name, args):
#       if fn_name == "plain_preprocess":
#         yield lambda x: x
#       elif fn_name == "0_to_1":
#         yield get_value_range_preprocess(0, 1)
#       elif fn_name == "-1_to_1":
#         yield get_value_range_preprocess(-1, 1)
#       elif fn_name == "value_range":
#         yield get_value_range_preprocess(*map(float, args))
#       elif fn_name == "resize":
#         yield get_resize_preprocess(args, is_training)
#       elif fn_name == "resize_small":
#         yield get_resize_small(int(args[0]))
#       elif fn_name == "crop":
#         yield get_crop(is_training, utils.str2intlist(args, 2))
#       elif fn_name == "central_crop":
#         yield get_crop(False, utils.str2intlist(args, 2))
#       elif fn_name == "multi_crop":
#         yield get_multi_crop(utils.str2intlist(args, 2))
#       elif fn_name == "inception_crop":
#         yield get_inception_crop(is_training)
#       elif fn_name == "flip_lr":
#         yield get_random_flip_lr(is_training)
#       elif fn_name == "hsvnoise":
#         # TODO(lbeyer): expose the parameters? Or maybe just a scale parameter?
#         yield get_hsvnoise_preprocess(*args)
#       elif fn_name == "crop_inception_preprocess_patches":
#         npatch = int(args[0])
#         size = utils.str2intlist(args[1:], 2)
#         yield get_inception_preprocess_patches(is_training, size, npatch)
#       elif fn_name == "crop_inception_patches":
#         npatch = int(args[0])
#         size = utils.str2intlist(args[1:], 2)
#         yield get_inception_crop_patches(size, npatch)
#       elif fn_name == "to_gray":
#         yield get_to_gray_preprocess(float(get(args, 0, 1.0)))
#       elif fn_name == "standardize":
#         yield get_standardize_preprocess()
#       elif fn_name == "rotate":
#         yield get_rotate_preprocess()
#       elif fn_name == "copy_label":
#         yield get_copy_label_preprocess(get(args, 0, "copy_label"))
# 
#       # Below this line specific combos decomposed.
#       # It would be nice to move them to the configs at some point.
# 
#       elif fn_name == "inception_preprocess":
#         yield get_inception_preprocess(is_training, utils.str2intlist(args, 2))
#       else:
#         raise ValueError("Not supported preprocessing %s" % fn_name)
# 
#     # Apply all the individual steps in sequence.
#     tf.logging.info("Data before pre-processing:\n%s", data)
#     for fn_name in pp_pipeline.split("|"):
#       for p in expand(*parse_fn(fn_name.strip())):
#         data = p(data)
#         tf.logging.info("Data after `%s`:\n%s", p, data)
#     return data
# 
#   return _fn


from absl import flags
from tensorflow.contrib.data import AUTOTUNE
from tensorflow.contrib.lookup import index_table_from_tensor
from preprocess import get_preprocess_fn
import abc
import json
import os
import tensorflow.compat.v1 as tf
          label = tf.cond(
              tf.math.equal(label_flag, tf.constant(1, dtype=tf.int32)),
              true_fn=lambda: label_original,
              false_fn=lambda: label)
        else:
            raise ValueError('Unkown split{}'.format(self.split_name))

    return self.preprocess_fn({'image': image, 'label': label})

  def get_filter(self):  # pylint: disable=missing-docstring
    def _filter_fn(example):
      index = self.filename_list.lookup(example[self.FILENAME_KEY])
      return tf.math.greater_equal(index, 0)
    return _filter_fn


DATASET_MAP = {
    'imagenet': DatasetImagenet,
}


def get_data_batch(batch_size,  # pylint: disable=missing-docstring
                   split_name,
                   is_training,
                   preprocessing,
                   filename_list=None,
                   shuffle=True,
                   num_epochs=None,
                   drop_remainder=False):
  dataset = DATASET_MAP[FLAGS.dataset]

# Path: preprocess.py
# def get_preprocess_fn(pp_pipeline, is_training):
#   """Returns preprocessing function.
# 
#   The minilanguage is as follows:
# 
#     fn1|fn2(arg, arg2,...)|fn3(key1=val1, key2=val2, ...)|...
# 
#   And describes the successive application of the various `fn`s to the input,
#   where each function can optionally have one or more arguments, which are
#   either positional or key/value, as dictated by the `fn`.
# 
#   Args:
#     pp_pipeline: A string describing the pre-processing pipeline.
#     is_training: Whether this should be run in train or eval mode.
#   Returns:
#     preprocessing function
# 
#   Raises:
#     ValueError: if preprocessing function name is unknown
#   """
# 
#   def _fn(data):
#     def parse_fn(fn_call):
#       """Parses the fn(arg1,arg2,...) and fn(a=1,b=2) structures.
# 
#       Args:
#         fn_call: string, the function call as a string.
# 
#       Returns:
#         The function name, and either a list (possibly empty) or a dict,
#         depending on the syntax of the function call.
#       """
#       if "(" in fn_call:
#         fn_name, fn_args = fn_call.split("(")
#         if "=" in fn_args:
#           fn_args = dict(kv.split("=") for kv in fn_args[:-1].split(","))
#           return fn_name, {k.strip(): v.strip() for k, v in fn_args.items()}
#         else:
#           return fn_name, [a.strip() for a in fn_args[:-1].split(",")]
#       else:
#         return fn_call, []
# 
#     def get(list_, index, default):
#       """Return element at `index` in `list_` or the `default`."""
#       try:
#         return list_[index]
#       except IndexError:
#         return default
# 
#     def expand(fn_name, args):
#       if fn_name == "plain_preprocess":
#         yield lambda x: x
#       elif fn_name == "0_to_1":
#         yield get_value_range_preprocess(0, 1)
#       elif fn_name == "-1_to_1":
#         yield get_value_range_preprocess(-1, 1)
#       elif fn_name == "value_range":
#         yield get_value_range_preprocess(*map(float, args))
#       elif fn_name == "resize":
#         yield get_resize_preprocess(args, is_training)
#       elif fn_name == "resize_small":
#         yield get_resize_small(int(args[0]))
#       elif fn_name == "crop":
#         yield get_crop(is_training, utils.str2intlist(args, 2))
#       elif fn_name == "central_crop":
#         yield get_crop(False, utils.str2intlist(args, 2))
#       elif fn_name == "multi_crop":
#         yield get_multi_crop(utils.str2intlist(args, 2))
#       elif fn_name == "inception_crop":
#         yield get_inception_crop(is_training)
#       elif fn_name == "flip_lr":
#         yield get_random_flip_lr(is_training)
#       elif fn_name == "hsvnoise":
#         # TODO(lbeyer): expose the parameters? Or maybe just a scale parameter?
#         yield get_hsvnoise_preprocess(*args)
#       elif fn_name == "crop_inception_preprocess_patches":
#         npatch = int(args[0])
#         size = utils.str2intlist(args[1:], 2)
#         yield get_inception_preprocess_patches(is_training, size, npatch)
#       elif fn_name == "crop_inception_patches":
#         npatch = int(args[0])
#         size = utils.str2intlist(args[1:], 2)
#         yield get_inception_crop_patches(size, npatch)
#       elif fn_name == "to_gray":
#         yield get_to_gray_preprocess(float(get(args, 0, 1.0)))
#       elif fn_name == "standardize":
#         yield get_standardize_preprocess()
#       elif fn_name == "rotate":
#         yield get_rotate_preprocess()
#       elif fn_name == "copy_label":
#         yield get_copy_label_preprocess(get(args, 0, "copy_label"))
# 
#       # Below this line specific combos decomposed.
#       # It would be nice to move them to the configs at some point.
# 
#       elif fn_name == "inception_preprocess":
#         yield get_inception_preprocess(is_training, utils.str2intlist(args, 2))
#       else:
#         raise ValueError("Not supported preprocessing %s" % fn_name)
# 
#     # Apply all the individual steps in sequence.
#     tf.logging.info("Data before pre-processing:\n%s", data)
#     for fn_name in pp_pipeline.split("|"):
#       for p in expand(*parse_fn(fn_name.strip())):
#         data = p(data)
#         tf.logging.info("Data after `%s`:\n%s", p, data)
#     return data
# 
#   return _fn


# Path: datasets.py
from absl import flags
from tensorflow.contrib.data import AUTOTUNE
from tensorflow.contrib.lookup import index_table_from_tensor
from preprocess import get_preprocess_fn
import abc
import json
import os
import tensorflow.compat.v1 as tf

          label = tf.cond(
              tf.math.equal(label_flag, tf.constant(1, dtype=tf.int32)),
              true_fn=lambda: label_original,
              false_fn=lambda: label)
        else:
            raise ValueError('Unkown split{}'.format(self.split_name))

    return self.preprocess_fn({'image': image, 'label': label})

  def get_filter(self):  # pylint: disable=missing-docstring
    def _filter_fn(example):
      index = self.filename_list.lookup(example[self.FILENAME_KEY])
      return tf.math.greater_equal(index, 0)
    return _filter_fn


DATASET_MAP = {
    'imagenet': DatasetImagenet,
}


def get_data_batch(batch_size,  # pylint: disable=missing-docstring
                   split_name,
                   is_training,
                   preprocessing,
                   filename_list=None,
                   shuffle=True,
                   num_epochs=None,
                   drop_remainder=False):
  dataset = DATASET_MAP[FLAGS.dataset]

  preprocess_fn = get_preprocess_fn(preprocessing, is_training)