import tensorflow as tf

from .flag import FLAGS, FlagAdder
from .estimator import def_estimator
from .inputs import def_def_train_input_fn
from .inputs import def_def_eval_input_fn


def def_def_experiment_fn():
    adder = FlagAdder()

    works_with = lambda name: "Works only with {}".format(name)
    train_help = works_with("qnd.train_and_evaluate()")

    adder.add_flag("train_steps", type=int, help=train_help)
    adder.add_flag("eval_steps", type=int, help=works_with("qnd.evaluate()"))
    adder.add_flag("min_eval_frequency", type=int, default=1, help=train_help)

    estimator = def_estimator()
    def_train_input_fn = def_def_train_input_fn()
    def_eval_input_fn = def_def_eval_input_fn()

    def def_experiment_fn(model_fn, input_fn):
        def experiment_fn(output_dir):
            return tf.contrib.learn.Experiment(
                estimator(model_fn, output_dir),
                def_train_input_fn(input_fn),
                def_eval_input_fn(input_fn),
                **{arg: getattr(FLAGS, arg) for arg in adder.flags})

        return experiment_fn

    return def_experiment_fn

import tensorflow as tf

from .flag import FLAGS, FlagAdder
from .estimator import def_estimator
from .inputs import DataUse, def_def_train_input_fn, def_def_eval_input_fn


def def_def_experiment_fn():
    adder = FlagAdder()

    for use in DataUse:
        use = use.value
        adder.add_flag("{}_steps".format(use), type=int,
                       help="Maximum number of {} steps".format(use))

    adder.add_flag("min_eval_frequency", type=int, default=1,
                   help="Minimum evaluation frequency in number of model "
                        "savings")

    estimator = def_estimator()
    def_train_input_fn = def_def_train_input_fn()
    def_eval_input_fn = def_def_eval_input_fn()

    def def_experiment_fn(model_fn, input_fn):
        def experiment_fn(output_dir):
            return tf.contrib.learn.Experiment(
                estimator(model_fn, output_dir),
                def_train_input_fn(input_fn),
                def_eval_input_fn(input_fn),
                **{arg: getattr(FLAGS, arg) for arg in adder.flags})

        return experiment_fn

    return def_experiment_fn

Fix outdated command line help
Fix outdated command line help

  import tensorflow as tf
  
  from .flag import FLAGS, FlagAdder
  from .estimator import def_estimator
+ from .inputs import DataUse, def_def_train_input_fn, def_def_eval_input_fn
- from .inputs import def_def_train_input_fn
- from .inputs import def_def_eval_input_fn
  
  
  def def_def_experiment_fn():
      adder = FlagAdder()
  
-     works_with = lambda name: "Works only with {}".format(name)
-     train_help = works_with("qnd.train_and_evaluate()")
+     for use in DataUse:
+         use = use.value
+         adder.add_flag("{}_steps".format(use), type=int,
+                        help="Maximum number of {} steps".format(use))
  
-     adder.add_flag("train_steps", type=int, help=train_help)
-     adder.add_flag("eval_steps", type=int, help=works_with("qnd.evaluate()"))
-     adder.add_flag("min_eval_frequency", type=int, default=1, help=train_help)
+     adder.add_flag("min_eval_frequency", type=int, default=1,
+                    help="Minimum evaluation frequency in number of model "
+                         "savings")
  
      estimator = def_estimator()
      def_train_input_fn = def_def_train_input_fn()
      def_eval_input_fn = def_def_eval_input_fn()
  
      def def_experiment_fn(model_fn, input_fn):
          def experiment_fn(output_dir):
              return tf.contrib.learn.Experiment(
                  estimator(model_fn, output_dir),
                  def_train_input_fn(input_fn),
                  def_eval_input_fn(input_fn),
                  **{arg: getattr(FLAGS, arg) for arg in adder.flags})
  
          return experiment_fn
  
      return def_experiment_fn
  
Fix outdated command line help
## Code Before:
import tensorflow as tf

from .flag import FLAGS, FlagAdder
from .estimator import def_estimator
from .inputs import def_def_train_input_fn
from .inputs import def_def_eval_input_fn


def def_def_experiment_fn():
    adder = FlagAdder()

    works_with = lambda name: "Works only with {}".format(name)
    train_help = works_with("qnd.train_and_evaluate()")

    adder.add_flag("train_steps", type=int, help=train_help)
    adder.add_flag("eval_steps", type=int, help=works_with("qnd.evaluate()"))
    adder.add_flag("min_eval_frequency", type=int, default=1, help=train_help)

    estimator = def_estimator()
    def_train_input_fn = def_def_train_input_fn()
    def_eval_input_fn = def_def_eval_input_fn()

    def def_experiment_fn(model_fn, input_fn):
        def experiment_fn(output_dir):
            return tf.contrib.learn.Experiment(
                estimator(model_fn, output_dir),
                def_train_input_fn(input_fn),
                def_eval_input_fn(input_fn),
                **{arg: getattr(FLAGS, arg) for arg in adder.flags})

        return experiment_fn

    return def_experiment_fn

## Instruction:
Fix outdated command line help
## Code After:
import tensorflow as tf

from .flag import FLAGS, FlagAdder
from .estimator import def_estimator
from .inputs import DataUse, def_def_train_input_fn, def_def_eval_input_fn


def def_def_experiment_fn():
    adder = FlagAdder()

    for use in DataUse:
        use = use.value
        adder.add_flag("{}_steps".format(use), type=int,
                       help="Maximum number of {} steps".format(use))

    adder.add_flag("min_eval_frequency", type=int, default=1,
                   help="Minimum evaluation frequency in number of model "
                        "savings")

    estimator = def_estimator()
    def_train_input_fn = def_def_train_input_fn()
    def_eval_input_fn = def_def_eval_input_fn()

    def def_experiment_fn(model_fn, input_fn):
        def experiment_fn(output_dir):
            return tf.contrib.learn.Experiment(
                estimator(model_fn, output_dir),
                def_train_input_fn(input_fn),
                def_eval_input_fn(input_fn),
                **{arg: getattr(FLAGS, arg) for arg in adder.flags})

        return experiment_fn

    return def_experiment_fn
