# Path: qnd/experiment.py
# def def_def_experiment_fn(batch_inputs=True,
#                           prepare_filename_queues=True,
#                           distributed=False):
#     adder = FlagAdder()
# 
#     for mode in [tf.contrib.learn.ModeKeys.TRAIN,
#                  tf.contrib.learn.ModeKeys.EVAL]:
#         adder.add_flag(
#             "{}_steps".format(mode),
#             type=int,
#             default=(100 if mode == tf.contrib.learn.ModeKeys.EVAL else None),
#             help="Maximum number of {} steps".format(mode))
# 
#     adder.add_flag(
#         "min_eval_frequency", type=int, default=1,
#         help="Minimum evaluation frequency in number of train steps")
# 
#     estimator = def_estimator(distributed)
#     def_train_input_fn = def_def_train_input_fn(batch_inputs,
#                                                 prepare_filename_queues)
#     def_eval_input_fn = def_def_eval_input_fn(batch_inputs,
#                                               prepare_filename_queues)
# 
#     def def_experiment_fn(model_fn,
#                           train_input_fn,
#                           eval_input_fn=None,
#                           serving_input_fn=None):
#         def experiment_fn(output_dir):
#             return tf.contrib.learn.Experiment(
#                 estimator(model_fn, output_dir),
#                 def_train_input_fn(train_input_fn),
#                 def_eval_input_fn(eval_input_fn or train_input_fn),
#                 export_strategies=(serving_input_fn and [
#                     tf.contrib.learn.make_export_strategy(serving_input_fn),
#                 ]),
#                 **adder.flags)
# 
#         return experiment_fn
# 
#     return def_experiment_fn
# 
# Path: qnd/flag.py
# FLAGS = gargparse.ARGS
# 
# def add_output_dir_flag():
#     add_flag("output_dir",
#              default="output",
#              help="Directory where checkpoint and event files are stored")


from tensorflow.contrib.learn.python.learn.learn_runner import run
from .experiment import def_def_experiment_fn
from .flag import FLAGS, add_output_dir_flag



def def_train_and_evaluate(batch_inputs=True,
                           prepare_filename_queues=True,
                           distributed=False):
    """Define `train_and_evaluate()` function.

    See also `help(def_train_and_evaluate())`.

    - Args
        - `batch_inputs`: If `True`, create batches from Tensors returned from
            `train_input_fn()` and `eval_input_fn()` and feed them to a model.
        - `prepare_filename_queues`: If `True`, create filename queues for
            train and eval data based on file paths specified by command line
            arguments.
        - `distributed`: If `True`, configure command line arguments to train
            and evaluate models on a distributed system.

    - Returns
        - `train_and_evaluate()` function.
    """

# Path: qnd/experiment.py
# def def_def_experiment_fn(batch_inputs=True,
#                           prepare_filename_queues=True,
#                           distributed=False):
#     adder = FlagAdder()
# 
#     for mode in [tf.contrib.learn.ModeKeys.TRAIN,
#                  tf.contrib.learn.ModeKeys.EVAL]:
#         adder.add_flag(
#             "{}_steps".format(mode),
#             type=int,
#             default=(100 if mode == tf.contrib.learn.ModeKeys.EVAL else None),
#             help="Maximum number of {} steps".format(mode))
# 
#     adder.add_flag(
#         "min_eval_frequency", type=int, default=1,
#         help="Minimum evaluation frequency in number of train steps")
# 
#     estimator = def_estimator(distributed)
#     def_train_input_fn = def_def_train_input_fn(batch_inputs,
#                                                 prepare_filename_queues)
#     def_eval_input_fn = def_def_eval_input_fn(batch_inputs,
#                                               prepare_filename_queues)
# 
#     def def_experiment_fn(model_fn,
#                           train_input_fn,
#                           eval_input_fn=None,
#                           serving_input_fn=None):
#         def experiment_fn(output_dir):
#             return tf.contrib.learn.Experiment(
#                 estimator(model_fn, output_dir),
#                 def_train_input_fn(train_input_fn),
#                 def_eval_input_fn(eval_input_fn or train_input_fn),
#                 export_strategies=(serving_input_fn and [
#                     tf.contrib.learn.make_export_strategy(serving_input_fn),
#                 ]),
#                 **adder.flags)
# 
#         return experiment_fn
# 
#     return def_experiment_fn
# 
# Path: qnd/flag.py
# FLAGS = gargparse.ARGS
# 
# def add_output_dir_flag():
#     add_flag("output_dir",
#              default="output",
#              help="Directory where checkpoint and event files are stored")


# Path: qnd/train_and_evaluate.py
from tensorflow.contrib.learn.python.learn.learn_runner import run
from .experiment import def_def_experiment_fn
from .flag import FLAGS, add_output_dir_flag




def def_train_and_evaluate(batch_inputs=True,
                           prepare_filename_queues=True,
                           distributed=False):
    """Define `train_and_evaluate()` function.

    See also `help(def_train_and_evaluate())`.

    - Args
        - `batch_inputs`: If `True`, create batches from Tensors returned from
            `train_input_fn()` and `eval_input_fn()` and feed them to a model.
        - `prepare_filename_queues`: If `True`, create filename queues for
            train and eval data based on file paths specified by command line
            arguments.
        - `distributed`: If `True`, configure command line arguments to train
            and evaluate models on a distributed system.

    - Returns
        - `train_and_evaluate()` function.
    """

    add_output_dir_flag()