# Path: qnd/flag.py
# class FlagAdder:
#     """Manage addition of flags."""
# 
#     def __init__(self):
#         """Create a `FlagAdder` instance."""
#         self._flags = []
# 
#     def add_flag(self, name, *args, **kwargs):
#         """Add a flag.
# 
#         See `add_flag()`.
#         """
#         add_flag(name, *args, **kwargs)
#         self._flags.append(kwargs.get("dest") or name)
# 
#     def add_required_flag(self, name, *args, **kwargs):
#         """Add a required flag.
# 
#         See `add_required_flag()`.
#         """
#         self.add_flag(name, *args, required=True, **kwargs)
# 
#     @property
#     def flags(self):
#         """Get added flags.
# 
#         - Returns
#             - `dict` of flag names to values added by a `FlagAdder` instance.
#         """
#         return {flag: getattr(FLAGS, flag) for flag in self._flags}
# 
# Path: qnd/estimator.py
# def def_estimator(distributed=False):
#     config = def_config(distributed)
# 
#     @util.func_scope
#     def estimator(model_fn, model_dir):
#         return tf.contrib.learn.Estimator(_wrap_model_fn(model_fn),
#                                           config=config(),
#                                           model_dir=model_dir)
# 
#     return estimator
# 
# Path: qnd/inputs.py
# DEFAULT_BATCH_SIZE = 64
# MODES = [tf.contrib.learn.ModeKeys.TRAIN,
#          tf.contrib.learn.ModeKeys.EVAL,
#          tf.contrib.learn.ModeKeys.INFER]
# def _add_file_flag(mode):
# def def_def_def_input_fn(mode):
#     def def_def_input_fn(batch_inputs=True, prepare_filename_queues=True):
#         def def_input_fn(user_input_fn):
#             def input_fn():
# def _batch_inputs(inputs, mode):
# def _batch_merged_inputs(inputs, mode):
# def _shuffle(inputs, capacity, min_after_dequeue, num_threads):
# def _merge_dicts(*dicts):
# def _check_inputs(inputs):
# def def_filenames_to_queue(mode):
#     def filenames_to_queue(filenames):


import tensorflow as tf
from .flag import FlagAdder
from .estimator import def_estimator
from .inputs import def_def_train_input_fn, def_def_eval_input_fn



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)

# Path: qnd/flag.py
# class FlagAdder:
#     """Manage addition of flags."""
# 
#     def __init__(self):
#         """Create a `FlagAdder` instance."""
#         self._flags = []
# 
#     def add_flag(self, name, *args, **kwargs):
#         """Add a flag.
# 
#         See `add_flag()`.
#         """
#         add_flag(name, *args, **kwargs)
#         self._flags.append(kwargs.get("dest") or name)
# 
#     def add_required_flag(self, name, *args, **kwargs):
#         """Add a required flag.
# 
#         See `add_required_flag()`.
#         """
#         self.add_flag(name, *args, required=True, **kwargs)
# 
#     @property
#     def flags(self):
#         """Get added flags.
# 
#         - Returns
#             - `dict` of flag names to values added by a `FlagAdder` instance.
#         """
#         return {flag: getattr(FLAGS, flag) for flag in self._flags}
# 
# Path: qnd/estimator.py
# def def_estimator(distributed=False):
#     config = def_config(distributed)
# 
#     @util.func_scope
#     def estimator(model_fn, model_dir):
#         return tf.contrib.learn.Estimator(_wrap_model_fn(model_fn),
#                                           config=config(),
#                                           model_dir=model_dir)
# 
#     return estimator
# 
# Path: qnd/inputs.py
# DEFAULT_BATCH_SIZE = 64
# MODES = [tf.contrib.learn.ModeKeys.TRAIN,
#          tf.contrib.learn.ModeKeys.EVAL,
#          tf.contrib.learn.ModeKeys.INFER]
# def _add_file_flag(mode):
# def def_def_def_input_fn(mode):
#     def def_def_input_fn(batch_inputs=True, prepare_filename_queues=True):
#         def def_input_fn(user_input_fn):
#             def input_fn():
# def _batch_inputs(inputs, mode):
# def _batch_merged_inputs(inputs, mode):
# def _shuffle(inputs, capacity, min_after_dequeue, num_threads):
# def _merge_dicts(*dicts):
# def _check_inputs(inputs):
# def def_filenames_to_queue(mode):
#     def filenames_to_queue(filenames):


# Path: qnd/experiment.py
import tensorflow as tf
from .flag import FlagAdder
from .estimator import def_estimator
from .inputs import def_def_train_input_fn, def_def_eval_input_fn




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,