# Path: hyperengine/model/base_runner.py
# class BaseRunner(object):
#   """
#   The runner represents a connecting layer between the solver and the machine learning model.
#   Responsible for communicating with the model with a data batch: prepare, train, evaluate.
#   """
# 
#   def build_model(self):
#     """
#     Builds and prepares a model. Method is not expected to return anything.
#     """
#     raise NotImplementedError()
# 
#   def init(self, **kwargs):
#     """
#      Runs the model initializer.
#     """
#     raise NotImplementedError()
# 
#   def run_batch(self, batch_x, batch_y):
#     """
#     Runs the training iteration for a batch of data. Method is not expected to return anything.
#     """
#     raise NotImplementedError()
# 
#   def evaluate(self, batch_x, batch_y):
#     """
#     Evaluates the test result for a batch of data. Method should return the dictionary that contains
#     one (or all) of the following:
#     - batch accuracy (key 'accuracy')
#     - associated loss (key 'loss')
#     - any other computed data (key 'data')
#     """
#     raise NotImplementedError()
# 
#   def model_size(self):
#     """
#     Returns the model size.
#     """
#     raise NotImplementedError()
# 
# Path: hyperengine/impl/tensorflow/tf_util.py
# def graph_vars(graph):
#   for n in graph.as_graph_def().node:
#     element = graph.as_graph_element(n.name)
#     if element.type == 'Variable' or element.type == 'VariableV2':
#       yield element
# 
# def get_total_dim(element):
#   # See TensorShapeProto
#   shape = element.get_attr('shape')
#   dims = shape.dim
#   return functools.reduce(lambda x, y: x * y, [dim.size for dim in dims], 1)


import tensorflow as tf
from ...base import *
from ...model.base_runner import BaseRunner
from .tf_util import graph_vars, get_total_dim
      feed_dict[self._mode] = mode
    for k, v in self._extra_feed.items():
      if isinstance(v, dict) and mode in v.keys():
        v = v[mode]
      feed_dict[k] = v
    return feed_dict

  def _find_tensor(self, name, mandatory=True):
    try:
      return self._graph.get_tensor_by_name(name + ':0')
    except KeyError:
      if not mandatory:
        debug('Tensor not found in Tensorflow graph:', name)
        return None
      warn('Failed to infer a tensor "%s" in Tensorflow graph. '
           'Most likely, you should add "name=\'%s\'" in the placeholder/tensor definition' % (name, name))
      raise

  def _find_op(self, name, default=None):
    try:
      return self._graph.get_operation_by_name(name)
    except KeyError:
      if default is not None:
        debug('Op not found in Tensorflow graph:', name)
        return default
      warn('Failed to infer an op "%s" in Tensorflow graph. '
           'Most likely, you should add "name=\'%s\'" in the op definition' % (name, name))
      raise

  def _calc_model_size(self):

# Path: hyperengine/model/base_runner.py
# class BaseRunner(object):
#   """
#   The runner represents a connecting layer between the solver and the machine learning model.
#   Responsible for communicating with the model with a data batch: prepare, train, evaluate.
#   """
# 
#   def build_model(self):
#     """
#     Builds and prepares a model. Method is not expected to return anything.
#     """
#     raise NotImplementedError()
# 
#   def init(self, **kwargs):
#     """
#      Runs the model initializer.
#     """
#     raise NotImplementedError()
# 
#   def run_batch(self, batch_x, batch_y):
#     """
#     Runs the training iteration for a batch of data. Method is not expected to return anything.
#     """
#     raise NotImplementedError()
# 
#   def evaluate(self, batch_x, batch_y):
#     """
#     Evaluates the test result for a batch of data. Method should return the dictionary that contains
#     one (or all) of the following:
#     - batch accuracy (key 'accuracy')
#     - associated loss (key 'loss')
#     - any other computed data (key 'data')
#     """
#     raise NotImplementedError()
# 
#   def model_size(self):
#     """
#     Returns the model size.
#     """
#     raise NotImplementedError()
# 
# Path: hyperengine/impl/tensorflow/tf_util.py
# def graph_vars(graph):
#   for n in graph.as_graph_def().node:
#     element = graph.as_graph_element(n.name)
#     if element.type == 'Variable' or element.type == 'VariableV2':
#       yield element
# 
# def get_total_dim(element):
#   # See TensorShapeProto
#   shape = element.get_attr('shape')
#   dims = shape.dim
#   return functools.reduce(lambda x, y: x * y, [dim.size for dim in dims], 1)


# Path: hyperengine/impl/tensorflow/tensorflow_runner.py
import tensorflow as tf
from ...base import *
from ...model.base_runner import BaseRunner
from .tf_util import graph_vars, get_total_dim

      feed_dict[self._mode] = mode
    for k, v in self._extra_feed.items():
      if isinstance(v, dict) and mode in v.keys():
        v = v[mode]
      feed_dict[k] = v
    return feed_dict

  def _find_tensor(self, name, mandatory=True):
    try:
      return self._graph.get_tensor_by_name(name + ':0')
    except KeyError:
      if not mandatory:
        debug('Tensor not found in Tensorflow graph:', name)
        return None
      warn('Failed to infer a tensor "%s" in Tensorflow graph. '
           'Most likely, you should add "name=\'%s\'" in the placeholder/tensor definition' % (name, name))
      raise

  def _find_op(self, name, default=None):
    try:
      return self._graph.get_operation_by_name(name)
    except KeyError:
      if default is not None:
        debug('Op not found in Tensorflow graph:', name)
        return default
      warn('Failed to infer an op "%s" in Tensorflow graph. '
           'Most likely, you should add "name=\'%s\'" in the op definition' % (name, name))
      raise

  def _calc_model_size(self):

    return sum(get_total_dim(element) for element in graph_vars(self._graph))