# Path: Bunch.py
# class Bunch(object):
#   def __init__(self, **kwds):
#     self.__dict__.update(kwds)
# 
#   def __eq__(self, other):
#     return self.__dict__ == other.__dict__
# 
#   def __str__(self):
#     string = "BUNCH {" + str(self.__dict__)[2:]
#     string = string.replace("': ", ":")
#     string = string.replace(", '", ", ")
#     return string
# 
#   def __repr__(self):
#     return str(self)
# 
#   def to_file_name(self, folder=None, ext=None):
#     res = str(self.__dict__)[2:-1]
#     res = res.replace("'", "")
#     res = res.replace(": ", ".")
#     parts = res.split(', ')
#     res = '_'.join(sorted(parts))
#     
#     if ext is not None:
#       res = '%s.%s' % (res, ext)
#     if folder is not None:
#       res = os.path.join(folder, res)
#     return res


import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
import network_utils as nut
import utils as ut
import re
import os
from Bunch import Bunch
    if cfg.type in [FC, CONV]:
      input = tf.stop_gradient(cfg.ein, name='stacked_breakpoint_%d' % i)
      net = build_encoder(input, [None, model.config[i]], reuse=True)
      net = build_decoder(net, [model.config[i]], reuse=True)
      losses.append(l2_loss(input, net, name='stacked_loss_%d' % i))
  model.stacked_losses = losses


def build_losses(layer_config):
  return []


def l2_loss(arg1, arg2, alpha=1.0, name='reco_loss'):
  with tf.name_scope(name):
    loss = tf.nn.l2_loss(arg1 - arg2)
    return alpha * loss


def get_activation(descriptor):
  if 'c' not in descriptor and 'f' not in descriptor:
    return None
  activation = tf.nn.relu if 'c' in descriptor else tf.nn.sigmoid
  act_descriptor = re.search('[r|s|i|t]&', descriptor)
  if act_descriptor is None:
    return activation
  act_descriptor = act_descriptor.group(0)
  return activation_voc[act_descriptor]


def _get_cfg_dummy():

# Path: Bunch.py
# class Bunch(object):
#   def __init__(self, **kwds):
#     self.__dict__.update(kwds)
# 
#   def __eq__(self, other):
#     return self.__dict__ == other.__dict__
# 
#   def __str__(self):
#     string = "BUNCH {" + str(self.__dict__)[2:]
#     string = string.replace("': ", ":")
#     string = string.replace(", '", ", ")
#     return string
# 
#   def __repr__(self):
#     return str(self)
# 
#   def to_file_name(self, folder=None, ext=None):
#     res = str(self.__dict__)[2:-1]
#     res = res.replace("'", "")
#     res = res.replace(": ", ".")
#     parts = res.split(', ')
#     res = '_'.join(sorted(parts))
#     
#     if ext is not None:
#       res = '%s.%s' % (res, ext)
#     if folder is not None:
#       res = os.path.join(folder, res)
#     return res


# Path: model_interpreter.py
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
import network_utils as nut
import utils as ut
import re
import os
from Bunch import Bunch

    if cfg.type in [FC, CONV]:
      input = tf.stop_gradient(cfg.ein, name='stacked_breakpoint_%d' % i)
      net = build_encoder(input, [None, model.config[i]], reuse=True)
      net = build_decoder(net, [model.config[i]], reuse=True)
      losses.append(l2_loss(input, net, name='stacked_loss_%d' % i))
  model.stacked_losses = losses


def build_losses(layer_config):
  return []


def l2_loss(arg1, arg2, alpha=1.0, name='reco_loss'):
  with tf.name_scope(name):
    loss = tf.nn.l2_loss(arg1 - arg2)
    return alpha * loss


def get_activation(descriptor):
  if 'c' not in descriptor and 'f' not in descriptor:
    return None
  activation = tf.nn.relu if 'c' in descriptor else tf.nn.sigmoid
  act_descriptor = re.search('[r|s|i|t]&', descriptor)
  if act_descriptor is None:
    return activation
  act_descriptor = act_descriptor.group(0)
  return activation_voc[act_descriptor]


def _get_cfg_dummy():

  return Bunch(enc_op_name=None, dec_op_name=None)