text: #   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """
  exporters
  """
  from __future__ import print_function
  from __future__ import absolute_import
  from __future__ import unicode_literals
  
  import sys
  import os
  import itertools
  import six
  import inspect
  import abc
  import logging
  
  import numpy as np
  import paddle.fluid as F
  import paddle.fluid.layers as L
  
  from propeller.util import map_structure
  from propeller.paddle.train import Saver
  from propeller.types import InferenceSpec
  from propeller.train.model import Model
  from propeller.paddle.train.trainer import _build_net
  from propeller.paddle.train.trainer import _build_model_fn
  from propeller.types import RunMode
  from propeller.types import ProgramPair
  
  log = logging.getLogger(__name__)
  
  
  @six.add_metaclass(abc.ABCMeta)
  class Exporter(object):
      """base exporter"""
  
      @abc.abstractmethod
      def export(self, exe, program, eval_result, state):
          """export"""
          raise NotImplementedError()
  
  
  class BestExporter(Exporter):
      """export saved model accordingto `cmp_fn`"""
  
      def __init__(self, export_dir, cmp_fn):
          """doc"""
          self._export_dir = export_dir
          self._best = None
          self.cmp_fn = cmp_fn
  
      def export(self, exe, program, eval_model_spec, eval_result, state):
          """doc"""
          log.debug('New evaluate result: %s \nold: %s' %
                    (repr(eval_result), repr(self._best)))
          if self._best is None and state['best_model'] is not None:
              self._best = state['best_model']
              log.debug('restoring best state %s' % repr(self._best))
          if self._best is None or self.cmp_fn(old=self._best, new=eval_result):
              log.debug('[Best Exporter]: export to %s' % self._export_dir)
              eval_program = program.train_program
              # FIXME: all eval datasets has same name/types/shapes now!!! so every eval program are the smae
  
              saver = Saver(
                  self._export_dir, exe, program=program, max_ckpt_to_keep=1)
              saver.save(state)
              eval_result = map_structure(float, eval_result)
              self._best = eval_result
              state['best_model'] = eval_result
          else:
              log.debug('[Best Exporter]: skip step %s' % state.gstep)
  
  
  class BestInferenceModelExporter(Exporter):
      """export inference model accordingto `cmp_fn`"""
  
      def __init__(self,
                   export_dir,
                   cmp_fn,
                   model_class_or_model_fn=None,
                   hparams=None,
                   dataset=None):
          """doc"""
          self._export_dir = export_dir
          self._best = None
          self.cmp_fn = cmp_fn
          self.model_class_or_model_fn = model_class_or_model_fn
          self.hparams = hparams
          self.dataset = dataset
  
      def export(self, exe, program, eval_model_spec, eval_result, state):
          """doc"""
          if self.model_class_or_model_fn is not None and self.hparams is not None \
                  and self.dataset is not None:
              log.info('Building program by user defined model function')
              if issubclass(self.model_class_or_model_fn, Model):
                  _model_fn = _build_model_fn(self.model_class_or_model_fn)
              elif inspect.isfunction(self.model_class_or_model_fn):
                  _model_fn = self.model_class_or_model_fn
              else:
                  raise ValueError('unknown model %s' %
                                   self.model_class_or_model_fn)
  
              # build net
              infer_program = F.Program()
              startup_prog = F.Program()
              with F.program_guard(infer_program, startup_prog):
                  #share var with Train net
                  with F.unique_name.guard():
                      log.info('Building Infer Graph')
                      infer_fea = self.dataset.features()
                      # run_config is None
                      self.model_spec = _build_net(_model_fn, infer_fea,
                                                   RunMode.PREDICT, self.hparams,
                                                   None)
                      log.info('Done')
              infer_program = infer_program.clone(for_test=True)
              self.program = ProgramPair(
                  train_program=infer_program, startup_program=startup_prog)
  
          else:
              self.program = program
              self.model_spec = eval_model_spec
          if self._best is None and state['best_inf_model'] is not None:
              self._best = state['best_inf_model']
              log.debug('restoring best state %s' % repr(self._best))
          log.debug('New evaluate result: %s \nold: %s' %
                    (repr(eval_result), repr(self._best)))
  
          if self._best is None or self.cmp_fn(old=self._best, new=eval_result):
              log.debug('[Best Exporter]: export to %s' % self._export_dir)
              if self.model_spec.inference_spec is None:
                  raise ValueError('model_fn didnt return InferenceSpec')
  
              inf_spec_dict = self.model_spec.inference_spec
              if not isinstance(inf_spec_dict, dict):
                  inf_spec_dict = {'inference': inf_spec_dict}
              for inf_spec_name, inf_spec in six.iteritems(inf_spec_dict):
                  if not isinstance(inf_spec, InferenceSpec):
                      raise ValueError('unknow inference spec type: %s' %
                                       inf_spec)
  
                  save_dir = os.path.join(self._export_dir, inf_spec_name)
                  log.debug('[Best Exporter]: save inference model: "%s" to %s' %
                            (inf_spec_name, save_dir))
                  feed_var = [i.name for i in inf_spec.inputs]
                  fetch_var = inf_spec.outputs
  
                  infer_program = self.program.train_program
                  startup_prog = F.Program()
                  F.io.save_inference_model(
                      save_dir,
                      feed_var,
                      fetch_var,
                      exe,
                      main_program=infer_program)
              eval_result = map_structure(float, eval_result)
              state['best_inf_model'] = eval_result
              self._best = eval_result
          else:
              log.debug('[Best Exporter]: skip step %s' % state.gstep)
  