text: #   Copyright (c) 2018 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.
  
  from paddle.fluid.proto import data_feed_pb2
  from google.protobuf import text_format
  
  __all__ = ['DataFeedDesc']
  
  
  class DataFeedDesc(object):
      """
      Datafeed descriptor, describing input training data format. This class is
      currently only used for AsyncExecutor (See comments for class AsyncExecutor
      for a brief introduction)
  
      DataFeedDesc shall be initialized from a valid protobuf message from disk:
      >>> data_feed = fluid.DataFeedDesc('data.proto')
  
      See :code:`paddle/fluid/framework/data_feed.proto` for message definition.
      A typical message might look like:
  
      >>> name: "MultiSlotDataFeed"
      >>> batch_size: 2
      >>> multi_slot_desc {
      >>>     slots {
      >>>         name: "words"
      >>>         type: "uint64"
      >>>         is_dense: false
      >>>         is_used: true
      >>>     }
      >>>     slots {
      >>>         name: "label"
      >>>         type: "uint64"
      >>>         is_dense: false
      >>>         is_used: true
      >>>     }
      >>> }
  
      However, users usually shouldn't care about the message format; instead,
      they are encouragd to use :code:`Data Generator` as a tool to generate a
      valid data description, in the process of converting their raw log files to
      training files acceptable to AsyncExecutor.
  
      DataFeedDesc can also be changed during runtime. Once you got familiar with
      what each field mean, you can modify it to better suit your need. E.g.:
      >>> data_feed.set_batch_size(128)
      >>> data_feed.set_dense_slots('wd')  # The slot named 'wd' will be dense
      >>> data_feed.set_use_slots('wd')    # The slot named 'wd' will be used
  
      Finally, the content can be dumped out for debugging purpose:
      >>> print(data_feed.desc())
  
      Args:
          proto_file(string): Disk file containing a data feed description.
      
      """
  
      def __init__(self, proto_file):
          self.proto_desc = data_feed_pb2.DataFeedDesc()
          with open(proto_file, 'r') as f:
              text_format.Parse(f.read(), self.proto_desc)
          if self.proto_desc.name == "MultiSlotDataFeed":
              self.__name_to_index = {
                  slot.name: i
                  for i, slot in enumerate(self.proto_desc.multi_slot_desc.slots)
              }
  
      def set_batch_size(self, batch_size):
          """
          Set batch size. Will be effective during training
  
          Example:
              >>> data_feed = fluid.DataFeedDesc('data.proto')
              >>> data_feed.set_batch_size(128)
  
          Args:
              batch_size: batch size
  
          """
          self.proto_desc.batch_size = batch_size
  
      def set_dense_slots(self, dense_slots_name):
          """
          Set if a specific slot will be dense. Will be effective during training.
          features for a dense slot will be fed into a Tensor, while those for a
          sparse slot will be fed into a LoDTensor
  
          Example:
              >>> data_feed = fluid.DataFeedDesc('data.proto')
              >>> data_feed.set_dense_slots(['words'])
  
          Args:
              dense_slots_name: a list of slot names which will be set dense
  
          Note:
              Default is sparse for all slots
          """
          if self.proto_desc.name != "MultiSlotDataFeed":
              raise ValueError(
                  "Only MultiSlotDataFeed need set_dense_slots, pls check your datafeed.proto"
              )
          for name in dense_slots_name:
              self.proto_desc.multi_slot_desc.slots[self.__name_to_index[
                  name]].is_dense = True
  
      def set_use_slots(self, use_slots_name):
          """
          Set if a specific slot will be used for training. A dataset shall
          contain a lot of features, through this function one can select which
          ones will be used for a specific model.
  
          Example:
              >>> data_feed = fluid.DataFeedDesc('data.proto')
              >>> data_feed.set_use_slots(['words'])
  
          Args:
              use_slots_name: a list of slot names which will be used in training
  
          Note:
              Default is not used for all slots
          """
          if self.proto_desc.name != "MultiSlotDataFeed":
              raise ValueError(
                  "Only MultiSlotDataFeed need set_use_slots, pls check your datafeed.proto"
              )
          for name in use_slots_name:
              self.proto_desc.multi_slot_desc.slots[self.__name_to_index[
                  name]].is_used = True
  
      def desc(self):
          """
          Returns a protobuf message for this DataFeedDesc
  
          Example:
              >>> data_feed = fluid.DataFeedDesc('data.proto')
              >>> print(data_feed.desc())
  
          Returns:
              A string message
          """
          return text_format.MessageToString(self.proto_desc)
  