# Copyright 2016 The TensorFlow 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.
# ==============================================================================

"""Model wrapper class for performing inference with a ShowAndTellModel."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np



import show_and_tell_model
from inference_utils import inference_wrapper_base


class InferenceWrapper(inference_wrapper_base.InferenceWrapperBase):
  """Model wrapper class for performing inference with a ShowAndTellModel."""

  def __init__(self):
    super(InferenceWrapper, self).__init__()
    self.beam_length = 3
    self.max_length = 20
    
    #self.listOut = np.zeros([self.max_length,self.beam_length,1024])
    self.listStateH = np.zeros([self.max_length,self.beam_length,512])
    self.listStateC = np.zeros([self.max_length,self.beam_length,512])
    
    self.seqNumber = 0

  def build_model(self, model_config):
    model = show_and_tell_model.ShowAndTellModel(model_config, mode="inference")
    model.build()
    return model

  def feed_image(self, sess, encoded_image):
    initial_state = sess.run(fetches="lstm/initial_state:0",
                             feed_dict={"image_feed:0": encoded_image})
    return initial_state

  def inference_step(self, sess, input_feed, state_feed):
    print("Input : ",np.asarray(input_feed[0]).shape,input_feed," INput state ",state_feed.shape)
    
    softmax_output, state_output,lastStateH,lastStateC = sess.run(
        fetches=["softmax:0", "lstm/state:0", "lstm/lastStateH:0", "lstm/lastStateC:0"],
        feed_dict={
            "input_feed:0": input_feed,
            "lstm/state_feed:0": state_feed,
            "lstm/state_listH:0":self.listStateH,
            "lstm/state_listC:0":self.listStateC,
            "lstm/seq_number:0":[self.seqNumber]
        })
    
    
    self.listStateH[self.seqNumber] = lastStateH
    self.listStateC[self.seqNumber] = lastStateC
    self.seqNumber+=1;
    
    
    print("so shape ",softmax_output.shape,"st shape ",state_output.shape)
    return softmax_output, state_output, None
