text: #copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
  #
  #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 __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle.fluid as fluid
  import os
  import sys
  import math
  import distutils.util
  import numpy as np
  import inspect
  import matplotlib
  import six
  matplotlib.use('agg')
  import matplotlib.pyplot as plt
  import matplotlib.gridspec as gridspec
  import copy
  from PIL import Image
  
  img_dim = 28
  
  
  def plot(gen_data):
      pad_dim = 1
      paded = pad_dim + img_dim
      gen_data = gen_data.reshape(gen_data.shape[0], img_dim, img_dim)
      n = int(math.ceil(math.sqrt(gen_data.shape[0])))
      gen_data = (np.pad(
          gen_data, [[0, n * n - gen_data.shape[0]], [pad_dim, 0], [pad_dim, 0]],
          'constant').reshape((n, n, paded, paded)).transpose((0, 2, 1, 3))
                  .reshape((n * paded, n * paded)))
      fig = plt.figure(figsize=(8, 8))
      plt.axis('off')
      plt.imshow(gen_data, cmap='Greys_r', vmin=-1, vmax=1)
      return fig
  
  
  def checkpoints(epoch, cfg, exe, trainer, name):
      output_path = os.path.join(cfg.output, 'checkpoints', str(epoch))
      if not os.path.exists(output_path):
          os.makedirs(output_path)
      fluid.io.save_persistables(
          exe, os.path.join(output_path, name), main_program=trainer.program)
      print('save checkpoints {} to {}'.format(name, output_path))
      sys.stdout.flush()
  
  
  def init_checkpoints(cfg, exe, trainer, name):
      assert os.path.exists(cfg.init_model), "{} cannot be found.".format(
          cfg.init_model)
      fluid.io.load_persistables(
          exe, os.path.join(cfg.init_model, name), main_program=trainer.program)
      print('load checkpoints {} {} DONE'.format(cfg.init_model, name))
      sys.stdout.flush()
  
  
  ### the initialize checkpoint is one file named checkpoint.pdparams
  def init_from_checkpoint(args, exe, trainer, name):
      if not os.path.exists(args.init_model):
          raise Warning("the checkpoint path does not exist.")
          return False
  
      fluid.io.load_persistables(
          executor=exe,
          dirname=os.path.join(args.init_model, name),
          main_program=trainer.program,
          filename="checkpoint.pdparams")
  
      print("finish initing model from checkpoint from %s" % (args.init_model))
  
      return True
  
  
  ### save the parameters of generator to one file
  def save_param(args, exe, program, dirname, var_name="generator"):
  
      param_dir = os.path.join(args.output, 'infer_vars')
  
      if not os.path.exists(param_dir):
          os.makedirs(param_dir)
  
      def _name_has_generator(var):
          res = (fluid.io.is_parameter(var) and var.name.startswith(var_name))
          print(var.name, res)
          return res
  
      fluid.io.save_vars(
          exe,
          os.path.join(param_dir, dirname),
          main_program=program,
          predicate=_name_has_generator,
          filename="params.pdparams")
      print("save parameters at %s" % (os.path.join(param_dir, dirname)))
  
      return True
  
  
  ### save the checkpoint to one file
  def save_checkpoint(epoch, args, exe, trainer, dirname):
  
      checkpoint_dir = os.path.join(args.output, 'checkpoints', str(epoch))
  
      if not os.path.exists(checkpoint_dir):
          os.makedirs(checkpoint_dir)
  
      fluid.io.save_persistables(
          exe,
          os.path.join(checkpoint_dir, dirname),
          main_program=trainer.program,
          filename="checkpoint.pdparams")
  
      print("save checkpoint at %s" % (os.path.join(checkpoint_dir, dirname)))
  
      return True
  
  
  def save_test_image(epoch,
                      cfg,
                      exe,
                      place,
                      test_program,
                      g_trainer,
                      A_test_reader,
                      B_test_reader=None,
                      A_id2name=None,
                      B_id2name=None):
      out_path = os.path.join(cfg.output, 'test')
      if not os.path.exists(out_path):
          os.makedirs(out_path)
      if cfg.model_net == "Pix2pix":
          for data in A_test_reader():
              A_data, B_data, image_name = data[0]['input_A'], data[0][
                  'input_B'], data[0]['image_name']
              fake_B_temp = exe.run(test_program,
                                    fetch_list=[g_trainer.fake_B],
                                    feed={"input_A": A_data,
                                          "input_B": B_data})
              fake_B_temp = np.squeeze(fake_B_temp[0]).transpose([1, 2, 0])
              input_A_temp = np.squeeze(np.array(A_data)[0]).transpose([1, 2, 0])
              input_B_temp = np.squeeze(np.array(B_data)[0]).transpose([1, 2, 0])
  
              fakeB_name = "fakeB_" + str(epoch) + "_" + A_id2name[np.array(
                  image_name).astype('int32')[0]]
              inputA_name = "inputA_" + str(epoch) + "_" + A_id2name[np.array(
                  image_name).astype('int32')[0]]
              inputB_name = "inputB_" + str(epoch) + "_" + A_id2name[np.array(
                  image_name).astype('int32')[0]]
  
              res_fakeB = Image.fromarray(((fake_B_temp + 1) * 127.5).astype(
                  np.uint8))
              res_fakeB.save(os.path.join(out_path, fakeB_name))
  
              res_inputA = Image.fromarray(((input_A_temp + 1) * 127.5).astype(
                  np.uint8))
              res_inputA.save(os.path.join(out_path, inputA_name))
  
              res_inputB = Image.fromarray(((input_B_temp + 1) * 127.5).astype(
                  np.uint8))
              res_inputB.save(os.path.join(out_path, inputB_name))
      elif cfg.model_net == "SPADE":
          for data in A_test_reader():
              data_A, data_B, data_C, name = data[0]['input_label'], data[0][
                  'input_img'], data[0]['input_ins'], data[0]['image_name']
              fake_B_temp = exe.run(test_program,
                                    fetch_list=[g_trainer.fake_B],
                                    feed={
                                        "input_label": data_A,
                                        "input_img": data_B,
                                        "input_ins": data_C
                                    })
              fake_B_temp = np.squeeze(fake_B_temp[0]).transpose([1, 2, 0])
              input_B_temp = np.squeeze(data_B[0]).transpose([1, 2, 0])
              image_name = A_id2name[np.array(name).astype('int32')[0]]
  
              res_fakeB = Image.fromarray(((fake_B_temp + 1) * 127.5).astype(
                  np.uint8))
              res_fakeB.save(out_path + "/fakeB_" + str(epoch) + "_" + image_name)
              res_real = Image.fromarray(((input_B_temp + 1) * 127.5).astype(
                  np.uint8))
              res_real.save(out_path + "/real_" + str(epoch) + "_" + image_name)
      elif cfg.model_net == "StarGAN":
          for data in A_test_reader():
              real_img, label_org, label_trg, image_name = data[0][
                  'image_real'], data[0]['label_org'], data[0]['label_trg'], data[
                      0]['image_name']
              attr_names = cfg.selected_attrs.split(',')
              real_img_temp = save_batch_image(np.array(real_img))
              images = [real_img_temp]
              for i in range(cfg.c_dim):
                  label_trg_tmp = copy.deepcopy(np.array(label_org))
                  for j in range(len(np.array(label_org))):
                      label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
                      np_label_trg = check_attribute_conflict(
                          label_trg_tmp, attr_names[i], attr_names)
                  label_trg.set(np_label_trg, place)
                  fake_temp, rec_temp = exe.run(
                      test_program,
                      feed={
                          "image_real": real_img,
                          "label_org": label_org,
                          "label_trg": label_trg
                      },
                      fetch_list=[g_trainer.fake_img, g_trainer.rec_img])
                  fake_temp = save_batch_image(fake_temp)
                  rec_temp = save_batch_image(rec_temp)
                  images.append(fake_temp)
                  images.append(rec_temp)
              images_concat = np.concatenate(images, 1)
              if len(np.array(label_org)) > 1:
                  images_concat = np.concatenate(images_concat, 1)
              image_name_save = "fake_img" + str(epoch) + "_" + str(
                  np.array(image_name)[0].astype('int32')) + '.jpg'
  
              res = Image.fromarray(((images_concat + 1) * 127.5).astype(
                  np.uint8))
              res.save(os.path.join(out_path, image_name_save))
  
      elif cfg.model_net == 'AttGAN' or cfg.model_net == 'STGAN':
          for data in A_test_reader():
              real_img, label_org, label_trg, image_name = data[0][
                  'image_real'], data[0]['label_org'], data[0]['label_trg'], data[
                      0]['image_name']
              attr_names = cfg.selected_attrs.split(',')
              real_img_temp = save_batch_image(np.array(real_img))
              images = [real_img_temp]
              for i in range(cfg.c_dim):
                  label_trg_tmp = copy.deepcopy(np.array(label_trg))
  
                  for j in range(len(label_trg_tmp)):
                      label_trg_tmp[j][i] = 1.0 - label_trg_tmp[j][i]
                      label_trg_tmp = check_attribute_conflict(
                          label_trg_tmp, attr_names[i], attr_names)
  
                  label_org_tmp = list(
                      map(lambda x: ((x * 2) - 1) * 0.5, np.array(label_org)))
                  label_trg_tmp = list(
                      map(lambda x: ((x * 2) - 1) * 0.5, label_trg_tmp))
  
                  if cfg.model_net == 'AttGAN':
                      for k in range(len(label_trg_tmp)):
                          label_trg_tmp[k][i] = label_trg_tmp[k][i] * 2.0
                  tensor_label_org_ = fluid.LoDTensor()
                  tensor_label_org_.set(label_org_tmp, place)
                  tensor_label_trg_ = fluid.LoDTensor()
                  tensor_label_trg_.set(label_trg_tmp, place)
  
                  out = exe.run(test_program,
                                feed={
                                    "image_real": real_img,
                                    "label_org": label_org,
                                    "label_org_": tensor_label_org_,
                                    "label_trg": label_trg,
                                    "label_trg_": tensor_label_trg_
                                },
                                fetch_list=[g_trainer.fake_img])
                  fake_temp = save_batch_image(out[0])
                  images.append(fake_temp)
              images_concat = np.concatenate(images, 1)
              if len(label_trg_tmp) > 1:
                  images_concat = np.concatenate(images_concat, 1)
              image_name_save = 'fake_img_' + str(epoch) + '_' + str(
                  np.array(image_name)[0].astype('int32')) + '.jpg'
  
              res = Image.fromarray(((images_concat + 1) * 127.5).astype(
                  np.uint8))
              res.save(os.path.join(out_path, image_name_save))
  
      else:
          for data_A, data_B in zip(A_test_reader(), B_test_reader()):
              A_data, A_name = data_A[0]['input_A'], data_A[0]['A_image_name']
              B_data, B_name = data_B[0]['input_B'], data_B[0]['B_image_name']
              fake_A_temp, fake_B_temp, cyc_A_temp, cyc_B_temp = exe.run(
                  test_program,
                  fetch_list=[
                      g_trainer.fake_A, g_trainer.fake_B, g_trainer.cyc_A,
                      g_trainer.cyc_B
                  ],
                  feed={"input_A": A_data,
                        "input_B": B_data})
              fake_A_temp = np.squeeze(fake_A_temp[0]).transpose([1, 2, 0])
              fake_B_temp = np.squeeze(fake_B_temp[0]).transpose([1, 2, 0])
              cyc_A_temp = np.squeeze(cyc_A_temp[0]).transpose([1, 2, 0])
              cyc_B_temp = np.squeeze(cyc_B_temp[0]).transpose([1, 2, 0])
              input_A_temp = np.squeeze(np.array(A_data)).transpose([1, 2, 0])
              input_B_temp = np.squeeze(np.array(B_data)).transpose([1, 2, 0])
  
              fakeA_name = "fakeA_" + str(epoch) + "_" + A_id2name[np.array(
                  A_name).astype('int32')[0]]
              fakeB_name = "fakeB_" + str(epoch) + "_" + B_id2name[np.array(
                  B_name).astype('int32')[0]]
              inputA_name = "inputA_" + str(epoch) + "_" + A_id2name[np.array(
                  A_name).astype('int32')[0]]
              inputB_name = "inputB_" + str(epoch) + "_" + B_id2name[np.array(
                  B_name).astype('int32')[0]]
              cycA_name = "cycA_" + str(epoch) + "_" + A_id2name[np.array(
                  A_name).astype('int32')[0]]
              cycB_name = "cycB_" + str(epoch) + "_" + B_id2name[np.array(
                  B_name).astype('int32')[0]]
  
              res_fakeB = Image.fromarray(((fake_B_temp + 1) * 127.5).astype(
                  np.uint8))
              res_fakeB.save(os.path.join(out_path, fakeB_name))
  
              res_fakeA = Image.fromarray(((fake_A_temp + 1) * 127.5).astype(
                  np.uint8))
              res_fakeA.save(os.path.join(out_path, fakeA_name))
  
              res_cycA = Image.fromarray(((cyc_A_temp + 1) * 127.5).astype(
                  np.uint8))
              res_cycA.save(os.path.join(out_path, cycA_name))
  
              res_cycB = Image.fromarray(((cyc_B_temp + 1) * 127.5).astype(
                  np.uint8))
              res_cycB.save(os.path.join(out_path, cycB_name))
  
              res_inputA = Image.fromarray(((input_A_temp + 1) * 127.5).astype(
                  np.uint8))
              res_inputA.save(os.path.join(out_path, inputA_name))
  
              res_inputB = Image.fromarray(((input_B_temp + 1) * 127.5).astype(
                  np.uint8))
              res_inputB.save(os.path.join(out_path, inputB_name))
  
  
  class ImagePool(object):
      def __init__(self, pool_size=50):
          self.pool = []
          self.count = 0
          self.pool_size = pool_size
  
      def pool_image(self, image):
          if self.count < self.pool_size:
              self.pool.append(image)
              self.count += 1
              return image
          else:
              p = np.random.rand()
              if p > 0.5:
                  random_id = np.random.randint(0, self.pool_size - 1)
                  temp = self.pool[random_id]
                  self.pool[random_id] = image
                  return temp
              else:
                  return image
  
  
  def check_attribute_conflict(label_batch, attr, attrs):
      ''' Based on https://github.com/LynnHo/AttGAN-Tensorflow'''
  
      def _set(label, value, attr):
          if attr in attrs:
              label[attrs.index(attr)] = value
  
      attr_id = attrs.index(attr)
      for label in label_batch:
          if attr in ['Bald', 'Receding_Hairline'] and attrs[attr_id] != 0:
              _set(label, 0, 'Bangs')
          elif attr == 'Bangs' and attrs[attr_id] != 0:
              _set(label, 0, 'Bald')
              _set(label, 0, 'Receding_Hairline')
          elif attr in ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Gray_Hair'
                        ] and attrs[attr_id] != 0:
              for a in ['Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Gray_Hair']:
                  if a != attr:
                      _set(label, 0, a)
          elif attr in ['Straight_Hair', 'Wavy_Hair'] and attrs[attr_id] != 0:
              for a in ['Straight_Hair', 'Wavy_Hair']:
                  if a != attr:
                      _set(label, 0, a)
      return label_batch
  
  
  def save_batch_image(img):
      #if img.shape[0] == 1:
      if len(img) == 1:
          res_img = np.squeeze(img).transpose([1, 2, 0])
      else:
          res_img = np.squeeze(img).transpose([0, 2, 3, 1])
      return res_img
  
  
  def check_gpu(use_gpu):
      """
       Log error and exit when set use_gpu=true in paddlepaddle
       cpu version.
       """
      err = "Config use_gpu cannot be set as true while you are " \
            "using paddlepaddle cpu version ! \nPlease try: \n" \
            "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
            "\t2. Set use_gpu as false in config file to run " \
            "model on CPU"
  
      try:
          if use_gpu and not fluid.is_compiled_with_cuda():
              print(err)
              sys.exit(1)
      except Exception as e:
          pass
  
  
  def check_version():
      """
      Log error and exit when the installed version of paddlepaddle is
      not satisfied.
      """
      err = "PaddlePaddle version 1.6 or higher is required, " \
            "or a suitable develop version is satisfied as well. \n" \
            "Please make sure the version is good with your code." \
  
      try:
          fluid.require_version('1.6.0')
      except Exception as e:
          print(err)
          sys.exit(1)
  
  def get_device_num(args):
      if args.use_gpu:
          gpus = os.environ.get("CUDA_VISIBLE_DEVICES", 1)
          gpu_num = len(gpus.split(','))
          return gpu_num
      else:
          cpu_num = os.environ.get("CPU_NUM", 1)
          return int(cpu_num)
  