text: # Copyright 2019 Huawei Technologies Co., Ltd
  #
  # 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.
  # ==============================================================================
  """
  This is the test module for mindrecord
  """
  import collections
  import json
  import os
  import re
  import string
  
  import mindspore.dataset.transforms.vision.c_transforms as vision
  import numpy as np
  import pytest
  from mindspore.dataset.transforms.vision import Inter
  from mindspore import log as logger
  
  import mindspore.dataset as ds
  from mindspore.mindrecord import FileWriter
  
  FILES_NUM = 4
  CV_FILE_NAME = "../data/mindrecord/imagenet.mindrecord"
  CV_DIR_NAME = "../data/mindrecord/testImageNetData"
  
  
  @pytest.fixture
  def add_and_remove_cv_file():
      """add/remove cv file"""
      paths = ["{}{}".format(CV_FILE_NAME, str(x).rjust(1, '0'))
               for x in range(FILES_NUM)]
      for x in paths:
          if os.path.exists("{}".format(x)):
              os.remove("{}".format(x))
          if os.path.exists("{}.db".format(x)):
              os.remove("{}.db".format(x))
      writer = FileWriter(CV_FILE_NAME, FILES_NUM)
      data = get_data(CV_DIR_NAME, True)
      cv_schema_json = {"id": {"type": "int32"},
                        "file_name": {"type": "string"},
                        "label": {"type": "int32"},
                        "data": {"type": "bytes"}}
      writer.add_schema(cv_schema_json, "img_schema")
      writer.add_index(["file_name", "label"])
      writer.write_raw_data(data)
      writer.commit()
      yield "yield_cv_data"
      for x in paths:
          os.remove("{}".format(x))
          os.remove("{}.db".format(x))
  
  def test_cv_minddataset_pk_sample_no_column(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      num_readers = 4
      sampler = ds.PKSampler(2)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", None, num_readers,
                                sampler=sampler)
  
      assert data_set.get_dataset_size() == 6
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info("-------------- item[file_name]: \
                  {}------------------------".format("".join([chr(x) for x in item["file_name"]])))
          logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
  def test_cv_minddataset_pk_sample_basic(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      sampler = ds.PKSampler(2)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
  
      assert data_set.get_dataset_size() == 6
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info("-------------- item[file_name]: \
                  {}------------------------".format("".join([chr(x) for x in item["file_name"]])))
          logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
  
      
  def test_cv_minddataset_pk_sample_shuffle(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      sampler = ds.PKSampler(3, None, True)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
  
      assert data_set.get_dataset_size() == 9
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info("-------------- item[file_name]: \
                  {}------------------------".format("".join([chr(x) for x in item["file_name"]])))
          logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
  
  
  def test_cv_minddataset_pk_sample_out_of_range(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      sampler = ds.PKSampler(5, None, True)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
      assert data_set.get_dataset_size() == 15
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info("-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info("-------------- item[file_name]: \
                  {}------------------------".format("".join([chr(x) for x in item["file_name"]])))
          logger.info("-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
  
  
  def test_cv_minddataset_subset_random_sample_basic(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      indices = [1, 2, 3, 5, 7]
      sampler = ds.SubsetRandomSampler(indices)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
      assert data_set.get_dataset_size() == 5
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info(
              "-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info(
              "-------------- item[data]: {}  -----------------------------".format(item["data"]))
          logger.info(
              "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
          logger.info(
              "-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
      assert num_iter == 5
  
  
  def test_cv_minddataset_subset_random_sample_replica(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      indices = [1, 2, 2, 5, 7, 9]
      sampler = ds.SubsetRandomSampler(indices)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
      assert data_set.get_dataset_size() == 6 
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info(
              "-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info(
              "-------------- item[data]: {}  -----------------------------".format(item["data"]))
          logger.info(
              "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
          logger.info(
              "-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
      assert num_iter == 6
  
  
  def test_cv_minddataset_subset_random_sample_empty(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      indices = []
      sampler = ds.SubsetRandomSampler(indices)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
      assert data_set.get_dataset_size() == 0
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info(
              "-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info(
              "-------------- item[data]: {}  -----------------------------".format(item["data"]))
          logger.info(
              "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
          logger.info(
              "-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
      assert num_iter == 0
  
  
  def test_cv_minddataset_subset_random_sample_out_of_range(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      indices = [1, 2, 4, 11, 13]
      sampler = ds.SubsetRandomSampler(indices)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
      assert data_set.get_dataset_size() == 5 
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info(
              "-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info(
              "-------------- item[data]: {}  -----------------------------".format(item["data"]))
          logger.info(
              "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
          logger.info(
              "-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
      assert num_iter == 5
  
  
  def test_cv_minddataset_subset_random_sample_negative(add_and_remove_cv_file):
      """tutorial for cv minderdataset."""
      columns_list = ["data", "file_name", "label"]
      num_readers = 4
      indices = [1, 2, 4, -1, -2]
      sampler = ds.SubsetRandomSampler(indices)
      data_set = ds.MindDataset(CV_FILE_NAME + "0", columns_list, num_readers,
                                sampler=sampler)
      assert data_set.get_dataset_size() == 5
      num_iter = 0
      for item in data_set.create_dict_iterator():
          logger.info(
              "-------------- cv reader basic: {} ------------------------".format(num_iter))
          logger.info(
              "-------------- item[data]: {}  -----------------------------".format(item["data"]))
          logger.info(
              "-------------- item[file_name]: {} ------------------------".format(item["file_name"]))
          logger.info(
              "-------------- item[label]: {} ----------------------------".format(item["label"]))
          num_iter += 1
      assert num_iter == 5
  
  
  def get_data(dir_name, sampler=False):
      """
      usage: get data from imagenet dataset
      params:
      dir_name: directory containing folder images and annotation information
  
      """
      if not os.path.isdir(dir_name):
          raise IOError("Directory {} not exists".format(dir_name))
      img_dir = os.path.join(dir_name, "images")
      if sampler:
          ann_file = os.path.join(dir_name, "annotation_sampler.txt")
      else:
          ann_file = os.path.join(dir_name, "annotation.txt")
      with open(ann_file, "r") as file_reader:
          lines = file_reader.readlines()
  
      data_list = []
      for i, line in enumerate(lines):
          try:
              filename, label = line.split(",")
              label = label.strip("\n")
              with open(os.path.join(img_dir, filename), "rb") as file_reader:
                  img = file_reader.read()
              data_json = {"id": i,
                           "file_name": filename,
                           "data": img,
                           "label": int(label)}
              data_list.append(data_json)
          except FileNotFoundError:
              continue
      return data_list
  