using System;
using System.Collections;
using System.Linq;
using Microsoft.VisualStudio.TestTools.UnitTesting;
using Newtonsoft.Json.Linq;
using NumSharp;
using Tensorflow;
using Tensorflow.Util;
using static Tensorflow.Python;

namespace TensorFlowNET.UnitTest
{
    /// <summary>
    /// Use as base class for test classes to get additional assertions
    /// </summary>
    public class PythonTest
    {
        #region python compatibility layer
        protected PythonTest self { get => this; }
        protected object None
        {
            get { return null; }
        }
        #endregion

        #region pytest assertions

        public void assertItemsEqual(ICollection given, ICollection expected)
        {
            if (given is Hashtable && expected is Hashtable)
            {
                Assert.AreEqual(JObject.FromObject(expected).ToString(), JObject.FromObject(given).ToString());
                return;
            }
            Assert.IsNotNull(expected);
            Assert.IsNotNull(given);
            var e = expected.OfType<object>().ToArray();
            var g = given.OfType<object>().ToArray();
            Assert.AreEqual(e.Length, g.Length, $"The collections differ in length expected {e.Length} but got {g.Length}");
            for (int i = 0; i < e.Length; i++)
            {
                if (g[i] is NDArray && e[i] is NDArray)
                    assertItemsEqual((g[i] as NDArray).GetData<object>(), (e[i] as NDArray).GetData<object>());
                else if (e[i] is ICollection && g[i] is ICollection)
                    assertEqual(g[i], e[i]);
                else
                    Assert.AreEqual(e[i], g[i], $"Items differ at index {i}, expected {e[i]} but got {g[i]}");
            }
        }

        public void assertAllEqual(ICollection given, ICollection expected)
        {
            assertItemsEqual(given, expected);
        }


        public void assertEqual(object given, object expected)
        {
            if (given is NDArray && expected is NDArray)
            {
                assertItemsEqual((given as NDArray).GetData<object>(), (expected as NDArray).GetData<object>());
                return;
            }
            if (given is Hashtable && expected is Hashtable)
            {
                Assert.AreEqual(JObject.FromObject(expected).ToString(), JObject.FromObject(given).ToString());
                return;
            }
            if (given is ICollection && expected is ICollection)
            {
                assertItemsEqual(given as ICollection, expected as ICollection);
                return;
            }
            Assert.AreEqual(expected, given);
        }

        public void assertEquals(object given, object expected)
        {
            assertEqual(given, expected);
        }

        public void assert(object given)
        {
            if (given is bool)
                Assert.IsTrue((bool)given);
            Assert.IsNotNull(given);
        }

        public void assertIsNotNone(object given)
        {
            Assert.IsNotNull(given);
        }

        public void assertFalse(bool cond)
        {
            Assert.IsFalse(cond);
        }

        public void assertTrue(bool cond)
        {
            Assert.IsTrue(cond);
        }

        public void assertAllClose(NDArray array1, NDArray array2, double eps = 1e-5)
        {
            Assert.IsTrue(np.allclose(array1, array2, rtol: eps));
        }

        public void assertAllClose(double value, NDArray array2, double eps = 1e-5)
        {
            var array1 = np.ones_like(array2) * value;
            Assert.IsTrue(np.allclose(array1, array2, rtol: eps));
        }

        public void assertProtoEquals(object toProto, object o)
        {
            throw new NotImplementedException();
        }

        #endregion

        #region tensor evaluation and test session

        protected object _eval_helper(Tensor[] tensors)
        {
            if (tensors == null)
                return null;
            return nest.map_structure(self._eval_tensor, tensors);
        }

        protected object _eval_tensor(object tensor)
        {
            if (tensor == None)
                return None;
            //else if (callable(tensor))
            //     return self._eval_helper(tensor())
            else
            {
                try
                {
                    //TODO:
                    //       if sparse_tensor.is_sparse(tensor):
                    //         return sparse_tensor.SparseTensorValue(tensor.indices, tensor.values,
                    //                                                tensor.dense_shape)
                    //return (tensor as Tensor).numpy();
                }
                catch (Exception)
                {
                    throw new ValueError("Unsupported type: " + tensor.GetType());
                }
                return null;
            }
        }

        /// <summary>
        /// This function is used in many original tensorflow unit tests to evaluate tensors 
        /// in a test session with special settings (for instance constant folding off)
        /// 
        /// </summary>
        public T evaluate<T>(Tensor tensor)
        {
            object result = null;
            //  if context.executing_eagerly():
            //    return self._eval_helper(tensors)
            //  else:
            {
                with(tf.Session(), s =>
                {
                    var ndarray=tensor.eval();
                    if (typeof(T) == typeof(double))
                    {
                        double x = ndarray;
                        result=x;
                    }
                    else if (typeof(T) == typeof(int))
                    {
                        int x = ndarray;
                        result = x;
                    }
                    else
                    {
                        result = ndarray;
                    }
                });
                return (T)result;
            }
        }


        public Session cached_session()
        {
            throw new NotImplementedException();
        }

        //Returns a TensorFlow Session for use in executing tests.
        public Session session(Graph graph = null, object config = null, bool use_gpu = false, bool force_gpu = false)
        {
            //Note that this will set this session and the graph as global defaults.

            //Use the `use_gpu` and `force_gpu` options to control where ops are run.If
            //`force_gpu` is True, all ops are pinned to `/device:GPU:0`. Otherwise, if
            //`use_gpu` is True, TensorFlow tries to run as many ops on the GPU as
            //possible.If both `force_gpu and `use_gpu` are False, all ops are pinned to
            //the CPU.

            //Example:
            //```python
            //class MyOperatorTest(test_util.TensorFlowTestCase):
            //  def testMyOperator(self):
            //    with self.session(use_gpu= True):
            //      valid_input = [1.0, 2.0, 3.0, 4.0, 5.0]
            //    result = MyOperator(valid_input).eval()
            //      self.assertEqual(result, [1.0, 2.0, 3.0, 5.0, 8.0]
            //      invalid_input = [-1.0, 2.0, 7.0]
            //    with self.assertRaisesOpError("negative input not supported"):
            //        MyOperator(invalid_input).eval()
            //```

            //Args:
            //  graph: Optional graph to use during the returned session.
            //  config: An optional config_pb2.ConfigProto to use to configure the
            //    session.
            //  use_gpu: If True, attempt to run as many ops as possible on GPU.
            //  force_gpu: If True, pin all ops to `/device:GPU:0`.

            //Yields:
            //  A Session object that should be used as a context manager to surround
            //  the graph building and execution code in a test case.

            Session s = null;
            //if (context.executing_eagerly())
            //  yield None
            //else 
            //{
            s = self._create_session(graph, config, force_gpu);
            self._constrain_devices_and_set_default(s, use_gpu, force_gpu);
            //}
            return s.as_default();
        }

        private IPython _constrain_devices_and_set_default(Session sess, bool useGpu, bool forceGpu)
        {
            //def _constrain_devices_and_set_default(self, sess, use_gpu, force_gpu):
            //"""Set the session and its graph to global default and constrain devices."""
            //if context.executing_eagerly():
            //  yield None
            //else:
            //  with sess.graph.as_default(), sess.as_default():
            //    if force_gpu:
            //      # Use the name of an actual device if one is detected, or
            //      # '/device:GPU:0' otherwise
            //      gpu_name = gpu_device_name()
            //      if not gpu_name:
            //            gpu_name = "/device:GPU:0"
            //      with sess.graph.device(gpu_name):
            //        yield sess
            //    elif use_gpu:
            //      yield sess
            //    else:
            //      with sess.graph.device("/device:CPU:0"):
            //        yield sess
            return sess;
        }

        // See session() for details.
        private Session _create_session(Graph graph, object cfg, bool forceGpu)
        {
            var prepare_config = new Func<object, object>((config) =>
            {
                //  """Returns a config for sessions.
                //  Args:
                //        config: An optional config_pb2.ConfigProto to use to configure the
                //      session.
                //  Returns:
                //    A config_pb2.ConfigProto object.

                //TODO: config

                //  # use_gpu=False. Currently many tests rely on the fact that any device
                //  # will be used even when a specific device is supposed to be used.
                //  allow_soft_placement = not force_gpu
                //  if config is None:
                //    config = config_pb2.ConfigProto()
                //    config.allow_soft_placement = allow_soft_placement
                //    config.gpu_options.per_process_gpu_memory_fraction = 0.3
                //  elif not allow_soft_placement and config.allow_soft_placement:
                //    config_copy = config_pb2.ConfigProto()
                //    config_copy.CopyFrom(config)
                //    config = config_copy
                //    config.allow_soft_placement = False
                //  # Don't perform optimizations for tests so we don't inadvertently run
                //  # gpu ops on cpu
                //  config.graph_options.optimizer_options.opt_level = -1
                //  # Disable Grappler constant folding since some tests & benchmarks
                //  # use constant input and become meaningless after constant folding.
                //  # DO NOT DISABLE GRAPPLER OPTIMIZERS WITHOUT CONSULTING WITH THE
                //  # GRAPPLER TEAM.
                //  config.graph_options.rewrite_options.constant_folding = (
                //      rewriter_config_pb2.RewriterConfig.OFF)
                //  config.graph_options.rewrite_options.pin_to_host_optimization = (
                //      rewriter_config_pb2.RewriterConfig.OFF)
                return config;
            });
            //TODO: use this instead of normal session
            //return new ErrorLoggingSession(graph = graph, config = prepare_config(config))
            return new Session(graph);//, config = prepare_config(config))
        }

        #endregion


    }
}