using System;

using System.Collections;

using System.Collections.Generic;

using System.Linq;

using System.Text;

using Tensorflow.Framework.Models;

namespace Tensorflow

{

/// <summary>

/// Abstract class representing a dataset with no inputs.

/// </summary>

public class DatasetV2 : IDatasetV2

{

protected dataset_ops ops = new dataset_ops();

public Tensor variant_tensor { get; set; }

public TensorSpec[] structure { get; set; }

public TensorShape[] output_shapes => structure.Select(x => x.shape).ToArray();

public TF_DataType[] output_types => structure.Select(x => x.dtype).ToArray();

public TensorSpec[] element_spec => structure;

public IDatasetV2 take(int count = -1)

=> new TakeDataset(this, count: count);

public IDatasetV2 batch(int batch_size, bool drop_remainder = false)

=> new BatchDataset(this, batch_size, drop_remainder: drop_remainder);

public IDatasetV2 prefetch(int buffer_size = -1, int? slack_period = null)

=> new PrefetchDataset(this, buffer_size: buffer_size, slack_period: slack_period);

public IDatasetV2 repeat(int count = -1)

=> new RepeatDataset(this, count: count);

public IDatasetV2 shuffle(int buffer_size, int? seed = null, bool reshuffle_each_iteration = true)

=> new ShuffleDataset(this, buffer_size, seed: seed, reshuffle_each_iteration: reshuffle_each_iteration);

public IDatasetV2 optimize(string[] optimizations, string[] optimization_configs)

=> new OptimizeDataset(this, optimizations, optimization_configs: optimization_configs);

public IDatasetV2 model(AutotuneAlgorithm algorithm, long cpu_budget)

=> new ModelDataset(this, algorithm, cpu_budget);

public IDatasetV2 apply_options()

{

// (1) Apply threading options

var graph_rewrites = new[]

{

"map_and_batch_fusion",

"noop_elimination",

"shuffle_and_repeat_fusion"

};

var graph_rewrite_configs = new string[0];

// (2) Apply graph rewrite options

var dataset = optimize(graph_rewrites, graph_rewrite_configs);

// (3) Apply autotune options

var autotune = true;

long cpu_budget = 0;

if (autotune)

dataset = dataset.model(AutotuneAlgorithm.HILL_CLIMB, cpu_budget);

// (4) Apply stats aggregator options

return dataset;

}

public override string ToString()

=> $"{GetType().Name} shapes: ({structure[0].shape}, {structure[1].shape}), types: (tf.{structure[0].dtype.as_numpy_name()}, tf.{structure[1].dtype.as_numpy_name()})";

public IEnumerator<(Tensor, Tensor)> GetEnumerator()

{

var ownedIterator = new OwnedIterator(this);

bool stop = false;

Tensor[] results = null;

while (!stop)

{

try

{

results = ownedIterator.next();

}

catch (StopIteration)

{

stop = true;

}

yield return (results[0], results[1]);

}

}

IEnumerator IEnumerable.GetEnumerator()

{

return this.GetEnumerator();

}

}

}