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// TODO: move to backend, into graph/
#pragma once
#include "common/config.h"
#include "tensors/tensor.h"
#include <functional>
#include <random>
namespace marian {
typedef std::function<void(Tensor)> NodeInitializer;
namespace inits {
void zeros(Tensor t);
void ones(Tensor t);
NodeInitializer from_value(float v);
NodeInitializer eye(float val = 1.f);
NodeInitializer normal(float mean = 0.f, float stddev = 1.f);
NodeInitializer uniform(float a = 0.f, float b = 1.f);
void glorot_uniform(Tensor t);
NodeInitializer glorot_uniform2(bool fanIn = true, bool fanOut = true);
void glorot_normal(Tensor t);
NodeInitializer glorot_normal2(bool fanIn = true, bool fanOut = true);
NodeInitializer bernoulli(float p, float scale = 1.f);
NodeInitializer dropout(float dropProb);
void gumbel(Tensor t);
static inline void dummy(Tensor) {}
NodeInitializer from_vector(const std::vector<float>& v);
NodeInitializer from_vector(const std::vector<IndexType>& v);
NodeInitializer from_item(const io::Item& item);
NodeInitializer from_sparse_vector(
std::pair<std::vector<size_t>, std::vector<float>>& v);
// NodeInitializer from_numpy(const cnpy::NpyArrayPtr& np);
NodeInitializer from_word2vec(const std::string& file,
int dimVoc,
int dimEmb,
bool normalize = false);
/**
* Computes Google's sinusoidal position embeddings
* starting from position 'start' taking into account
* batch and time dimensions of the tensor.
*
* Expected tensor layout {-2: time, -1: model}
*
* Usually gets later reshaped to {time, 1, model} and
* added with a broadcast to learned embeddings. Positional
* embeddings are the same for each batch entry and change
* over time.
*/
NodeInitializer sinusoidalPositionEmbeddings(int start);
} // namespace inits
} // namespace marian
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