#pragma once

#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
#include <torch/csrc/jit/tensorexpr/lowerings.h>
#include <torch/csrc/jit/tensorexpr/tensor.h>

namespace torch::jit::tensorexpr {

struct TensorInfo {
  std::vector<int64_t> dims;
  c10::ScalarType dtype;
};
std::optional<TensorInfo> getTensorInfo(const BufHandle& b);

int64_t normalizeAndCheckIndex(int64_t idx, int64_t list_size);

// Convert boolean to integer, if needed.
ExprHandle boolToInteger(const ExprHandle& x);
ExprHandle promoteToDtype(ExprHandle e, ScalarType dt);
void promoteInputs(
    std::vector<ExprHandle>& inputs,
    const int typeConstraints = kAllTypes);
ExprHandle promoteIntegerToDefaultType(const ExprHandle& e);
ExprHandle promoteHalfToFloat(const ExprHandle& e);
ExprHandle demoteOutput(
    const ExprHandle& e,
    const std::optional<ScalarType> type);

std::vector<ExprHandle> broadcastShapes(
    std::vector<std::vector<ExprHandle>> shapes);
std::vector<ExprHandle> broadcastShapes(
    const std::vector<ExprHandle>& a,
    const std::vector<ExprHandle>& b);

std::vector<ExprHandle> valueShape(const ArgValue& v);
ExprHandle tensorOrConstant(
    const ArgValue& v,
    const std::vector<ExprHandle>& axes);
ExprHandle scalarOrConstant(const ArgValue& v);
ExprHandle broadcast(const BufHandle& b, const std::vector<ExprHandle>& axes);
ExprHandle constant(const ArgValue& v);

ExprHandle clamp(
    const ExprHandle& cmin,
    const ExprHandle& cmax,
    const ExprHandle& input);

Tensor computeChunk(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape,
    const std::vector<ExprHandle>& outputStrides,
    const std::optional<ScalarType>& outputType,
    at::Device device);
Tensor computeTranspose(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape,
    const std::vector<ExprHandle>& outputStrides,
    const std::optional<ScalarType>& outputType,
    at::Device device);
Tensor computeExpand(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape,
    const std::vector<ExprHandle>& outputStrides,
    const std::optional<ScalarType>& outputType,
    at::Device device);
Tensor computeReshape(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape,
    const std::vector<ExprHandle>& outputStrides,
    const std::optional<ScalarType>& outputType,
    at::Device device);
Tensor computeFlatten(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape,
    const std::vector<ExprHandle>& outputStrides,
    const std::optional<ScalarType>& outputType,
    at::Device device);
Tensor computeCatWoConditionals(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape);
Tensor computeCat(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape,
    const std::vector<ExprHandle>& outputStrides,
    const std::optional<ScalarType>& outputType,
    at::Device device);
Tensor computeEmbedding(
    const std::vector<ArgValue>& inputs,
    const std::vector<ExprHandle>& outputShape,
    const std::vector<ExprHandle>& outputStrides,
    const std::optional<ScalarType>& outputType,
    at::Device device);

} // namespace torch::jit::tensorexpr