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"""
Collection of builtin functions used for host reference in EVT
"""

import numpy as np

from cutlass_cppgen.utils.datatypes import is_cupy_tensor, is_numpy_tensor, is_torch_available, is_torch_tensor

if is_torch_available():
    import torch


def multiply_add(x, y, z):
    return x * y + z


def sum(x, dim):
    if is_numpy_tensor(x):
        return x.sum(axis=tuple(dim))
    elif is_torch_tensor(x):
        return torch.sum(x, dim)


def max(x, dim):
    if is_numpy_tensor(x):
        return x.max(axis=tuple(dim))
    elif is_torch_tensor(x):
        return torch.amax(x, dim)


def maximum(x, y):
    if is_numpy_tensor(x):
        return np.maximum(x, y)
    elif is_torch_tensor(x):
        return torch.maximum(x, torch.tensor(y))


def minimum(x, y):
    if is_numpy_tensor(x):
        return np.minimum(x, y)
    elif is_torch_tensor(x):
        return torch.minimum(x, torch.tensor(y))

def exp(x):
    if is_numpy_tensor(x):
        return np.exp(x)
    elif is_torch_tensor(x):
        return torch.exp(x)


##############################################################################
# Layout manipulate nodes
##############################################################################

def permute(x, indices: tuple):
    if is_numpy_tensor(x):
        return np.transpose(x, axes=indices)
    elif is_torch_tensor(x):
        return x.permute(*indices)


def reshape(x, new_shape: tuple):
    if is_numpy_tensor(x):
        return np.reshape(x, newshape=new_shape)
    elif is_torch_tensor(x):
        return x.view(new_shape)