# Copyright 2023, UChicago Argonne, LLC
# All Rights Reserved
# Software Name: NEML2 -- the New Engineering material Model Library, version 2
# By: Argonne National Laboratory
# OPEN SOURCE LICENSE (MIT)
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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import pytest

# fixtures
from common import *

from neml2.tensors import BatchTensor


def test_named_ctors(tensor_options):
    batch_shape = (2, 3)
    base_shape = (3, 1, 2)
    shape = batch_shape + base_shape

    # empty
    A = BatchTensor.empty(base_shape, **tensor_options)
    assert A.batch.dim() == 0
    A = BatchTensor.empty(batch_shape, base_shape, **tensor_options)
    assert A.batch.dim() == len(batch_shape)

    # zeros
    A = BatchTensor.zeros(base_shape, **tensor_options)
    assert A.batch.dim() == 0
    assert torch.allclose(A.tensor(), torch.zeros(base_shape, **tensor_options))
    A = BatchTensor.zeros(batch_shape, base_shape, **tensor_options)
    assert A.batch.dim() == len(batch_shape)
    assert torch.allclose(A.tensor(), torch.zeros(shape, **tensor_options))

    # ones
    A = BatchTensor.ones(base_shape, **tensor_options)
    assert A.batch.dim() == 0
    assert torch.allclose(A.tensor(), torch.ones(base_shape, **tensor_options))
    A = BatchTensor.ones(batch_shape, base_shape, **tensor_options)
    assert A.batch.dim() == len(batch_shape)
    assert torch.allclose(A.tensor(), torch.ones(shape, **tensor_options))

    # full
    A = BatchTensor.full(base_shape, 1.1, **tensor_options)
    assert A.batch.dim() == 0
    assert torch.allclose(A.tensor(), torch.full(base_shape, 1.1, **tensor_options))
    A = BatchTensor.full(batch_shape, base_shape, 2.3, **tensor_options)
    assert A.batch.dim() == len(batch_shape)
    assert torch.allclose(A.tensor(), torch.full(shape, 2.3, **tensor_options))

    # identity
    A = BatchTensor.identity(5, **tensor_options)
    assert A.batch.dim() == 0
    assert torch.allclose(A.tensor(), torch.eye(5, **tensor_options))
    A = BatchTensor.identity(batch_shape, 5, **tensor_options)
    assert A.batch.dim() == len(batch_shape)
    assert torch.allclose(
        A.tensor(), torch.eye(5, **tensor_options).expand(batch_shape + (-1, -1))
    )


def test_views(tensor_options):
    batch_shape = (2, 3)
    base_shape = (3, 1, 2)
    A = BatchTensor.full(batch_shape, base_shape, 2.3, **tensor_options)
    A_torch = A.tensor()

    assert A.device == A_torch.device
    assert A.dtype == A_torch.dtype

    A_torch[0, 2, ..., :] = 5.5

    assert torch.allclose(A.tensor(), A_torch)
    assert A.device == A_torch.device
    assert A.dtype == A_torch.dtype
