# Copyright 2024 ByteDance and/or its affiliates.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import unittest

import torch

from protenix.model.modules.primitives import (
    rearrange_qk_to_dense_trunk,
    rearrange_to_dense_trunk,
)


def create_qkv(batch_size_dims, n_q, n_kv, d):
    q = torch.rand(size=(*batch_size_dims, n_q, d))
    k = torch.rand(size=(*batch_size_dims, n_kv, d))
    v = torch.rand(size=(*batch_size_dims, n_kv, d))
    return q, k, v


class TestUtils(unittest.TestCase):
    def setUp(self):
        return super().setUp()

    def test_equivalence(self):

        batch_size_dims = (3, 5)
        n = 128 * 2 + 18
        d = 9
        n_queries = 32
        n_keys = 128
        inf = 10e10

        torch.random.manual_seed(42)
        q, k, v = create_qkv(batch_size_dims, n, n, d)
        q_trunked, k_trunked, _, attn_bias_trunked, q_pad_length = (
            rearrange_to_dense_trunk(
                q,
                k,
                v,
                n_queries,
                n_keys,
                inf=inf,
            )
        )

        q_b, k_b, padding_info = rearrange_qk_to_dense_trunk(
            q, k, dim_q=-2, dim_k=-2, n_queries=n_queries, n_keys=n_keys
        )
        self.assertTrue(
            torch.allclose(
                padding_info["mask_trunked"] > 0, attn_bias_trunked[0, 0] > -1
            )
        )
        self.assertTrue(torch.allclose(q_b, q_trunked))
        self.assertTrue(torch.allclose(k_b, k_trunked))


if __name__ == "__main__":
    unittest.main()