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# SPDX-License-Identifier: Apache-2.0
#
# 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,
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import os
import sys
import unittest

import torch
from parameterized import parameterized

import tensorrt_llm
from tensorrt_llm import Tensor

sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
from utils.util import create_session, run_session, unittest_name_func


class TestFGroupNorm(unittest.TestCase):

    def setUp(self):
        tensorrt_llm.logger.set_level('error')

    @parameterized.expand([('float32', ), ('float16', )],
                          name_func=unittest_name_func)
    def test_group_norm(self, dtype):
        # test data
        num_channels = 6
        num_groups = 3
        x_shape = (2, num_channels, 3, 3)
        x_data = torch.rand(x_shape,
                            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
                            device="cuda")

        # construct trt network
        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):

            x = Tensor(name='x',
                       shape=x_shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.group_norm(x, num_groups)
            output.mark_output('output', dtype)

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {
            'x': x_data,
        }
        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.nn.functional.group_norm(x_data, num_groups)

        # compare diff
        torch.testing.assert_close(ref, outputs['output'], atol=1e-2, rtol=1e-2)