# SPDX-FileCopyrightText: Copyright (c) 2022-2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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import os
import sys
import unittest

import numpy as np
import pytest
import torch
from parameterized import parameterized
from polygraphy.backend.trt import CreateConfig, EngineFromNetwork, TrtRunner

import tensorrt_llm
from tensorrt_llm import Tensor

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


class TestFunctional(unittest.TestCase):

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

    @parameterized.expand([('float32', False), ('float32', True),
                           ('float16', False), ('float16', True),
                           ('bfloat16', False), ('bfloat16', True)])
    def test_identity(self, dtype, use_plugin):
        # Skip tests that are not supported in pre-ampere architecture
        if getSMVersion() < 80:
            if dtype == 'bfloat16':
                pytest.skip(
                    "bfloat16 is not supported in pre-ampere architecture")

        x_data = torch.randn(
            (4, 6, 3, 4), dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype))
        builder = tensorrt_llm.Builder()
        net = builder.create_network()
        if use_plugin:
            net.plugin_config.set_identity_plugin(dtype)
        with tensorrt_llm.net_guard(net):
            network = tensorrt_llm.default_trtnet()
            x = Tensor(name='x',
                       shape=x_data.shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.identity(x).trt_tensor
            output.name = 'output'
            network.mark_output(output)
            output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)

        build_engine = EngineFromNetwork(
            (builder.trt_builder, net.trt_network),
            config=CreateConfig(fp16=(dtype == 'float16'),
                                bf16=(dtype == 'bfloat16')))
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(feed_dict={'x': x_data})

        np.testing.assert_allclose(x_data.to(torch.float32),
                                   outputs['output'].to(torch.float32))