# 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
# limitations under the License.
import unittest

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

import tensorrt_llm


class TestFunctional(unittest.TestCase):

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

    def test_arange_int(self):
        # test data
        start = 0
        end = 128
        dtype = 'int32'

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

            output = tensorrt_llm.functional.arange(start=start,
                                                    end=end,
                                                    dtype=dtype).trt_tensor
            output.name = 'output'
            network.mark_output(output)
            output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)

        # trt run
        build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(feed_dict={})

        ref = torch.arange(start, end).int()
        np.testing.assert_allclose(ref.cpu().numpy(),
                                   outputs['output'],
                                   atol=1e-5)

    def test_arange_tensor(self):
        # test data
        s = 0
        e = 128
        dtype = 'int32'

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

            start = tensorrt_llm.functional.constant(np.array(s,
                                                              dtype=np.int32))
            end_tensor = tensorrt_llm.functional.constant(
                np.array([0] * e, dtype=np.int32))

            output = tensorrt_llm.functional.arange(
                start=start,
                end=tensorrt_llm.functional.shape(end_tensor, 0),
                dtype=dtype).trt_tensor
            output.name = 'output'
            network.mark_output(output)
            output.dtype = tensorrt_llm.str_dtype_to_trt(dtype)

        # trt run
        build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(feed_dict={})

        ref = torch.arange(s, e).int()
        np.testing.assert_allclose(ref.cpu().numpy(),
                                   outputs['output'],
                                   atol=1e-5)