# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 os
import sys
import unittest
from itertools import product

import numpy as np
import torch
from parameterized import parameterized

import tensorrt_llm

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


class TestArange(unittest.TestCase):

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

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

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

            output = tensorrt_llm.functional.arange(start=start,
                                                    end=end,
                                                    dtype="int32")
            output.mark_output('output', "int32")

        # trt run
        inputs = {}
        session = create_session(builder, network, precision="float32")
        outputs = run_session(session, inputs)

        ref = torch.arange(start, end).int().cuda()
        torch.testing.assert_close(outputs['output'], ref)

    @parameterized.expand(
        list(
            product(['int32', 'int64'], ['int32', 'int64'],
                    ['int32', 'int64', 'float32', 'float16'])))
    def test_arange_tensor(self,
                           s_dtype='int32',
                           e_dtype='int32',
                           r_dtype='int32'):
        # test data
        s = 0
        e = 128
        s_np_dtype = tensorrt_llm._utils.str_dtype_to_np(s_dtype)
        e_np_dtype = tensorrt_llm._utils.str_dtype_to_np(e_dtype)

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

            start = tensorrt_llm.functional.constant(
                np.array(s, dtype=s_np_dtype))
            end = tensorrt_llm.functional.constant(
                np.array([e], dtype=e_np_dtype))

            output = tensorrt_llm.functional.arange(start=start,
                                                    end=end,
                                                    dtype=r_dtype)

            output.mark_output('output', r_dtype)

        # trt run
        inputs = {}
        session = create_session(
            builder,
            network,
            precision="float32" if r_dtype != 'float16' else 'float16')
        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.arange(
            s, e, dtype=tensorrt_llm.str_dtype_to_torch(r_dtype)).cuda()

        # compare diff
        torch.testing.assert_close(outputs['output'], ref)