# 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
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import os
import sys
import unittest

import torch
from parameterized import parameterized

import tensorrt_llm
from tensorrt_llm import Tensor
from tensorrt_llm._utils import str_dtype_to_torch

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


class TestSqueeze(unittest.TestCase):

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

    @parameterized.expand([
        (
            [[[-3.0, -2.0, -1.0, 10.0, -25.0]], [[0.0, 1.0, 2.0, -2.0, -1.0]]],
            1,
        ),
        (
            [1.0],
            0,
        ),
    ])
    def test_squeeze(self, input_data, dim):
        dtype = 'float32'
        input_data = torch.tensor(input_data).cuda()
        torch_dtype = str_dtype_to_torch(dtype)
        input_data = input_data.to(torch_dtype)

        # construct trt network
        builder = tensorrt_llm.Builder()
        network = builder.create_network()
        with tensorrt_llm.net_guard(network):
            input_t = Tensor(name='input',
                             shape=input_data.shape,
                             dtype=tensorrt_llm.str_dtype_to_trt(dtype))

            output = tensorrt_llm.functional.squeeze(input_t, dim=dim)
            output.mark_output('output')

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

        # pytorch run
        ref = torch.squeeze(input_data, dim=dim)

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