# 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 unittest

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

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


class TestFunctional(unittest.TestCase):

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

    def test_squeeze(self,
                     input_data=[[[-3.0, -2.0, -1.0, 10.0, -25.0]],
                                 [[0.0, 1.0, 2.0, -2.0, -1.0]]],
                     dim=1):
        dtype = 'float32'
        torch_dtype = str_dtype_to_torch(dtype)
        input_data = input_data if isinstance(
            input_data, torch.Tensor) else torch.tensor(input_data)
        input_data = input_data.to(torch_dtype)

        builder = tensorrt_llm.Builder()
        net = builder.create_network()
        with tensorrt_llm.net_guard(net):
            network = tensorrt_llm.default_trtnet()
            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 = output.trt_tensor
            output.name = 'output'
            network.mark_output(output)

        build_engine = EngineFromNetwork((builder.trt_builder, net.trt_network))
        with TrtRunner(build_engine) as runner:
            outputs = runner.infer(feed_dict={
                'input': input_data.numpy(),
            })

        ref = torch.squeeze(input_data, dim=dim)

        np.testing.assert_allclose(ref.cpu().numpy(),
                                   outputs['output'],
                                   atol=1e-5)
        # print(ref)
        # print(outputs['output'])
        return