# 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

import tensorrt_llm
from tensorrt_llm import Tensor

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


class TestEinsum(unittest.TestCase):

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

    def test_einsum(self):
        dtype = 'float32'
        # test data
        x_shape = (12, 12, 96, 96)
        y_shape = (12, 12, 96, 64)
        x_data = torch.rand(x_shape,
                            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
                            device="cuda")
        y_data = torch.rand(y_shape,
                            dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype),
                            device="cuda")
        equation = 'bnth,bnhs->bnts'

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

            x = Tensor(name='x',
                       shape=x_shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            y = Tensor(name='y',
                       shape=y_shape,
                       dtype=tensorrt_llm.str_dtype_to_trt(dtype))
            output = tensorrt_llm.functional.einsum(equation, [x, y])
            output.mark_output('output', dtype)

        # trt run
        session = create_session(builder, network, precision=dtype)
        inputs = {'x': x_data, 'y': y_data}
        outputs = run_session(session, inputs)

        # pytorch run
        ref = torch.functional.einsum(equation, [x_data, y_data])

        # compare diff
        torch.testing.assert_close(outputs['output'], ref, atol=5e-3, rtol=2e-4)