#
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
#
# 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.
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from unittest.mock import Mock, patch

import torch

from tests.ut.base import TestBase
from vllm_ascend._310p.quantization.methods.w8a8_dynamic import AscendW8A8DynamicFusedMoEMethod310


class TestAscendW8A8FusedMoEMethod310(TestBase):
    num_experts = 8
    hidden_size = 128
    intermediate_size = 128

    @patch("vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_ep_group")
    def setUp(self, mock_get_ep_group):
        with patch(
            "vllm_ascend._310p.quantization.methods.w8a8_dynamic.get_current_vllm_config"
        ) as mock_get_current_vllm_config:
            mock_vllm_config = Mock()
            mock_vllm_config.quant_config = Mock(quant_description={"group_size": 0})
            mock_vllm_config.scheduler_config = Mock(
                max_num_batched_tokens=2048, max_model_len=2048, enable_chunked_prefill=False
            )
            mock_get_current_vllm_config.return_value = mock_vllm_config
            mock_ep_group = Mock()
            mock_get_ep_group.return_value = mock_ep_group
            mock_ascend_config = Mock()

            mock_ascend_config.enable_chunked_prefill = False

            self.quant_method = AscendW8A8DynamicFusedMoEMethod310()

    def test_get_weight_310(self):
        param_dict = self.quant_method.get_weight(
            self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
        )
        self.assertEqual(param_dict["w13_weight"].dtype, torch.int8)
        self.assertEqual(
            param_dict["w13_weight"].shape, (self.num_experts, 2 * self.intermediate_size, self.hidden_size)
        )
        self.assertEqual(param_dict["w2_weight"].dtype, torch.int8)
        self.assertEqual(param_dict["w2_weight"].shape, (self.num_experts, self.hidden_size, self.intermediate_size))

    def test_get_dynamic_quant_param_310(self):
        param_dict = self.quant_method.get_dynamic_quant_param(
            self.num_experts, self.intermediate_size, self.hidden_size, torch.float16
        )
        self.assertEqual(param_dict["w13_weight_scale"].dtype, torch.float32)
        self.assertEqual(param_dict["w13_weight_scale"].shape, (self.num_experts, 2 * self.intermediate_size, 1))
        self.assertEqual(param_dict["w2_weight_scale"].dtype, torch.float32)
        self.assertEqual(param_dict["w2_weight_scale"].shape, (self.num_experts, self.hidden_size, 1))