import os
from unittest.mock import MagicMock, patch

import torch

from tests.ut.base import TestBase
from vllm_ascend.quantization.methods.w8a16 import AscendW8A16LinearMethod


class TestAscendW8A16LinearMethod(TestBase):

    def setUp(self):
        self.method = AscendW8A16LinearMethod()

    def test_get_weight(self):
        weight = self.method.get_weight(10, 20)
        self.assertEqual(weight['weight'].dtype, torch.int8)
        self.assertEqual(weight['weight'].shape, (20, 10))

    @patch("torch_npu.npu_weight_quant_batchmatmul")
    def test_apply_with_x_is_int8(self, mock_npu_weight_quant_batchmatmul):
        layer = MagicMock()
        layer.weight.data = torch.randn(128, 256)
        layer.weight_scale.data = torch.randn(128, 1)
        layer.weight_offset.data = torch.randn(128, 1)

        x = torch.randn(32, 128)
        bias = torch.randn(256)

        expected_y_output = torch.randn(32, 256)
        mock_npu_weight_quant_batchmatmul.return_value = expected_y_output

        output = self.method.apply(layer, x, bias)
        expected_y_output += bias
        self.assertTrue(torch.equal(output, expected_y_output))

    @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "0"})
    @patch('torch_npu.npu_format_cast')
    def test_process_weights_after_loading_with_nz0(self,
                                                    mock_npu_format_cast):
        layer = MagicMock()
        layer.weight.data = torch.randint(-127,
                                          128, (128, 256),
                                          dtype=torch.int8)
        layer.weight_scale.data = torch.randn(128, 1)
        layer.weight_offset.data = torch.randn(128, 1)

        mock_npu_format_cast.return_value = MagicMock
        self.method.process_weights_after_loading(layer)

        self.assertEqual(layer.weight_scale.data.shape, (128, ))
        self.assertEqual(layer.weight_offset.data.shape, (128, ))
        mock_npu_format_cast.assert_not_called()

    @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "1"})
    @patch('torch_npu.npu_format_cast')
    def test_process_weights_after_loading_with_nz1(self,
                                                    mock_npu_format_cast):
        layer = MagicMock()

        layer.weight.data = torch.randint(-127,
                                          128, (128, 256),
                                          dtype=torch.int8)
        layer.weight_scale.data = torch.randn(128, 1)
        layer.weight_offset.data = torch.randn(128, 1)

        mock_npu_format_cast.return_value = MagicMock
        self.method.process_weights_after_loading(layer)

        self.assertEqual(layer.weight_scale.data.shape, (128, ))
        self.assertEqual(layer.weight_offset.data.shape, (128, ))
        mock_npu_format_cast.assert_called_once()

    @patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_NZ": "2"})
    @patch('torch_npu.npu_format_cast')
    def test_process_weights_after_loading_with_nz2(self,
                                                    mock_npu_format_cast):
        layer = MagicMock()

        layer.weight.data = torch.randint(-127,
                                          128, (128, 256),
                                          dtype=torch.int8)
        layer.weight_scale.data = torch.randn(128, 1)
        layer.weight_offset.data = torch.randn(128, 1)

        mock_npu_format_cast.return_value = MagicMock
        self.method.process_weights_after_loading(layer)

        self.assertEqual(layer.weight_scale.data.shape, (128, ))
        self.assertEqual(layer.weight_offset.data.shape, (128, ))
        mock_npu_format_cast.assert_called_once()
