"""数据访问层的基于属性的测试。"""

import pytest
import pandas as pd
import numpy as np
from hypothesis import given, strategies as st, settings, HealthCheck
from typing import Dict, Any

from src.data_access import DataAccessLayer


# 生成随机 DataFrame 的策略
@st.composite
def dataframe_strategy(draw):
    """生成随机 DataFrame 用于测试。"""
    n_rows = draw(st.integers(min_value=10, max_value=1000))
    n_cols = draw(st.integers(min_value=2, max_value=20))
    
    data = {}
    for i in range(n_cols):
        col_type = draw(st.sampled_from(['int', 'float', 'str', 'datetime']))
        col_name = f'col_{i}'
        
        if col_type == 'int':
            data[col_name] = draw(st.lists(
                st.integers(min_value=-1000, max_value=1000),
                min_size=n_rows,
                max_size=n_rows
            ))
        elif col_type == 'float':
            data[col_name] = draw(st.lists(
                st.floats(min_value=-1000.0, max_value=1000.0, allow_nan=False, allow_infinity=False),
                min_size=n_rows,
                max_size=n_rows
            ))
        elif col_type == 'str':
            data[col_name] = draw(st.lists(
                st.text(min_size=1, max_size=20, alphabet=st.characters(blacklist_categories=('Cs',))),
                min_size=n_rows,
                max_size=n_rows
            ))
        else:  # datetime
            # 生成日期字符串
            dates = pd.date_range('2020-01-01', periods=n_rows, freq='D')
            data[col_name] = dates.tolist()
    
    return pd.DataFrame(data)


class TestDataAccessProperties:
    """数据访问层的属性测试。"""
    
    # Feature: true-ai-agent, Property 18: 数据访问限制
    @given(df=dataframe_strategy())
    @settings(max_examples=20, deadline=None, suppress_health_check=[HealthCheck.data_too_large])
    def test_property_18_data_access_restriction(self, df):
        """
        属性 18：数据访问限制
        
        验证需求：约束条件5.3
        
        对于任何数据，数据画像应该只包含元数据和统计摘要，
        不应该包含完整的原始行级数据。
        """
        # 创建数据访问层
        dal = DataAccessLayer(df, file_path="test.csv")
        
        # 获取数据画像
        profile = dal.get_profile()
        
        # 验证：数据画像不应包含原始数据
        # 1. 检查行数和列数是元数据
        assert profile.row_count == len(df)
        assert profile.column_count == len(df.columns)
        
        # 2. 检查列信息
        assert len(profile.columns) == len(df.columns)
        
        for col_info in profile.columns:
            # 3. 示例值应该被限制（最多5个）
            assert len(col_info.sample_values) <= 5
            
            # 4. 统计信息应该是聚合数据，不是原始数据
            if col_info.dtype == 'numeric':
                # 统计信息应该是单个值，不是数组
                if col_info.statistics:
                    for stat_key, stat_value in col_info.statistics.items():
                        assert not isinstance(stat_value, (list, np.ndarray, pd.Series))
                        # 应该是标量值或 None
                        assert stat_value is None or isinstance(stat_value, (int, float))
            
            # 5. 缺失率应该是聚合指标（0-1之间的浮点数）
            assert 0.0 <= col_info.missing_rate <= 1.0
            
            # 6. 唯一值数量应该是聚合指标
            assert isinstance(col_info.unique_count, int)
            assert col_info.unique_count >= 0
        
        # 7. 验证数据画像的 JSON 序列化不包含大量原始数据
        profile_json = profile.to_json()
        # JSON 大小应该远小于原始数据
        # 原始数据至少有 n_rows * n_cols 个值
        # 数据画像应该只有元数据和少量示例
        original_data_size = len(df) * len(df.columns)
        # 数据画像的大小应该远小于原始数据（至少小于10%）
        assert len(profile_json) < original_data_size * 100  # 粗略估计
    
    @given(df=dataframe_strategy())
    @settings(max_examples=10, deadline=None)
    def test_data_profile_completeness(self, df):
        """
        测试数据画像的完整性。
        
        数据画像应该包含所有必需的元数据字段。
        """
        dal = DataAccessLayer(df, file_path="test.csv")
        profile = dal.get_profile()
        
        # 验证必需字段存在
        assert profile.file_path == "test.csv"
        assert profile.row_count > 0
        assert profile.column_count > 0
        assert len(profile.columns) > 0
        assert profile.inferred_type is not None
        
        # 验证每个列信息的完整性
        for col_info in profile.columns:
            assert col_info.name is not None
            assert col_info.dtype in ['numeric', 'categorical', 'datetime', 'text']
            assert 0.0 <= col_info.missing_rate <= 1.0
            assert col_info.unique_count >= 0
            assert isinstance(col_info.sample_values, list)
            assert isinstance(col_info.statistics, dict)
    
    @given(df=dataframe_strategy())
    @settings(max_examples=10, deadline=None)
    def test_column_type_inference(self, df):
        """
        测试列类型推断的正确性。
        
        推断的类型应该与实际数据类型一致。
        """
        dal = DataAccessLayer(df, file_path="test.csv")
        profile = dal.get_profile()
        
        for i, col_info in enumerate(profile.columns):
            col_name = col_info.name
            actual_dtype = df[col_name].dtype
            
            # 验证类型推断的合理性
            if pd.api.types.is_numeric_dtype(actual_dtype):
                assert col_info.dtype in ['numeric', 'categorical']
            elif pd.api.types.is_datetime64_any_dtype(actual_dtype):
                assert col_info.dtype == 'datetime'
            elif pd.api.types.is_object_dtype(actual_dtype):
                assert col_info.dtype in ['categorical', 'text', 'datetime']