"""
Edit Tool - 文件编辑工具

参考 OpenCode 的 edit.ts 完整实现。

核心功能：
- 精确字符串替换
- 9 种智能匹配策略（按优先级依次尝试）
- 生成 diff 预览
"""

from pathlib import Path
from typing import Optional, Generator
import difflib
import re

from agent.tools import tool, ToolResult, ToolContext


@tool(description="编辑文件，使用精确字符串替换。支持多种智能匹配策略。")
async def edit_file(
    file_path: str,
    old_string: str,
    new_string: str,
    replace_all: bool = False,
    context: Optional[ToolContext] = None
) -> ToolResult:
    """
    编辑文件内容

    使用 9 种智能匹配策略，按优先级尝试：
    1. SimpleReplacer - 精确匹配
    2. LineTrimmedReplacer - 忽略行首尾空白
    3. BlockAnchorReplacer - 基于首尾锚点的块匹配（使用 Levenshtein 相似度）
    4. WhitespaceNormalizedReplacer - 空白归一化
    5. IndentationFlexibleReplacer - 灵活缩进匹配
    6. EscapeNormalizedReplacer - 转义序列归一化
    7. TrimmedBoundaryReplacer - 边界空白裁剪
    8. ContextAwareReplacer - 上下文感知匹配
    9. MultiOccurrenceReplacer - 多次出现匹配

    Args:
        file_path: 文件路径
        old_string: 要替换的文本
        new_string: 替换后的文本
        replace_all: 是否替换所有匹配（默认 False，只替换唯一匹配）
        context: 工具上下文

    Returns:
        ToolResult: 编辑结果和 diff
    """
    if old_string == new_string:
        return ToolResult(
            title="无需编辑",
            output="old_string 和 new_string 相同",
            error="Strings are identical"
        )

    # 解析路径
    path = Path(file_path)
    if not path.is_absolute():
        path = Path.cwd() / path

    # 检查文件
    if not path.exists():
        return ToolResult(
            title="文件未找到",
            output=f"文件不存在: {file_path}",
            error="File not found"
        )

    if path.is_dir():
        return ToolResult(
            title="路径错误",
            output=f"路径是目录，不是文件: {file_path}",
            error="Path is a directory"
        )

    # 读取文件
    try:
        with open(path, 'r', encoding='utf-8') as f:
            content_old = f.read()
    except Exception as e:
        return ToolResult(
            title="读取失败",
            output=f"无法读取文件: {str(e)}",
            error=str(e)
        )

    # 执行替换
    try:
        content_new = replace(content_old, old_string, new_string, replace_all)
    except ValueError as e:
        return ToolResult(
            title="替换失败",
            output=str(e),
            error=str(e)
        )

    # 生成 diff
    diff = _create_diff(file_path, content_old, content_new)

    # 写入文件
    try:
        with open(path, 'w', encoding='utf-8') as f:
            f.write(content_new)
    except Exception as e:
        return ToolResult(
            title="写入失败",
            output=f"无法写入文件: {str(e)}",
            error=str(e)
        )

    # 统计变更
    old_lines = content_old.count('\n')
    new_lines = content_new.count('\n')

    return ToolResult(
        title=path.name,
        output=f"编辑成功\n\n{diff}",
        metadata={
            "diff": diff,
            "old_lines": old_lines,
            "new_lines": new_lines,
            "additions": max(0, new_lines - old_lines),
            "deletions": max(0, old_lines - new_lines)
        },
        long_term_memory=f"已编辑文件 {path.name}"
    )


# ============================================================================
# 替换策略（Replacers）
# ============================================================================

def replace(content: str, old_string: str, new_string: str, replace_all: bool = False) -> str:
    """
    使用多种策略尝试替换

    按优先级尝试所有策略，直到找到匹配
    """
    if old_string == new_string:
        raise ValueError("old_string 和 new_string 必须不同")

    not_found = True

    # 按优先级尝试策略
    for replacer in [
        simple_replacer,
        line_trimmed_replacer,
        block_anchor_replacer,
        whitespace_normalized_replacer,
        indentation_flexible_replacer,
        escape_normalized_replacer,
        trimmed_boundary_replacer,
        context_aware_replacer,
        multi_occurrence_replacer,
    ]:
        for search in replacer(content, old_string):
            index = content.find(search)
            if index == -1:
                continue

            not_found = False

            if replace_all:
                return content.replace(search, new_string)

            # 检查唯一性
            last_index = content.rfind(search)
            if index != last_index:
                continue

            return content[:index] + new_string + content[index + len(search):]

    if not_found:
        raise ValueError("在文件中未找到匹配的文本")

    raise ValueError(
        "找到多个匹配。请在 old_string 中提供更多上下文以唯一标识，"
        "或使用 replace_all=True 替换所有匹配。"
    )


# 1. SimpleReplacer - 精确匹配
def simple_replacer(content: str, find: str) -> Generator[str, None, None]:
    """精确匹配"""
    yield find


# 2. LineTrimmedReplacer - 忽略行首尾空白
def line_trimmed_replacer(content: str, find: str) -> Generator[str, None, None]:
    """忽略行首尾空白进行匹配"""
    content_lines = content.split('\n')
    find_lines = find.rstrip('\n').split('\n')

    for i in range(len(content_lines) - len(find_lines) + 1):
        # 检查所有行是否匹配（忽略首尾空白）
        matches = all(
            content_lines[i + j].strip() == find_lines[j].strip()
            for j in range(len(find_lines))
        )

        if matches:
            # 计算原始文本位置
            match_start = sum(len(content_lines[k]) + 1 for k in range(i))
            match_end = match_start + sum(
                len(content_lines[i + k]) + (1 if k < len(find_lines) - 1 else 0)
                for k in range(len(find_lines))
            )
            yield content[match_start:match_end]


# 3. BlockAnchorReplacer - 基于首尾锚点的块匹配
def block_anchor_replacer(content: str, find: str) -> Generator[str, None, None]:
    """
    基于首尾行作为锚点进行块匹配
    使用 Levenshtein 距离计算中间行的相似度
    """
    content_lines = content.split('\n')
    find_lines = find.rstrip('\n').split('\n')

    if len(find_lines) < 3:
        return

    first_line_find = find_lines[0].strip()
    last_line_find = find_lines[-1].strip()
    find_block_size = len(find_lines)

    # 收集所有候选位置（首尾行都匹配）
    candidates = []
    for i in range(len(content_lines)):
        if content_lines[i].strip() != first_line_find:
            continue

        # 查找匹配的尾行
        for j in range(i + 2, len(content_lines)):
            if content_lines[j].strip() == last_line_find:
                candidates.append((i, j))
                break

    if not candidates:
        return

    # 单个候选：使用宽松阈值
    if len(candidates) == 1:
        start_line, end_line = candidates[0]
        actual_block_size = end_line - start_line + 1

        similarity = _calculate_block_similarity(
            content_lines[start_line:end_line + 1],
            find_lines
        )

        if similarity >= 0.0:  # SINGLE_CANDIDATE_SIMILARITY_THRESHOLD
            match_start = sum(len(content_lines[k]) + 1 for k in range(start_line))
            match_end = match_start + sum(
                len(content_lines[k]) + (1 if k < end_line else 0)
                for k in range(start_line, end_line + 1)
            )
            yield content[match_start:match_end]
        return

    # 多个候选：选择相似度最高的
    best_match = None
    max_similarity = -1

    for start_line, end_line in candidates:
        similarity = _calculate_block_similarity(
            content_lines[start_line:end_line + 1],
            find_lines
        )

        if similarity > max_similarity:
            max_similarity = similarity
            best_match = (start_line, end_line)

    if max_similarity >= 0.3 and best_match:  # MULTIPLE_CANDIDATES_SIMILARITY_THRESHOLD
        start_line, end_line = best_match
        match_start = sum(len(content_lines[k]) + 1 for k in range(start_line))
        match_end = match_start + sum(
            len(content_lines[k]) + (1 if k < end_line else 0)
            for k in range(start_line, end_line + 1)
        )
        yield content[match_start:match_end]


def _calculate_block_similarity(content_block: list, find_block: list) -> float:
    """计算块相似度（使用 Levenshtein 距离）"""
    actual_size = len(content_block)
    find_size = len(find_block)
    lines_to_check = min(find_size - 2, actual_size - 2)

    if lines_to_check <= 0:
        return 1.0

    similarity = 0.0
    for j in range(1, min(find_size - 1, actual_size - 1)):
        content_line = content_block[j].strip()
        find_line = find_block[j].strip()
        max_len = max(len(content_line), len(find_line))

        if max_len == 0:
            continue

        distance = _levenshtein(content_line, find_line)
        similarity += (1 - distance / max_len) / lines_to_check

    return similarity


def _levenshtein(a: str, b: str) -> int:
    """Levenshtein 距离算法"""
    if not a:
        return len(b)
    if not b:
        return len(a)

    matrix = [[0] * (len(b) + 1) for _ in range(len(a) + 1)]

    for i in range(len(a) + 1):
        matrix[i][0] = i
    for j in range(len(b) + 1):
        matrix[0][j] = j

    for i in range(1, len(a) + 1):
        for j in range(1, len(b) + 1):
            cost = 0 if a[i - 1] == b[j - 1] else 1
            matrix[i][j] = min(
                matrix[i - 1][j] + 1,      # 删除
                matrix[i][j - 1] + 1,      # 插入
                matrix[i - 1][j - 1] + cost  # 替换
            )

    return matrix[len(a)][len(b)]


# 4. WhitespaceNormalizedReplacer - 空白归一化
def whitespace_normalized_replacer(content: str, find: str) -> Generator[str, None, None]:
    """空白归一化匹配（所有空白序列归一化为单个空格）"""
    def normalize_ws(text: str) -> str:
        return re.sub(r'\s+', ' ', text).strip()

    normalized_find = normalize_ws(find)
    lines = content.split('\n')

    # 单行匹配
    for line in lines:
        if normalize_ws(line) == normalized_find:
            yield line
            continue

        # 子串匹配
        if normalized_find in normalize_ws(line):
            words = find.strip().split()
            if words:
                pattern = r'\s+'.join(re.escape(word) for word in words)
                match = re.search(pattern, line)
                if match:
                    yield match.group(0)

    # 多行匹配
    find_lines = find.split('\n')
    if len(find_lines) > 1:
        for i in range(len(lines) - len(find_lines) + 1):
            block = lines[i:i + len(find_lines)]
            if normalize_ws('\n'.join(block)) == normalized_find:
                yield '\n'.join(block)


# 5. IndentationFlexibleReplacer - 灵活缩进匹配
def indentation_flexible_replacer(content: str, find: str) -> Generator[str, None, None]:
    """移除缩进后匹配"""
    def remove_indentation(text: str) -> str:
        lines = text.split('\n')
        non_empty = [line for line in lines if line.strip()]

        if not non_empty:
            return text

        min_indent = min(len(line) - len(line.lstrip()) for line in non_empty)
        return '\n'.join(
            line[min_indent:] if line.strip() else line
            for line in lines
        )

    normalized_find = remove_indentation(find)
    content_lines = content.split('\n')
    find_lines = find.split('\n')

    for i in range(len(content_lines) - len(find_lines) + 1):
        block = '\n'.join(content_lines[i:i + len(find_lines)])
        if remove_indentation(block) == normalized_find:
            yield block


# 6. EscapeNormalizedReplacer - 转义序列归一化
def escape_normalized_replacer(content: str, find: str) -> Generator[str, None, None]:
    """反转义后匹配"""
    def unescape_string(s: str) -> str:
        replacements = {
            r'\n': '\n',
            r'\t': '\t',
            r'\r': '\r',
            r"\'": "'",
            r'\"': '"',
            r'\`': '`',
            r'\\': '\\',
            r'\$': '$',
        }
        result = s
        for escaped, unescaped in replacements.items():
            result = result.replace(escaped, unescaped)
        return result

    unescaped_find = unescape_string(find)

    # 直接匹配
    if unescaped_find in content:
        yield unescaped_find

    # 尝试反转义后匹配
    lines = content.split('\n')
    find_lines = unescaped_find.split('\n')

    for i in range(len(lines) - len(find_lines) + 1):
        block = '\n'.join(lines[i:i + len(find_lines)])
        if unescape_string(block) == unescaped_find:
            yield block


# 7. TrimmedBoundaryReplacer - 边界空白裁剪
def trimmed_boundary_replacer(content: str, find: str) -> Generator[str, None, None]:
    """裁剪边界空白后匹配"""
    trimmed_find = find.strip()

    if trimmed_find == find:
        return  # 已经是 trimmed，无需尝试

    # 尝试匹配 trimmed 版本
    if trimmed_find in content:
        yield trimmed_find

    # 尝试块匹配
    lines = content.split('\n')
    find_lines = find.split('\n')

    for i in range(len(lines) - len(find_lines) + 1):
        block = '\n'.join(lines[i:i + len(find_lines)])
        if block.strip() == trimmed_find:
            yield block


# 8. ContextAwareReplacer - 上下文感知匹配
def context_aware_replacer(content: str, find: str) -> Generator[str, None, None]:
    """基于上下文（首尾行）匹配，允许中间行有差异"""
    find_lines = find.split('\n')
    if find_lines and find_lines[-1] == '':
        find_lines.pop()

    if len(find_lines) < 3:
        return

    content_lines = content.split('\n')
    first_line = find_lines[0].strip()
    last_line = find_lines[-1].strip()

    # 查找首尾匹配的块
    for i in range(len(content_lines)):
        if content_lines[i].strip() != first_line:
            continue

        for j in range(i + 2, len(content_lines)):
            if content_lines[j].strip() == last_line:
                block_lines = content_lines[i:j + 1]

                # 检查块长度是否匹配
                if len(block_lines) == len(find_lines):
                    # 计算中间行匹配率
                    matching_lines = 0
                    total_non_empty = 0

                    for k in range(1, len(block_lines) - 1):
                        block_line = block_lines[k].strip()
                        find_line = find_lines[k].strip()

                        if block_line or find_line:
                            total_non_empty += 1
                            if block_line == find_line:
                                matching_lines += 1

                    # 至少 50% 的中间行匹配
                    if total_non_empty == 0 or matching_lines / total_non_empty >= 0.5:
                        yield '\n'.join(block_lines)
                        break
                break


# 9. MultiOccurrenceReplacer - 多次出现匹配
def multi_occurrence_replacer(content: str, find: str) -> Generator[str, None, None]:
    """yield 所有精确匹配，用于 replace_all"""
    start_index = 0
    while True:
        index = content.find(find, start_index)
        if index == -1:
            break
        yield find
        start_index = index + len(find)


# ============================================================================
# 辅助函数
# ============================================================================

def _create_diff(filepath: str, old_content: str, new_content: str) -> str:
    """生成 unified diff"""
    old_lines = old_content.splitlines(keepends=True)
    new_lines = new_content.splitlines(keepends=True)

    diff_lines = list(difflib.unified_diff(
        old_lines,
        new_lines,
        fromfile=f"a/{filepath}",
        tofile=f"b/{filepath}",
        lineterm=''
    ))

    if not diff_lines:
        return "(无变更)"

    return ''.join(diff_lines)