#!/usr/bin/env python3
"""
Fuzzy Matching Module for File Operations

Implements a multi-strategy matching chain to robustly find and replace text,
accommodating variations in whitespace, indentation, and escaping common
in LLM-generated code.

The 9-strategy chain (inspired by OpenCode), tried in order:
1. Exact match - Direct string comparison
2. Line-trimmed - Strip leading/trailing whitespace per line
3. Whitespace normalized - Collapse multiple spaces/tabs to single space
4. Indentation flexible - Ignore indentation differences entirely
5. Escape normalized - Convert \\n literals to actual newlines
6. Trimmed boundary - Trim first/last line whitespace only
7. Block anchor - Match first+last lines, use similarity for middle
8. Context-aware - 50% line similarity threshold

Multi-occurrence matching is handled via the replace_all flag.

Usage:
    from tools.fuzzy_match import fuzzy_find_and_replace
    
    new_content, match_count, strategy, error = fuzzy_find_and_replace(
        content="def foo():\\n    pass",
        old_string="def foo():",
        new_string="def bar():",
        replace_all=False
    )
"""

import re
from typing import Tuple, Optional, List, Callable
from difflib import SequenceMatcher

UNICODE_MAP = {
    "\u201c": '"', "\u201d": '"',  # smart double quotes
    "\u2018": "'", "\u2019": "'",  # smart single quotes
    "\u2014": "--", "\u2013": "-", # em/en dashes
    "\u2026": "...", "\u00a0": " ", # ellipsis and non-breaking space
}

def _unicode_normalize(text: str) -> str:
    """Normalizes Unicode characters to their standard ASCII equivalents."""
    for char, repl in UNICODE_MAP.items():
        text = text.replace(char, repl)
    return text


def fuzzy_find_and_replace(content: str, old_string: str, new_string: str,
                            replace_all: bool = False) -> Tuple[str, int, Optional[str], Optional[str]]:
    """
    Find and replace text using a chain of increasingly fuzzy matching strategies.

    Args:
        content: The file content to search in
        old_string: The text to find
        new_string: The replacement text
        replace_all: If True, replace all occurrences; if False, require uniqueness

    Returns:
        Tuple of (new_content, match_count, strategy_name, error_message)
        - If successful: (modified_content, number_of_replacements, strategy_used, None)
        - If failed: (original_content, 0, None, error_description)
    """
    if not old_string:
        return content, 0, None, "old_string cannot be empty"

    if old_string == new_string:
        return content, 0, None, "old_string and new_string are identical"

    # Try each matching strategy in order
    strategies: List[Tuple[str, Callable]] = [
        ("exact", _strategy_exact),
        ("line_trimmed", _strategy_line_trimmed),
        ("whitespace_normalized", _strategy_whitespace_normalized),
        ("indentation_flexible", _strategy_indentation_flexible),
        ("escape_normalized", _strategy_escape_normalized),
        ("trimmed_boundary", _strategy_trimmed_boundary),
        ("unicode_normalized", _strategy_unicode_normalized),
        ("block_anchor", _strategy_block_anchor),
        ("context_aware", _strategy_context_aware),
    ]

    for strategy_name, strategy_fn in strategies:
        matches = strategy_fn(content, old_string)

        if matches:
            # Found matches with this strategy
            if len(matches) > 1 and not replace_all:
                return content, 0, None, (
                    f"Found {len(matches)} matches for old_string. "
                    f"Provide more context to make it unique, or use replace_all=True."
                )

            # Escape-drift guard: when the matched strategy is NOT `exact`,
            # we matched via some form of normalization. If new_string
            # contains shell/JSON-style escape sequences (\' or \") that
            # would be written literally into the file but the matched
            # region of the file has no such sequences, this is almost
            # certainly tool-call serialization drift — the model typed
            # an apostrophe/quote and the transport added a stray
            # backslash. Writing new_string as-is would corrupt the file.
            # Block with a helpful error so the model re-reads and retries
            # instead of the caller silently persisting garbage (or not).
            if strategy_name != "exact":
                drift_err = _detect_escape_drift(content, matches, old_string, new_string)
                if drift_err:
                    return content, 0, None, drift_err

            # Perform replacement. When the matched strategy is NOT `exact`,
            # the file's indentation may differ from what the LLM sent in
            # old_string/new_string — e.g. LLM used 2-space indent but the
            # file is 4-space. Shift new_string by the indentation delta so
            # the replacement matches the file's actual indent pattern.
            # LLMs frequently serialize tabs / carriage returns in JSON
            # tool-call arguments as the two-character sequences ``\t`` and
            # ``\r`` (backslash + letter) instead of the real control bytes.
            # If we write new_string verbatim, the file ends up with literal
            # backslash sequences where the surrounding code uses real tabs.
            #
            # Strategy: only unescape when the matched region of the file
            # *actually contains* the corresponding real control character.
            # That mirrors the region-based heuristic in
            # ``_detect_escape_drift`` and keeps legitimate writes of the
            # literal two-character string ``"\t"`` (e.g. patching Python
            # source that contains a tab string literal in source text)
            # untouched — those files have a backslash+t in the matched
            # region, not a real tab, so we leave new_string alone.
            #
            # ``\n`` is intentionally excluded: newlines serialize correctly
            # through JSON, and rewriting backslash-n would mangle escape
            # sequences in source code constants far more often than help.
            effective_new = _maybe_unescape_new_string(
                new_string, content, matches,
            )
            # Unicode-preservation guard: when strategy 7 (unicode_normalized)
            # matched, the file has Unicode characters (em-dashes, smart quotes,
            # ellipsis) but old_string/new_string from the LLM are ASCII
            # equivalents.  Writing new_string verbatim would silently corrupt
            # the file's Unicode — em-dashes become two hyphens, smart quotes
            # become straight quotes.  Align the replacement with the file's
            # actual Unicode so only the LLM's intended changes are applied
            # and unchanged portions keep their original characters.
            if strategy_name == "unicode_normalized":
                effective_new = _preserve_unicode_in_replacement(
                    content, matches, old_string, effective_new,
                )
            new_content = _apply_replacements(
                content, matches, effective_new,
                old_string=old_string if strategy_name != "exact" else None,
            )
            return new_content, len(matches), strategy_name, None

    # No strategy found a match
    return content, 0, None, "Could not find a match for old_string in the file"


def _detect_escape_drift(content: str, matches: List[Tuple[int, int]],
                         old_string: str, new_string: str) -> Optional[str]:
    """Detect tool-call escape-drift artifacts in new_string.

    Looks for ``\\'`` or ``\\"`` sequences that are present in both
    old_string and new_string (i.e. the model copy-pasted them as "context"
    it intended to preserve) but don't exist in the matched region of the
    file. That pattern indicates the transport layer inserted spurious
    shell-style escapes around apostrophes or quotes — writing new_string
    verbatim would literally insert ``\\'`` into source code.

    Returns an error string if drift is detected, None otherwise.
    """
    # Cheap pre-check: bail out unless new_string actually contains a
    # suspect escape sequence. This keeps the guard free for all the
    # common, correct cases.
    if "\\'" not in new_string and '\\"' not in new_string:
        return None

    # Aggregate matched regions of the file — that's what new_string will
    # replace. If the suspect escapes are present there already, the
    # model is genuinely preserving them (valid for some languages /
    # escaped strings); accept the patch.
    matched_regions = "".join(content[start:end] for start, end in matches)

    for suspect in ("\\'", '\\"'):
        if suspect in new_string and suspect in old_string and suspect not in matched_regions:
            plain = suspect[1]  # "'" or '"'
            return (
                f"Escape-drift detected: old_string and new_string contain "
                f"the literal sequence {suspect!r} but the matched region of "
                f"the file does not. This is almost always a tool-call "
                f"serialization artifact where an apostrophe or quote got "
                f"prefixed with a spurious backslash. Re-read the file with "
                f"read_file and pass old_string/new_string without "
                f"backslash-escaping {plain!r} characters."
            )
    return None


def _leading_whitespace(line: str) -> str:
    """Return the leading whitespace prefix of a line (spaces/tabs)."""
    i = 0
    while i < len(line) and line[i] in (" ", "\t"):
        i += 1
    return line[:i]


def _first_meaningful_line(text: str) -> Optional[str]:
    """Return the first line of ``text`` that has any non-whitespace content.

    Returns ``None`` if no such line exists (text is empty or all whitespace).
    """
    for line in text.split("\n"):
        if line.strip():
            return line
    return None


def _reindent_replacement(file_region: str, old_string: str, new_string: str) -> str:
    """Adjust ``new_string`` so its indentation matches ``file_region``.

    Used after a non-exact fuzzy match: the LLM may have sent old_string and
    new_string with a different indent than the file actually has (e.g.
    2-space indent in tool args vs 4-space indent on disk). The fuzzy
    strategy successfully matched anyway, but writing ``new_string`` verbatim
    would corrupt the file's indentation.

    Approach:

    1. For each non-blank line in ``new_string``, compute its indent
       *relative* to the shallowest non-blank line of ``old_string`` (the
       LLM's base indent).
    2. Anchor that relative indent onto the file's actual base indent (the
       leading whitespace of the file_region's first non-blank line).
    3. Re-emit each non-blank line as ``file_base + (line_indent - llm_base)``.

    Blank lines and lines less-indented than the LLM's base are anchored
    directly to the file's base indent.

    No-op cases (returns ``new_string`` unchanged):
    - file_region or old_string has no meaningful line
    - LLM base indent equals file base indent
    - new_string is empty
    """
    if not new_string:
        return new_string

    old_first = _first_meaningful_line(old_string)
    file_first = _first_meaningful_line(file_region)
    if old_first is None or file_first is None:
        return new_string

    old_indent = _leading_whitespace(old_first)
    file_indent = _leading_whitespace(file_first)

    if old_indent == file_indent:
        return new_string

    # Re-indent each line of new_string. Strategy: replace the LLM's base
    # indent prefix with the file's base indent prefix, preserving any
    # additional indent the LLM added on top. This is the same approach
    # Roo Code uses (multi-search-replace.ts:466-500). It preserves the
    # LLM's intended *relative* nesting between lines while anchoring to
    # the file's actual indent style.
    out_lines: List[str] = []
    for line in new_string.split("\n"):
        if not line.strip():
            # Blank lines: leave whitespace untouched.
            out_lines.append(line)
            continue
        line_indent = _leading_whitespace(line)
        if line_indent.startswith(old_indent):
            # Common case: line has the LLM's base indent (possibly plus
            # extra). Swap base prefix for the file's base prefix.
            remainder = line[len(old_indent):]
            out_lines.append(file_indent + remainder)
        else:
            # Line is less-indented than the LLM's base — e.g. a dedent at
            # the start of new_string. Anchor to the file's base.
            out_lines.append(file_indent + line.lstrip(" \t"))
    return "\n".join(out_lines)


def _maybe_unescape_new_string(new_string: str,
                               content: str,
                               matches: List[Tuple[int, int]]) -> str:
    """Conditionally unescape ``\\t``/``\\r`` in new_string.

    LLMs frequently send the two-character sequences ``\\t`` (backslash + t)
    and ``\\r`` (backslash + r) inside JSON tool-call arguments where they
    meant a real tab or carriage-return byte. Writing the string verbatim
    corrupts tab-indented files with literal backslash-letter pairs.

    The unescape is only applied per-sequence when the *matched region of
    the file* actually contains the corresponding control character — that
    is, we only convert ``\\t`` -> tab when the file region we're replacing
    contains a real tab byte. Files that legitimately contain the literal
    two-character string ``"\\t"`` (e.g. a Python source line that defines
    ``sep = "\\t"``) get a backslash+t in the matched region instead of a
    tab, so we leave new_string alone.

    ``\\n`` is intentionally excluded: newlines serialize correctly through
    JSON and rewriting backslash-n would corrupt escape sequences in
    string literals far more often than it would help.
    """
    # Cheap pre-check — bail out unless new_string actually contains one of
    # the suspect sequences. Keeps the common case free.
    if "\\t" not in new_string and "\\r" not in new_string:
        return new_string

    matched_regions = "".join(content[start:end] for start, end in matches)
    out = new_string
    if "\\t" in out and "\t" in matched_regions:
        out = out.replace("\\t", "\t")
    if "\\r" in out and "\r" in matched_regions:
        out = out.replace("\\r", "\r")
    return out


def _preserve_unicode_in_replacement(
    content: str, matches: List[Tuple[int, int]],
    old_string: str, new_string: str,
) -> str:
    """Preserve Unicode characters from the file in the replacement string.

    When strategy 7 (unicode_normalized) matched, the file has Unicode
    characters (em-dashes, smart quotes, ellipsis, non-breaking spaces)
    but old_string/new_string from the LLM are ASCII equivalents.
    Writing new_string verbatim would silently corrupt the file's
    Unicode — em-dashes become two hyphens, smart quotes become
    straight quotes.

    This function aligns the replacement with the file's actual Unicode
    by diffing old_string→new_string and applying only the actual edits
    to the file's original text, preserving Unicode for unchanged portions.
    """
    # Aggregate the matched file regions
    file_region = "".join(content[start:end] for start, end in matches)

    # Normalize both for comparison
    norm_old = _unicode_normalize(old_string)
    norm_file = _unicode_normalize(file_region)

    # If the normalized forms don't match, the strategy shouldn't have
    # fired — fall back to direct replacement.
    if norm_old != norm_file:
        return new_string

    # Build position maps from normalized space back to original space
    # for both old_string and file_region.  UNICODE_MAP replacements can
    # expand characters (em-dash → '--'), so normalized positions don't
    # map 1:1 to original positions.  Reuse the module-level
    # _build_orig_to_norm_map, then invert it (same inversion as
    # _map_positions_norm_to_orig) to get norm→orig lookups.
    file_orig_to_norm = _build_orig_to_norm_map(file_region)
    file_norm_to_orig: dict[int, int] = {}
    for orig_pos, np in enumerate(file_orig_to_norm[:-1]):
        if np not in file_norm_to_orig:
            file_norm_to_orig[np] = orig_pos

    # Diff norm_old → new_string to find the actual edits
    sm = SequenceMatcher(None, norm_old, new_string)
    opcodes = sm.get_opcodes()

    # Apply edits to file_region, preserving Unicode for unchanged spans
    result_parts: List[str] = []
    for tag, i1, i2, j1, j2 in opcodes:
        if tag == "equal":
            # Keep the original file_region text for this span
            orig_start = file_norm_to_orig.get(i1, 0)
            orig_end = orig_start
            while (
                orig_end < len(file_region)
                and file_orig_to_norm[orig_end] < i2
            ):
                orig_end += 1
            result_parts.append(file_region[orig_start:orig_end])
        elif tag == "replace":
            result_parts.append(new_string[j1:j2])
        elif tag == "delete":
            pass  # skip deleted portion
        elif tag == "insert":
            result_parts.append(new_string[j1:j2])

    return "".join(result_parts)


def _apply_replacements(content: str, matches: List[Tuple[int, int]],
                        new_string: str, old_string: Optional[str] = None) -> str:
    """
    Apply replacements at the given positions.

    Args:
        content: Original content
        matches: List of (start, end) positions to replace
        new_string: Replacement text
        old_string: When non-None, signals that the match came from a
            non-exact fuzzy strategy; ``new_string`` is re-indented to
            match the file's actual indentation before substitution.

    Returns:
        Content with replacements applied
    """
    # Sort matches by position (descending) to replace from end to start
    # This preserves positions of earlier matches
    sorted_matches = sorted(matches, key=lambda x: x[0], reverse=True)

    result = content
    for start, end in sorted_matches:
        if old_string is not None:
            file_region = content[start:end]
            adjusted = _reindent_replacement(file_region, old_string, new_string)
        else:
            adjusted = new_string
        result = result[:start] + adjusted + result[end:]

    return result


# =============================================================================
# Matching Strategies
# =============================================================================

def _strategy_exact(content: str, pattern: str) -> List[Tuple[int, int]]:
    """Strategy 1: Exact string match."""
    matches = []
    start = 0
    while True:
        pos = content.find(pattern, start)
        if pos == -1:
            break
        matches.append((pos, pos + len(pattern)))
        # Advance past the whole match, not just one char, so self-overlapping
        # patterns (e.g. "aa" in "aaaa") produce non-overlapping spans matching
        # str.replace() semantics. Advancing by 1 yielded overlapping matches
        # that corrupt the file under replace_all=True (reverse-order apply on
        # stale offsets).
        start = pos + len(pattern)
    return matches


def _strategy_line_trimmed(content: str, pattern: str) -> List[Tuple[int, int]]:
    """
    Strategy 2: Match with line-by-line whitespace trimming.
    
    Strips leading/trailing whitespace from each line before matching.
    """
    # Normalize pattern and content by trimming each line
    pattern_lines = [line.strip() for line in pattern.split('\n')]
    pattern_normalized = '\n'.join(pattern_lines)
    
    content_lines = content.split('\n')
    content_normalized_lines = [line.strip() for line in content_lines]
    
    # Build mapping from normalized positions back to original positions
    return _find_normalized_matches(
        content, content_lines, content_normalized_lines,
        pattern, pattern_normalized
    )


def _strategy_whitespace_normalized(content: str, pattern: str) -> List[Tuple[int, int]]:
    """
    Strategy 3: Collapse multiple whitespace to single space.
    """
    def normalize(s):
        # Collapse multiple spaces/tabs to single space, preserve newlines
        return re.sub(r'[ \t]+', ' ', s)
    
    pattern_normalized = normalize(pattern)
    content_normalized = normalize(content)
    
    # Find in normalized, map back to original
    matches_in_normalized = _strategy_exact(content_normalized, pattern_normalized)
    
    if not matches_in_normalized:
        return []
    
    # Map positions back to original content
    return _map_normalized_positions(content, content_normalized, matches_in_normalized)


def _strategy_indentation_flexible(content: str, pattern: str) -> List[Tuple[int, int]]:
    """
    Strategy 4: Ignore indentation differences entirely.
    
    Strips all leading whitespace from lines before matching.
    """
    content_lines = content.split('\n')
    content_stripped_lines = [line.lstrip() for line in content_lines]
    pattern_lines = [line.lstrip() for line in pattern.split('\n')]
    
    return _find_normalized_matches(
        content, content_lines, content_stripped_lines,
        pattern, '\n'.join(pattern_lines)
    )


def _strategy_escape_normalized(content: str, pattern: str) -> List[Tuple[int, int]]:
    """
    Strategy 5: Convert escape sequences to actual characters.
    
    Handles \\n -> newline, \\t -> tab, etc.
    """
    def unescape(s):
        # Convert common escape sequences
        return s.replace('\\n', '\n').replace('\\t', '\t').replace('\\r', '\r')
    
    pattern_unescaped = unescape(pattern)
    
    if pattern_unescaped == pattern:
        # No escapes to convert, skip this strategy
        return []
    
    return _strategy_exact(content, pattern_unescaped)


def _strategy_trimmed_boundary(content: str, pattern: str) -> List[Tuple[int, int]]:
    """
    Strategy 6: Trim whitespace from first and last lines only.
    
    Useful when the pattern boundaries have whitespace differences.
    """
    pattern_lines = pattern.split('\n')
    if not pattern_lines:
        return []
    
    # Trim only first and last lines
    pattern_lines[0] = pattern_lines[0].strip()
    if len(pattern_lines) > 1:
        pattern_lines[-1] = pattern_lines[-1].strip()
    
    modified_pattern = '\n'.join(pattern_lines)
    
    content_lines = content.split('\n')
    
    # Search through content for matching block
    matches = []
    pattern_line_count = len(pattern_lines)
    
    for i in range(len(content_lines) - pattern_line_count + 1):
        block_lines = content_lines[i:i + pattern_line_count]
        
        # Trim first and last of this block
        check_lines = block_lines.copy()
        check_lines[0] = check_lines[0].strip()
        if len(check_lines) > 1:
            check_lines[-1] = check_lines[-1].strip()
        
        if '\n'.join(check_lines) == modified_pattern:
            # Found match - calculate original positions
            start_pos, end_pos = _calculate_line_positions(
                content_lines, i, i + pattern_line_count, len(content)
            )
            matches.append((start_pos, end_pos))
    
    return matches


def _build_orig_to_norm_map(original: str) -> List[int]:
    """Build a list mapping each original character index to its normalized index.

    Because UNICODE_MAP replacements may expand characters (e.g. em-dash → '--',
    ellipsis → '...'), the normalised string can be longer than the original.
    This map lets us convert positions in the normalised string back to the
    corresponding positions in the original string.

    Returns a list of length ``len(original) + 1``; entry ``i`` is the
    normalised index that character ``i`` maps to.
    """
    result: List[int] = []
    norm_pos = 0
    for char in original:
        result.append(norm_pos)
        repl = UNICODE_MAP.get(char)
        norm_pos += len(repl) if repl is not None else 1
    result.append(norm_pos)  # sentinel: one past the last character
    return result


def _map_positions_norm_to_orig(
    orig_to_norm: List[int],
    norm_matches: List[Tuple[int, int]],
) -> List[Tuple[int, int]]:
    """Convert (start, end) positions in the normalised string to original positions."""
    # Invert the map: norm_pos -> first original position with that norm_pos
    norm_to_orig_start: dict[int, int] = {}
    for orig_pos, norm_pos in enumerate(orig_to_norm[:-1]):
        if norm_pos not in norm_to_orig_start:
            norm_to_orig_start[norm_pos] = orig_pos

    results: List[Tuple[int, int]] = []
    orig_len = len(orig_to_norm) - 1  # number of original characters

    for norm_start, norm_end in norm_matches:
        if norm_start not in norm_to_orig_start:
            continue
        orig_start = norm_to_orig_start[norm_start]

        # Walk forward until orig_to_norm[orig_end] >= norm_end
        orig_end = orig_start
        while orig_end < orig_len and orig_to_norm[orig_end] < norm_end:
            orig_end += 1

        results.append((orig_start, orig_end))

    return results


def _strategy_unicode_normalized(content: str, pattern: str) -> List[Tuple[int, int]]:
    """Strategy 7: Unicode normalisation.

    Normalises smart quotes, em/en-dashes, ellipsis, and non-breaking spaces
    to their ASCII equivalents in both *content* and *pattern*, then runs
    exact and line_trimmed matching on the normalised copies.

    Positions are mapped back to the *original* string via
    ``_build_orig_to_norm_map`` — necessary because some UNICODE_MAP
    replacements expand a single character into multiple ASCII characters,
    making a naïve position copy incorrect.
    """
    # Normalize both sides. Either the content or the pattern (or both) may
    # carry unicode variants — e.g. content has an em-dash that should match
    # the LLM's ASCII '--', or vice-versa.  Skip only when neither changes.
    norm_pattern = _unicode_normalize(pattern)
    norm_content = _unicode_normalize(content)
    if norm_content == content and norm_pattern == pattern:
        return []

    norm_matches = _strategy_exact(norm_content, norm_pattern)
    if not norm_matches:
        norm_matches = _strategy_line_trimmed(norm_content, norm_pattern)

    if not norm_matches:
        return []

    orig_to_norm = _build_orig_to_norm_map(content)
    return _map_positions_norm_to_orig(orig_to_norm, norm_matches)


def _strategy_block_anchor(content: str, pattern: str) -> List[Tuple[int, int]]:
    """
    Strategy 8: Match by anchoring on first and last lines.
    Adjusted with permissive thresholds and unicode normalization.
    """
    # Normalize both strings for comparison while keeping original content for offset calculation
    norm_pattern = _unicode_normalize(pattern)
    norm_content = _unicode_normalize(content)
    
    pattern_lines = norm_pattern.split('\n')
    if len(pattern_lines) < 2:
        return []
    
    first_line = pattern_lines[0].strip()
    last_line = pattern_lines[-1].strip()
    
    # Use normalized lines for matching logic
    norm_content_lines = norm_content.split('\n')
    # BUT use original lines for calculating start/end positions to prevent index shift
    orig_content_lines = content.split('\n')
    
    pattern_line_count = len(pattern_lines)
    
    potential_matches = []
    for i in range(len(norm_content_lines) - pattern_line_count + 1):
        if (norm_content_lines[i].strip() == first_line and 
            norm_content_lines[i + pattern_line_count - 1].strip() == last_line):
            potential_matches.append(i)
            
    matches = []
    candidate_count = len(potential_matches)
    
    # Thresholding logic: 0.50 for unique matches, 0.70 for multiple candidates.
    # Previous values (0.10 / 0.30) were dangerously loose — a 10% middle-section
    # similarity could match completely unrelated blocks.
    threshold = 0.50 if candidate_count == 1 else 0.70

    for i in potential_matches:
        if pattern_line_count <= 2:
            similarity = 1.0
        else:
            # Compare normalized middle sections
            content_middle = '\n'.join(norm_content_lines[i+1:i+pattern_line_count-1])
            pattern_middle = '\n'.join(pattern_lines[1:-1])
            similarity = SequenceMatcher(None, content_middle, pattern_middle).ratio()
        
        if similarity >= threshold:
            # Calculate positions using ORIGINAL lines to ensure correct character offsets in the file
            start_pos, end_pos = _calculate_line_positions(
                orig_content_lines, i, i + pattern_line_count, len(content)
            )
            matches.append((start_pos, end_pos))
    
    return matches


def _strategy_context_aware(content: str, pattern: str) -> List[Tuple[int, int]]:
    """
    Strategy 9: Line-by-line similarity with 50% threshold.
    
    Finds blocks where at least 50% of lines have high similarity.
    """
    pattern_lines = pattern.split('\n')
    content_lines = content.split('\n')
    
    if not pattern_lines:
        return []
    
    matches = []
    pattern_line_count = len(pattern_lines)
    
    for i in range(len(content_lines) - pattern_line_count + 1):
        block_lines = content_lines[i:i + pattern_line_count]
        
        # Calculate line-by-line similarity
        high_similarity_count = 0
        for p_line, c_line in zip(pattern_lines, block_lines):
            sim = SequenceMatcher(None, p_line.strip(), c_line.strip()).ratio()
            if sim >= 0.80:
                high_similarity_count += 1
        
        # Need at least 50% of lines to have high similarity
        if high_similarity_count >= len(pattern_lines) * 0.5:
            start_pos, end_pos = _calculate_line_positions(
                content_lines, i, i + pattern_line_count, len(content)
            )
            matches.append((start_pos, end_pos))
    
    return matches


# =============================================================================
# Helper Functions
# =============================================================================

def _calculate_line_positions(content_lines: List[str], start_line: int,
                              end_line: int, content_length: int) -> Tuple[int, int]:
    """Calculate start and end character positions from line indices.

    Args:
        content_lines: List of lines (without newlines)
        start_line: Starting line index (0-based)
        end_line: Ending line index (exclusive, 0-based)
        content_length: Total length of the original content string

    Returns:
        Tuple of (start_pos, end_pos) in the original content
    """
    start_pos = sum(len(line) + 1 for line in content_lines[:start_line])
    end_pos = sum(len(line) + 1 for line in content_lines[:end_line]) - 1
    end_pos = min(content_length, end_pos)
    return start_pos, end_pos


def _find_normalized_matches(content: str, content_lines: List[str],
                              content_normalized_lines: List[str],
                              pattern: str, pattern_normalized: str) -> List[Tuple[int, int]]:
    """
    Find matches in normalized content and map back to original positions.
    
    Args:
        content: Original content string
        content_lines: Original content split by lines
        content_normalized_lines: Normalized content lines
        pattern: Original pattern
        pattern_normalized: Normalized pattern
    
    Returns:
        List of (start, end) positions in the original content
    """
    pattern_norm_lines = pattern_normalized.split('\n')
    num_pattern_lines = len(pattern_norm_lines)
    
    matches = []
    
    for i in range(len(content_normalized_lines) - num_pattern_lines + 1):
        # Check if this block matches
        block = '\n'.join(content_normalized_lines[i:i + num_pattern_lines])
        
        if block == pattern_normalized:
            # Found a match - calculate original positions
            start_pos, end_pos = _calculate_line_positions(
                content_lines, i, i + num_pattern_lines, len(content)
            )
            matches.append((start_pos, end_pos))
    
    return matches


def _map_normalized_positions(original: str, normalized: str,
                               normalized_matches: List[Tuple[int, int]]) -> List[Tuple[int, int]]:
    """
    Map positions from normalized string back to original.
    
    This is a best-effort mapping that works for whitespace normalization.
    """
    if not normalized_matches:
        return []
    
    # Build character mapping from normalized to original
    orig_to_norm = []  # orig_to_norm[i] = position in normalized
    
    orig_idx = 0
    norm_idx = 0
    
    while orig_idx < len(original) and norm_idx < len(normalized):
        if original[orig_idx] == normalized[norm_idx]:
            orig_to_norm.append(norm_idx)
            orig_idx += 1
            norm_idx += 1
        elif original[orig_idx] in ' \t' and normalized[norm_idx] == ' ':
            # Original has space/tab, normalized collapsed to space
            orig_to_norm.append(norm_idx)
            orig_idx += 1
            # Don't advance norm_idx yet - wait until all whitespace consumed
            if orig_idx < len(original) and original[orig_idx] not in ' \t':
                norm_idx += 1
        elif original[orig_idx] in ' \t':
            # Extra whitespace in original
            orig_to_norm.append(norm_idx)
            orig_idx += 1
        else:
            # Mismatch - shouldn't happen with our normalization
            orig_to_norm.append(norm_idx)
            orig_idx += 1
    
    # Fill remaining
    while orig_idx < len(original):
        orig_to_norm.append(len(normalized))
        orig_idx += 1
    
    # Reverse mapping: for each normalized position, find original range
    norm_to_orig_start = {}
    norm_to_orig_end = {}
    
    for orig_pos, norm_pos in enumerate(orig_to_norm):
        if norm_pos not in norm_to_orig_start:
            norm_to_orig_start[norm_pos] = orig_pos
        norm_to_orig_end[norm_pos] = orig_pos
    
    # Map matches
    original_matches = []
    for norm_start, norm_end in normalized_matches:
        # Find original start
        if norm_start in norm_to_orig_start:
            orig_start = norm_to_orig_start[norm_start]
        else:
            # Find nearest
            orig_start = min(i for i, n in enumerate(orig_to_norm) if n >= norm_start)
        
        # Find original end
        if norm_end - 1 in norm_to_orig_end:
            orig_end = norm_to_orig_end[norm_end - 1] + 1
        else:
            orig_end = orig_start + (norm_end - norm_start)
        
        # Expand to include trailing whitespace that was normalized,
        # but only when the normalized match itself ended with whitespace.
        # When the match ends with a non-space character, the first
        # whitespace in the original is a word boundary and must not be
        # consumed.  See https://github.com/NousResearch/hermes-agent/issues/52491
        if norm_end < len(normalized) and normalized[norm_end - 1] == ' ':
            while orig_end < len(original) and original[orig_end] in ' \t':
                orig_end += 1
        
        original_matches.append((orig_start, min(orig_end, len(original))))
    
    return original_matches


def find_closest_lines(old_string: str, content: str, context_lines: int = 2, max_results: int = 3) -> str:
    """Find lines in content most similar to old_string for "did you mean?" feedback.

    Returns a formatted string showing the closest matching lines with context,
    or empty string if no useful match is found.
    """
    if not old_string or not content:
        return ""

    old_lines = old_string.splitlines()
    content_lines = content.splitlines()

    if not old_lines or not content_lines:
        return ""

    # Use first line of old_string as anchor for search
    anchor = old_lines[0].strip()
    if not anchor:
        # Try second line if first is blank
        candidates = [l.strip() for l in old_lines if l.strip()]
        if not candidates:
            return ""
        anchor = candidates[0]

    # Score each line in content by similarity to anchor
    scored = []
    for i, line in enumerate(content_lines):
        stripped = line.strip()
        if not stripped:
            continue
        ratio = SequenceMatcher(None, anchor, stripped).ratio()
        if ratio > 0.3:
            scored.append((ratio, i))

    if not scored:
        return ""

    # Take top matches
    scored.sort(key=lambda x: -x[0])
    top = scored[:max_results]

    parts = []
    seen_ranges = set()
    for _, line_idx in top:
        start = max(0, line_idx - context_lines)
        end = min(len(content_lines), line_idx + len(old_lines) + context_lines)
        key = (start, end)
        if key in seen_ranges:
            continue
        seen_ranges.add(key)
        snippet = "\n".join(
            f"{start + j + 1:4d}| {content_lines[start + j]}"
            for j in range(end - start)
        )
        parts.append(snippet)

    if not parts:
        return ""

    return "\n---\n".join(parts)


def format_no_match_hint(error: Optional[str], match_count: int,
                         old_string: str, content: str) -> str:
    """Return a '\\n\\nDid you mean...' snippet for plain no-match errors.

    Gated so the hint only fires for actual "old_string not found" failures.
    Ambiguous-match ("Found N matches"), escape-drift, and identical-strings
    errors all have ``match_count == 0`` but a "did you mean?" snippet would
    be misleading — those failed for unrelated reasons.

    Returns an empty string when there's nothing useful to append.
    """
    if match_count != 0:
        return ""
    if not error or not error.startswith("Could not find"):
        return ""
    hint = find_closest_lines(old_string, content)
    if not hint:
        return ""
    return "\n\nDid you mean one of these sections?\n" + hint
