"""Result dataclasses and pure text helpers shared by ``tools.file_operations``
and its search/lint mixins.

Leaf module (imports nothing from ``tools`` at module scope) so the mixins and
the origin module can all depend on it without an import cycle. The
``to_dict`` output of every class here IS tool behavior — key names, key
order, and omission rules are pinned by tests and read by the model.
"""

import re
from dataclasses import dataclass, field
from typing import Any, ClassVar, Dict, List, Optional


@dataclass
class ReadResult:
    """Result from reading a file."""
    content: str = ""
    total_lines: int = 0
    file_size: int = 0
    truncated: bool = False
    hint: Optional[str] = None
    is_binary: bool = False
    is_image: bool = False
    base64_content: Optional[str] = None
    mime_type: Optional[str] = None
    dimensions: Optional[str] = None  # For images: "WIDTHxHEIGHT"
    error: Optional[str] = None
    similar_files: List[str] = field(default_factory=list)

    def to_dict(self) -> dict:
        return {k: v for k, v in self.__dict__.items() if v is not None and v != []}


@dataclass
class WriteResult:
    """Result from writing a file."""
    bytes_written: int = 0
    dirs_created: bool = False
    # True when the on-disk sha256 matched the intended content; None when the
    # backend couldn't verify (no sha256sum). A mismatch is a hard error, never a flag.
    verified: Optional[bool] = None
    lint: Optional[Dict[str, Any]] = None
    # LSP semantic diagnostics, kept separate from ``lint`` (syntax) so the model
    # reads the two as independent signals. None when LSP is off/inapplicable.
    lsp_diagnostics: Optional[str] = None
    error: Optional[str] = None
    warning: Optional[str] = None

    def to_dict(self) -> dict:
        return {k: v for k, v in self.__dict__.items() if v is not None}


@dataclass
class PatchResult:
    """Result from patching a file."""
    success: bool = False
    diff: str = ""
    files_modified: List[str] = field(default_factory=list)
    files_created: List[str] = field(default_factory=list)
    files_deleted: List[str] = field(default_factory=list)
    lint: Optional[Dict[str, Any]] = None
    lsp_diagnostics: Optional[str] = None  # see WriteResult.lsp_diagnostics
    error: Optional[str] = None
    # Success-shaped no-op: the edit was already present, nothing written; ``note`` says why.
    no_change: bool = False
    note: Optional[str] = None

    # Emission order is part of the output contract.
    _DICT_FIELDS: ClassVar[tuple] = (
        "diff", "files_modified", "files_created", "files_deleted",
        "lint", "lsp_diagnostics", "error",
    )

    def to_dict(self) -> dict:
        result: Dict[str, Any] = {"success": self.success}
        if self.no_change:
            result["no_change"] = True
        if self.note:
            result["note"] = self.note
        for key in self._DICT_FIELDS:
            value = getattr(self, key)
            if value:
                result[key] = value
        return result


@dataclass
class SearchMatch:
    """A single search match."""
    path: str
    line_number: int
    content: str
    mtime: float = 0.0  # Modification time for sorting


@dataclass
class SearchResult:
    """Result from searching."""
    matches: List[SearchMatch] = field(default_factory=list)
    files: List[str] = field(default_factory=list)
    counts: Dict[str, int] = field(default_factory=dict)
    total_count: int = 0
    truncated: bool = False
    limit_reason: Optional[str] = None
    warning: Optional[str] = None
    error: Optional[str] = None

    # Below this many matches the verbose array is already compact enough that
    # a path-grouping header would cost more tokens than it saves.
    _DENSIFY_MIN_MATCHES: ClassVar[int] = 5

    def _densify_matches(self) -> Optional[str]:
        """Render content matches as a lossless, path-grouped text block.

        Path printed once, then ``  <line>: <content>`` rows. Relies on rg/grep
        emitting a file's hits consecutively, so grouping on path change needs
        no reordering. Returns None when too few matches to be worth it.
        """
        if len(self.matches) < self._DENSIFY_MIN_MATCHES:
            return None
        lines: list[str] = []
        current_path: Optional[str] = None
        for m in self.matches:
            if m.path != current_path:
                lines.append(m.path)
                current_path = m.path
            # rstrip only: leading indentation is meaningful code and kept verbatim.
            lines.append(f"  {m.line_number}: {m.content.rstrip()}")
        return "\n".join(lines)

    def to_dict(self, densify: bool = False) -> dict:
        result: dict[str, object] = {"total_count": self.total_count}
        if self.matches:
            dense = self._densify_matches() if densify else None
            if dense is not None:
                # Self-describing so the model never guesses the block's shape.
                result["matches_format"] = (
                    "path-grouped: each file path on its own line, followed by "
                    "indented '<line>: <content>' rows for matches in that file"
                )
                result["matches_text"] = dense
            else:
                result["matches"] = [
                    {"path": m.path, "line": m.line_number, "content": m.content}
                    for m in self.matches
                ]
        if self.files:
            result["files"] = self.files
        if self.counts:
            result["counts"] = self.counts
        if self.truncated:
            result["truncated"] = True
        for key in ("limit_reason", "warning", "error"):
            value = getattr(self, key)
            if value:
                result[key] = value
        return result


@dataclass
class LintResult:
    """Result from linting a file."""
    success: bool = True
    skipped: bool = False
    output: str = ""
    message: str = ""

    def to_dict(self) -> dict:
        if self.skipped:
            return {"status": "skipped", "message": self.message}
        result = {"status": "ok" if self.success else "error", "output": self.output}
        if self.message:
            result["message"] = self.message
        return result


@dataclass
class ExecuteResult:
    """Result from executing a shell command."""
    stdout: str = ""
    exit_code: int = 0


# ---------------------------------------------------------------------------
# Pure text helpers (no I/O)
# ---------------------------------------------------------------------------

_OSC_SEQUENCE_RE = re.compile(r"\x1b\][^\x07\x1b]*(?:\x07|\x1b\\)")
_FENCE_MARKER_RE = re.compile(r"'?\x07?__HERMES_FENCE_[A-Za-z0-9]+__\x07?'?")


def _strip_terminal_fence_leaks(text: str) -> str:
    """Strip leaked terminal fence wrappers (OSC sequences, fence markers) from
    command output; drops lines that were nothing but wrapper."""
    if not text:
        return text
    cleaned_lines: List[str] = []
    for line in text.splitlines(keepends=True):
        had_terminal_wrapper = "__HERMES_FENCE_" in line or "\x1b]" in line
        cleaned = _OSC_SEQUENCE_RE.sub("", line)
        cleaned = _FENCE_MARKER_RE.sub("", cleaned)
        cleaned = cleaned.replace("\x07", "")
        if had_terminal_wrapper and cleaned.strip("'\r\n\t ") == "":
            continue
        cleaned_lines.append(cleaned)
    return "".join(cleaned_lines)


def _detect_line_ending(sample: str) -> Optional[str]:
    """Dominant line ending of ``sample`` (``\\r\\n`` if any CRLF in the first 4KB,
    else ``\\n``), or None for empty/single-line content.

    Used to preserve a file's endings across write_file/patch: the agent's bare-LF
    tool args would otherwise silently normalize CRLF files, and patch would
    produce mixed endings when only the substituted region changes.
    """
    if not sample:
        return None
    head = sample[:4096]
    if "\r\n" in head:
        return "\r\n"
    if "\n" in head:
        return "\n"
    return None


def _normalize_line_endings(text: str, target: str) -> str:
    """Convert every line ending (CRLF, lone CR, LF) in ``text`` to ``target``.
    Idempotent. Collapses to LF first — separate replacements would
    double-convert CRLF → LFLF."""
    lf_normalized = text.replace("\r\n", "\n").replace("\r", "\n")
    if target == "\n":
        return lf_normalized
    if target == "\r\n":
        return lf_normalized.replace("\n", "\r\n")
    return text


# UTF-8 BOM (EF BB BF == U+FEFF), prepended by some Windows editors. Stripped on
# read so the model never sees a phantom first character (and patch's first-line
# match works), restored on write when the on-disk file had one — mirroring the
# line-ending preservation above.
_UTF8_BOM = "\ufeff"


def _strip_bom(text: str) -> tuple[str, bool]:
    """Return (text-without-leading-BOM, had_bom). Only a leading BOM is
    stripped; mid-content U+FEFF is legitimate data."""
    if text and text.startswith(_UTF8_BOM):
        return text[len(_UTF8_BOM):], True
    return text, False


def _has_bom(text: Optional[str]) -> bool:
    """True if ``text`` begins with a UTF-8 BOM."""
    return bool(text) and text.startswith(_UTF8_BOM)


# ---------------------------------------------------------------------------
# Pagination clamps
# ---------------------------------------------------------------------------

DEFAULT_READ_OFFSET = 1
DEFAULT_READ_LIMIT = 2000
DEFAULT_SEARCH_OFFSET = 0
DEFAULT_SEARCH_LIMIT = 50


def _coerce_int(value: Any, default: int) -> int:
    """Best-effort integer coercion for tool pagination inputs."""
    try:
        return int(value)
    except (TypeError, ValueError):
        return default


def normalize_read_pagination(offset: Any = DEFAULT_READ_OFFSET,
                              limit: Any = DEFAULT_READ_LIMIT) -> tuple[int, int]:
    """Clamp read_file pagination so invalid values can never reach a sed range
    like ``0,-1p`` (schemas declare bounds, but not every caller enforces them).
    The ``limit`` ceiling is ``tool_output.max_lines`` from config.yaml."""
    from tools.tool_output_limits import get_max_lines
    max_lines = get_max_lines()
    normalized_offset = max(1, _coerce_int(offset, DEFAULT_READ_OFFSET))
    normalized_limit = _coerce_int(limit, DEFAULT_READ_LIMIT)
    normalized_limit = max(1, min(normalized_limit, max_lines))
    return normalized_offset, normalized_limit


def normalize_search_pagination(offset: Any = DEFAULT_SEARCH_OFFSET,
                                limit: Any = DEFAULT_SEARCH_LIMIT) -> tuple[int, int]:
    """Return safe search pagination bounds for shell head/tail pipelines."""
    normalized_offset = max(0, _coerce_int(offset, DEFAULT_SEARCH_OFFSET))
    normalized_limit = max(1, _coerce_int(limit, DEFAULT_SEARCH_LIMIT))
    return normalized_offset, normalized_limit