from __future__ import annotations

import json
import re
from typing import Any, Iterable, List

from .types import GroundedEvidence, ReasoningStep


_JSON_FENCE_RE = re.compile(r"```(?:json)?(.*?)```", re.DOTALL | re.IGNORECASE)
_STEP_MARKER_RE = re.compile(r"(?im)(?:^|\n)\s*(?:step\s*(\d+)|(\d+)[\.\)])\s*[:\-]?\s*")
_NEEDS_VISION_RE = re.compile(
    r"needs[\s_]*vision\s*[:\-]?\s*(?P<value>true|false|yes|no|required|not required|necessary|unnecessary)",
    re.IGNORECASE,
)
_REASON_RE = re.compile(r"reason\s*[:\-]\s*(?P<value>.+)", re.IGNORECASE)
_BOX_RE = re.compile(
    r"\[\s*-?\d+(?:\.\d+)?\s*,\s*-?\d+(?:\.\d+)?\s*,\s*-?\d+(?:\.\d+)?\s*,\s*-?\d+(?:\.\d+)?\s*\]"
)

_ORDINAL_WORD_MAP = {
    "first": 1,
    "second": 2,
    "third": 3,
    "fourth": 4,
    "fifth": 5,
    "sixth": 6,
    "seventh": 7,
    "eighth": 8,
    "ninth": 9,
    "tenth": 10,
}

_NUMBER_WORD_MAP = {
    "one": 1,
    "two": 2,
    "three": 3,
    "four": 4,
    "five": 5,
    "six": 6,
    "seven": 7,
    "eight": 8,
    "nine": 9,
    "ten": 10,
}

_ORDINAL_STEP_RE = re.compile(
    r"(?im)\b(?P<word>first|second|third|fourth|fifth|sixth|seventh|eighth|ninth|tenth)\s+step\b"
)
_WORD_STEP_RE = re.compile(
    r"(?im)\bstep\s+(?P<word>one|two|three|four|five|six|seven|eight|nine|ten)\b"
)

_META_TOKENS = {"maybe", "wait", "let's", "lets", "question", "protocol"}


def _to_bool(value: Any) -> bool:
    if isinstance(value, bool):
        return value
    if value is None:
        return False
    if isinstance(value, (int, float)):
        return value != 0
    if isinstance(value, str):
        lowered = value.strip().lower()
        if lowered in {"true", "t", "yes", "y", "1"}:
            return True
        if lowered in {"false", "f", "no", "n", "0"}:
            return False
    return False


def _extract_json_strings(text: str) -> Iterable[str]:
    """Return candidate JSON payloads from the response text."""

    fenced = _JSON_FENCE_RE.findall(text)
    if fenced:
        for body in fenced:
            yield body.strip()
    stripped = text.strip()
    if stripped:
        yield stripped


def _load_first_json(text: str) -> Any:
    last_error = None
    for candidate in _extract_json_strings(text):
        try:
            return json.loads(candidate)
        except json.JSONDecodeError as err:
            last_error = err
            continue
    if last_error:
        raise ValueError(f"Unable to parse JSON from response: {last_error}") from last_error
    raise ValueError("Empty response, cannot parse JSON.")


def _trim_reasoning_text(text: str) -> str:
    lowered = text.lower()
    for anchor in ("let's draft", "draft:", "structured steps", "final reasoning"):
        pos = lowered.rfind(anchor)
        if pos != -1:
            return text[pos:]
    return text


def _clean_sentence(text: str) -> str:
    return " ".join(text.strip().split())


def _normalize_step_markers(text: str) -> str:
    """Convert ordinal step markers into numeric form (e.g., 'First step' -> 'Step 1')."""

    def replace_ordinal(match: re.Match[str]) -> str:
        word = match.group("word").lower()
        num = _ORDINAL_WORD_MAP.get(word)
        return f"Step {num}" if num is not None else match.group(0)

    def replace_word_number(match: re.Match[str]) -> str:
        word = match.group("word").lower()
        num = _NUMBER_WORD_MAP.get(word)
        return f"Step {num}" if num is not None else match.group(0)

    normalized = _ORDINAL_STEP_RE.sub(replace_ordinal, text)
    normalized = _WORD_STEP_RE.sub(replace_word_number, normalized)
    return normalized


def _extract_statement(body: str) -> str | None:
    statement_match = re.search(r"statement\s*[:\-]\s*(.+?)(?=\s*(?:needs\s*vision|reason\s*[:\-]|$))", body, re.IGNORECASE | re.DOTALL)
    if statement_match:
        candidate = statement_match.group(1)
    else:
        # Fallback: take first sentence or line before metadata
        candidate = re.split(r"(?i)needs\s*vision|reason\s*[:\-]", body)[0]
    
    # Clean up the candidate
    candidate = candidate.strip().rstrip(".,;:")
    
    # If still empty or too short, return None
    if not candidate or len(candidate) < 5:
        return None
    
    return _clean_sentence(candidate)


def _extract_needs_vision(body: str) -> bool:
    match = _NEEDS_VISION_RE.search(body)
    if not match:
        return True
    token = match.group("value").strip().lower()
    if token in {"not required", "unnecessary"}:
        return False
    if token in {"required", "necessary"}:
        return True
    return _to_bool(token)


def _extract_reason(body: str) -> str | None:
    match = _REASON_RE.search(body)
    if match:
        reason = match.group("value").strip()
        reason = re.split(r"(?i)needs\s*vision", reason)[0].strip()
        reason = reason.rstrip(".")
        return reason or None
    because_match = re.search(r"because\s+(.+?)(?:\.|$)", body, re.IGNORECASE)
    if because_match:
        reason = because_match.group(1).strip().rstrip(".")
        return reason or None
    return None


def _parse_step_block(index_guess: int, body: str) -> ReasoningStep | None:
    statement = _extract_statement(body)
    if not statement:
        return None
    needs_vision = _extract_needs_vision(body)
    reason = _extract_reason(body)
    index = index_guess if index_guess > 0 else 1
    return ReasoningStep(index=index, statement=statement, needs_vision=needs_vision, reason=reason)


def _parse_reasoning_from_text(response_text: str, max_steps: int) -> List[ReasoningStep]:
    text = _trim_reasoning_text(response_text)
    text = _normalize_step_markers(text)
    matches = list(_STEP_MARKER_RE.finditer(text))
    if not matches:
        return []
    steps_map: dict[int, ReasoningStep] = {}
    ordering: List[int] = []
    fallback_index = 1
    for idx, marker in enumerate(matches):
        start = marker.end()
        end = matches[idx + 1].start() if idx + 1 < len(matches) else len(text)
        body = text[start:end].strip()
        if not body:
            continue
        raw_index = marker.group(1) or marker.group(2)
        try:
            index_guess = int(raw_index) if raw_index else fallback_index
        except (TypeError, ValueError):
            index_guess = fallback_index
        if raw_index is None:
            fallback_index += 1
        step = _parse_step_block(index_guess, body)
        if step is None:
            continue
        if step.index not in steps_map:
            ordering.append(step.index)
        steps_map[step.index] = step
        if len(ordering) >= max_steps:
            break
    return [steps_map[idx] for idx in ordering[:max_steps]]


def _looks_like_meta_statement(statement: str) -> bool:
    lowered = statement.lower()
    if any(token in lowered for token in _META_TOKENS) and "step" in lowered:
        return True
    if lowered.startswith(("maybe", "wait", "let's", "lets")):
        return True
    if len(statement) > 260 and "step" in lowered:
        return True
    return False


def _prune_steps(steps: List[ReasoningStep]) -> List[ReasoningStep]:
    filtered: List[ReasoningStep] = []
    seen_statements: set[str] = set()
    for step in steps:
        normalized = step.statement.strip().lower()
        if _looks_like_meta_statement(step.statement):
            continue
        if normalized in seen_statements:
            continue
        seen_statements.add(normalized)
        filtered.append(step)
    return filtered or steps


def _extract_description(text: str, start_index: int) -> str | None:
    boundary = max(text.rfind("\n", 0, start_index), text.rfind(".", 0, start_index))
    if boundary == -1:
        boundary = 0
    snippet = text[boundary:start_index].strip(" \n.:–-")
    if not snippet:
        return None
    return _clean_sentence(snippet)


def _parse_roi_from_text(response_text: str, default_step_index: int) -> List[GroundedEvidence]:
    evidences: List[GroundedEvidence] = []
    seen: set[tuple[float, float, float, float]] = set()
    for match in _BOX_RE.finditer(response_text):
        coords_str = match.group(0).strip("[]")
        try:
            coords = [float(part.strip()) for part in coords_str.split(",")]
        except ValueError:
            continue
        if len(coords) != 4:
            continue
        try:
            bbox = _normalize_bbox(coords)
        except ValueError:
            continue
        key = tuple(round(c, 4) for c in bbox)
        if key in seen:
            continue
        description = _extract_description(response_text, match.start())
        evidences.append(
            GroundedEvidence(
                step_index=default_step_index,
                bbox=bbox,
                description=description,
                confidence=None,
                raw_source={"bbox": coords, "description": description},
            )
        )
        seen.add(key)
    return evidences


def parse_structured_reasoning(response_text: str, max_steps: int) -> List[ReasoningStep]:
    """Parse Qwen3-VL structured reasoning output into dataclasses."""

    try:
        payload = _load_first_json(response_text)
    except ValueError as json_error:
        steps = _parse_reasoning_from_text(response_text, max_steps=max_steps)
        if steps:
            return _prune_steps(steps)[:max_steps]
        raise json_error
    if not isinstance(payload, list):
        raise ValueError("Structured reasoning response must be a JSON list.")

    steps: List[ReasoningStep] = []
    for idx, item in enumerate(payload, start=1):
        if not isinstance(item, dict):
            continue
        statement = item.get("statement") or item.get("step") or item.get("text")
        if not isinstance(statement, str):
            continue
        statement = statement.strip()
        if not statement:
            continue
        step_index = item.get("index")
        if not isinstance(step_index, int):
            step_index = idx
        needs_vision = _to_bool(item.get("needs_vision") or item.get("requires_vision"))
        reason = item.get("reason") or item.get("justification")
        if isinstance(reason, str):
            reason = reason.strip() or None
        else:
            reason = None
        steps.append(ReasoningStep(index=step_index, statement=statement, needs_vision=needs_vision, reason=reason))
        if len(steps) >= max_steps:
            break
    steps = _prune_steps(steps)[:max_steps]
    if not steps:
        raise ValueError("No reasoning steps parsed from response.")
    return steps


def _normalize_bbox(bbox: Any) -> tuple[float, float, float, float]:
    if not isinstance(bbox, (list, tuple)) or len(bbox) != 4:
        raise ValueError(f"Bounding box must be a list of 4 numbers, got {bbox!r}")
    coords = []
    for raw in bbox:
        if isinstance(raw, str):
            raw = raw.strip()
            if not raw:
                raw = 0
            else:
                raw = float(raw)
        elif isinstance(raw, (int, float)):
            raw = float(raw)
        else:
            raw = 0.0
        coords.append(raw)
    scale = max(abs(v) for v in coords) if coords else 1.0
    if scale > 1.5:  # assume 0..1000 or pixel coordinates
        coords = [max(0.0, min(v / 1000.0, 1.0)) for v in coords]
    else:
        coords = [max(0.0, min(v, 1.0)) for v in coords]
    x1, y1, x2, y2 = coords
    x_min, x_max = sorted((x1, x2))
    y_min, y_max = sorted((y1, y2))
    return (x_min, y_min, x_max, y_max)


def parse_roi_evidence(response_text: str, default_step_index: int) -> List[GroundedEvidence]:
    """Parse ROI grounding output into evidence structures."""

    try:
        payload = _load_first_json(response_text)
    except ValueError:
        return _parse_roi_from_text(response_text, default_step_index=default_step_index)
    if not isinstance(payload, list):
        raise ValueError("ROI extraction response must be a JSON list.")

    evidences: List[GroundedEvidence] = []
    for item in payload:
        if not isinstance(item, dict):
            continue
        raw_bbox = item.get("bbox") or item.get("bbox_2d") or item.get("box")
        if raw_bbox is None:
            continue
        try:
            bbox = _normalize_bbox(raw_bbox)
        except ValueError:
            continue
        step_index = item.get("step") or item.get("step_index") or default_step_index
        if not isinstance(step_index, int):
            step_index = default_step_index
        description = item.get("description") or item.get("caption") or item.get("detail")
        if isinstance(description, str):
            description = description.strip() or None
        else:
            description = None
        confidence = item.get("confidence") or item.get("score") or item.get("probability")
        if isinstance(confidence, str):
            confidence = confidence.strip()
            confidence = float(confidence) if confidence else None
        elif isinstance(confidence, (int, float)):
            confidence = float(confidence)
        else:
            confidence = None
        evidences.append(
            GroundedEvidence(
                step_index=step_index,
                bbox=bbox,
                description=description,
                confidence=confidence,
                raw_source=item,
            )
        )
    return evidences