"""
Docs-to-context-pack mapping and relevance rules for richer AI form generation.
Consumes LLM documentation (from docs API) and builds token-budgeted prompt sections
based on detected intent (conditional logic, document extraction, advanced fields).
"""

from typing import Any, Dict, List, Optional, Tuple
import json
import logging

logger = logging.getLogger(__name__)

# Approximate tokens from chars (conservative for Latin text)
CHARS_PER_TOKEN = 4

# --- Intent detection keywords (plan: Smart Context Strategy) ---
CONDITIONAL_LOGIC_SIGNALS = [
    "if", "when", "depends", "show", "hide", "branch", "conditional",
    "only when", "based on", "depending on", "skip", "reveal",
]
DOC_EXTRACTION_SIGNALS = [
    "resume", "cv", "invoice", "receipt", "id card", "parse", "extract",
    "document extraction", "upload and extract", "pdf", "scan",
]
ADVANCED_FIELD_SIGNALS = [
    "matrix", "ranking", "nps", "likert", "rating", "score", "slider",
    "validation", "validate", "scale", "grid",
]

# --- Pack names and doc section mapping ---
PACK_FIELD_TYPES = "fieldTypes"
PACK_CONDITIONAL_LOGIC = "conditionalLogic"
PACK_DOC_EXTRACTION = "aiFields"
PACK_EXAMPLES = "exampleForms"
PACK_BEST_PRACTICES = "bestPractices"
PACK_FORM_SCHEMA = "formSchema"

# Default character budgets (approx 4 chars per token; ~500 tokens per pack)
MAX_CHARS_PER_PACK = 2000
MAX_TOTAL_CONTEXT_CHARS = 8000


def detect_intent(
    description: str = "",
    user_request: Optional[str] = None,
    current_fields: Optional[List[Dict[str, Any]]] = None,
    conversation_context: Optional[List[str]] = None,
) -> Dict[str, bool]:
    """
    Detect which context packs are relevant from user text and current form.
    Returns flags: needs_conditional_logic, needs_document_extraction, needs_advanced_fields.
    """
    text = " ".join(
        filter(
            None,
            [description or "", user_request or ""]
            + (conversation_context or []),
        )
    ).lower()
    has_conditional = any(s in text for s in CONDITIONAL_LOGIC_SIGNALS)
    has_doc_extraction = any(s in text for s in DOC_EXTRACTION_SIGNALS)
    has_advanced = any(s in text for s in ADVANCED_FIELD_SIGNALS)
    # If current form already has conditional logic or document-extraction, include those packs for refine
    if current_fields:
        for f in current_fields:
            if isinstance(f, dict):
                if (f.get("conditionalLogic") or {}).get("enabled"):
                    has_conditional = True
                if f.get("type") == "document-extraction":
                    has_doc_extraction = True
    return {
        "needs_conditional_logic": has_conditional,
        "needs_document_extraction": has_doc_extraction,
        "needs_advanced_fields": has_advanced,
    }


def _truncate(text: str, max_chars: int) -> str:
    if len(text) <= max_chars:
        return text
    return text[: max_chars - 3].rstrip() + "..."


def _pack_field_types(docs: Dict[str, Any], max_chars: int) -> str:
    """Build FieldTypes pack from docs.fieldTypes (summary + key types)."""
    field_types = docs.get("fieldTypes") or {}
    if not field_types:
        return ""
    lines = ["## Field types reference\n"]
    for name, spec in list(field_types.items())[:25]:
        if not isinstance(spec, dict):
            continue
        desc = spec.get("description", "")
        required = spec.get("requiredProperties", [])
        lines.append(f"- **{name}**: {desc[:200]}")
        if required:
            lines.append(f"  Required: {', '.join(required)}")
        ex = spec.get("exampleJSON")
        if ex:
            lines.append(f"  Example: {json.dumps(ex)[:300]}")
    out = "\n".join(lines)
    return _truncate(out, max_chars)


def _pack_conditional_logic(docs: Dict[str, Any], max_chars: int) -> str:
    """Build ConditionalLogic pack from docs.conditionalLogic."""
    cl = docs.get("conditionalLogic") or {}
    if not cl:
        return ""
    lines = [
        "## Conditional logic\n",
        (cl.get("overview") or "")[:500],
        "\n### Operators (use in conditions): ",
    ]
    ops = cl.get("operators") or {}
    for op_name, op_spec in list(ops.items())[:15]:
        if isinstance(op_spec, dict):
            lines.append(f"- {op_name}: {(op_spec.get('description') or '')[:150]}")
    lines.append("\n### Actions: show | hide | validate | cap_responses")
    actions = cl.get("actions") or {}
    for act_name, act_spec in list(actions.items())[:4]:
        if isinstance(act_spec, dict):
            ex = act_spec.get("example")
            if ex:
                lines.append(f"- {act_name}: {json.dumps(ex)[:200]}")
    examples = cl.get("examples") or []
    for ex in examples[:2]:
        if isinstance(ex, dict) and ex.get("json"):
            lines.append(f"Example: {json.dumps(ex['json'])[:250]}")
    out = "\n".join(lines)
    return _truncate(out, max_chars)


def _pack_doc_extraction(docs: Dict[str, Any], max_chars: int) -> str:
    """Build DocExtraction pack from docs.aiFields.documentExtraction."""
    ai = docs.get("aiFields") or {}
    doc_ext = ai.get("documentExtraction") if isinstance(ai, dict) else None
    if not doc_ext:
        return ""
    lines = [
        "## Document extraction field (document-extraction)\n",
        (doc_ext.get("description") or "")[:400],
        "\n### customFields: array of { id, name, description, fieldType, required, editable }",
        "fieldType: single_value | list | number | date",
        "Always set acceptedFileTypes (e.g. ['pdf','doc','docx','png','jpg','jpeg']) and maxFileSize in bytes (e.g. 10485760 for 10MB). Do not leave customFields empty.",
        "verificationPrompt: descriptive text only (full sentences) stating how to verify the candidate against extracted data. Use the user's must-have/role requirements (e.g. from clarifying answers: years of experience, degree, skills). Never put only a number (e.g. 80) — invalid; use criteria like 'Candidate must have 2+ years experience, masters in CS, and LangGraph expertise.'",
    ]
    config = doc_ext.get("configuration") or {}
    if isinstance(config, dict):
        cf = config.get("customFields") or {}
        if isinstance(cf, dict) and cf.get("fieldTypes"):
            lines.append("Field types: " + json.dumps(list((cf["fieldTypes"] or {}).keys())))
    example_configs = doc_ext.get("exampleConfigurations") or []
    for ex in example_configs[:2]:
        if isinstance(ex, dict) and ex.get("json"):
            lines.append(f"Example: {json.dumps(ex['json'])[:400]}")
    out = "\n".join(lines)
    return _truncate(out, max_chars)


def _pack_examples(docs: Dict[str, Any], max_chars: int, intent: Dict[str, bool]) -> str:
    """Build Examples pack: 1–3 examples matching intent."""
    examples = docs.get("exampleForms") or []
    if not examples:
        return ""
    chosen = []
    for ex in examples:
        if not isinstance(ex, dict) or not ex.get("json"):
            continue
        title = (ex.get("title") or "").lower()
        features = ex.get("featuresUsed") or []
        if intent.get("needs_document_extraction") and (
            "document-extraction" in features or "resume" in title or "extraction" in title
        ):
            chosen.append(ex)
        elif intent.get("needs_conditional_logic") and "conditionalLogic" in str(features):
            chosen.append(ex)
        elif not chosen:
            chosen.append(ex)
        if len(chosen) >= 3:
            break
    if not chosen:
        chosen = examples[:2]
    lines = ["## Example forms (reference only)\n"]
    for ex in chosen:
        lines.append(f"### {ex.get('title', 'Form')}")
        lines.append(json.dumps(ex.get("json") or {}, indent=2)[:800])
    out = "\n".join(lines)
    return _truncate(out, max_chars)


def build_context_packs(
    docs: Dict[str, Any],
    intent: Dict[str, bool],
    max_chars_per_pack: int = MAX_CHARS_PER_PACK,
    max_total_chars: int = MAX_TOTAL_CONTEXT_CHARS,
    log_usage: bool = True,
) -> str:
    """
    Build a single formatted context string from docs and intent.
    Packs are included by relevance; total output is capped by max_total_chars.
    When log_usage is True, logs pack names and approximate token count.
    """
    sections = []
    used = 0
    pack_names: List[str] = []

    # 1. Form schema (compact, always useful)
    form_schema = docs.get("formSchema") or {}
    if form_schema:
        schema_str = json.dumps(form_schema.get("example") or form_schema)[:800]
        block = f"## Form structure\n{schema_str}\n"
        if used + len(block) <= max_total_chars:
            sections.append(block)
            used += len(block)
            pack_names.append("formSchema")

    # 2. Field types (always include, truncated)
    block = _pack_field_types(docs, max_chars_per_pack)
    if block and used + len(block) <= max_total_chars:
        sections.append(block)
        used += len(block)
        pack_names.append("fieldTypes")

    # 3. Conditional logic (if intent says so)
    if intent.get("needs_conditional_logic"):
        block = _pack_conditional_logic(docs, max_chars_per_pack)
        if block and used + len(block) <= max_total_chars:
            sections.append(block)
            used += len(block)
            pack_names.append("conditionalLogic")

    # 4. Document extraction (if intent says so)
    if intent.get("needs_document_extraction"):
        block = _pack_doc_extraction(docs, max_chars_per_pack)
        if block and used + len(block) <= max_total_chars:
            sections.append(block)
            used += len(block)
            pack_names.append("docExtraction")

    # 5. Examples (1–3 matching intent)
    block = _pack_examples(docs, max_chars_per_pack, intent)
    if block and used + len(block) <= max_total_chars:
        sections.append(block)
        used += len(block)
        pack_names.append("examples")

    # 6. Best practices (short)
    practices = docs.get("bestPractices") or []
    if practices and used < max_total_chars:
        block = "## Best practices\n" + "\n".join(f"- {p}" for p in practices[:8])
        block = _truncate(block, max_total_chars - used)
        if block:
            sections.append(block)
            used += len(block)
            pack_names.append("bestPractices")

    out = "\n\n".join(sections) if sections else ""
    if log_usage and pack_names:
        approx_tokens = len(out) // CHARS_PER_TOKEN
        logger.info(
            "context_packs built packs=%s total_chars=%s approx_tokens=%s",
            pack_names,
            len(out),
            approx_tokens,
        )
    return out