"""
Source file processing pipeline for infospace tooling.

Processes source files through the pipeline stages defined in
``infospace.yaml`` using simple ``@{macro}`` template substitution —
no SQLite or space-resolver dependencies.

Example usage from CLI::

    markitect infospace process "book-1-chapter-0[1-3].md" \\
        --provider openrouter --check-after-each

Or programmatically::

    from markitect.infospace.pipeline import SourcePipeline
    from markitect.llm import create_adapter

    adapter = create_adapter("openrouter")
    pipeline = SourcePipeline(config, root, adapter=adapter)
    pipeline.process_sources(source_files)
"""

from __future__ import annotations

import re
import subprocess
import time
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple

import yaml

from markitect.infospace.config import InfospaceConfig, PipelineStage

# Default max_tokens when not specified on a stage
_DEFAULT_MAX_TOKENS = 4096


class SourcePipeline:
    """Processes source files through infospace pipeline stages.

    Each stage in :attr:`config.pipeline.stages` is run in order for
    every source file.  Stage outputs are passed as macros to subsequent
    stages via the ``output_macro`` field.

    Template substitution uses ``@{macro_name}`` tokens.  Static macros
    are loaded from files listed under ``stage.macros``; dynamic macros
    include ``chapter_text`` (source file content), ``existing_entities``
    (for split-entities stages), and outputs from prior stages.
    """

    _PROVIDER_DEFAULTS: Dict[str, str] = {
        "openrouter": "arcee-ai/trinity-large-preview:free",
    }

    def __init__(
        self,
        config: InfospaceConfig,
        root: Path,
        adapter=None,
        provider: str = "",
        model: str = "",
        no_commit: bool = False,
    ) -> None:
        self.config = config
        self.root = root
        self.adapter = adapter
        self.provider = provider
        self.model = model
        self.no_commit = no_commit

    # ── Public API ────────────────────────────────────────────────────

    def process_sources(
        self,
        source_files: List[Path],
        check_after_each: bool = False,
    ) -> int:
        """Process a list of source files through all pipeline stages.

        Args:
            source_files: Ordered list of source files to process.
            check_after_each: If True, run collection checks after each
                source and record a metrics snapshot.

        Returns:
            Number of source files fully processed (all stages complete).
        """
        completed = 0
        for source_file in source_files:
            success = self.process_source(source_file)
            if success:
                completed += 1
                if check_after_each:
                    self.run_collection_check()
        return completed

    def process_source(self, source_file: Path) -> bool:
        """Run the full pipeline for a single source file.

        Args:
            source_file: Path to the source markdown file.

        Returns:
            True if all stages completed successfully.
        """
        source_id = source_file.stem
        source_content = source_file.read_text(encoding="utf-8")
        stage_outputs: Dict[str, str] = {}
        stage_logs: List[Dict[str, Any]] = []

        print(f"\nProcessing: {source_id}")
        print("=" * 60)

        if not self.config.pipeline or not self.config.pipeline.stages:
            print("  No pipeline stages configured.")
            return False

        for stage in self.config.pipeline.stages:
            content, stage_meta = self._run_stage(
                stage, source_id, source_content, stage_outputs
            )
            if stage_meta:
                stage_logs.append(stage_meta)
            if content is None:
                stage_label = stage.name or stage.template
                print(f"\n  Pipeline paused at stage '{stage_label}'.")
                self._write_processing_log(source_id, stage_logs, success=False)
                return False
            if stage.output_macro:
                stage_outputs[stage.output_macro] = content

        print(f"\n  {source_id}: all stages complete.")
        self._write_processing_log(source_id, stage_logs, success=True)
        if not self.no_commit:
            self._git_commit(source_id)

        return True

    def run_collection_check(self) -> None:
        """Run collection-level quality checks (C1–C5) and print metrics."""
        from markitect.infospace.entity_parser import parse_entity_directory
        from markitect.infospace.checks import run_all_checks
        from markitect.infospace.history import record_check_results

        entities_dir = self.root / self.config.entities_dir
        if not entities_dir.is_dir():
            print("\n  Collection check: no entities directory.")
            return

        entities = parse_entity_directory(entities_dir)
        if not entities:
            print("\n  Collection check: no entities found.")
            return

        print(f"\n  Collection check ({len(entities)} entities):")
        report = run_all_checks(entities=entities)
        m = report.metrics()
        for k, v in sorted(m.items()):
            print(f"    {k}: {v:.4f}")

        snap = record_check_results(
            report, self.config, self.root, entity_count=len(entities)
        )
        print(f"    Snapshot recorded: {snap.snapshot_id}")

    # ── Stage Execution ───────────────────────────────────────────────

    def _run_stage(
        self,
        stage: PipelineStage,
        source_id: str,
        source_content: str,
        stage_outputs: Dict[str, str],
    ) -> Tuple[Optional[str], Optional[Dict[str, Any]]]:
        """Run a single pipeline stage.

        Returns:
            ``(content, metadata)`` where *content* is the stage output
            (or ``None`` on failure) and *metadata* is a log dict.
        """
        stage_label = stage.name or stage.template
        print(f"\n  [{stage_label}]")

        output_file = self._get_output_file(stage, source_id)

        # Skip-if-exists — load from disk
        if output_file and output_file.exists():
            print(f"    Found existing output: {output_file.name}")
            if stage.split_entities:
                return self._load_from_view(output_file), None
            return output_file.read_text(encoding="utf-8"), None

        # Build macro substitution dict
        macros = self._build_macros(stage, source_content, stage_outputs)

        # Load and resolve template
        template_path = self.root / stage.template
        if not template_path.exists():
            print(f"    ERROR: Template not found: {stage.template}")
            return None, {"stage": stage_label, "error": f"template not found: {stage.template}"}

        template_content = template_path.read_text(encoding="utf-8")
        prompt = self._resolve_macros(template_content, macros)

        # Write prompt for inspection
        if output_file:
            prompt_file = output_file.parent / f"{source_id}-prompt.md"
            prompt_file.parent.mkdir(parents=True, exist_ok=True)
            prompt_file.write_text(prompt, encoding="utf-8")
            print(f"    Prompt written to {prompt_file.name}")

        # Without an adapter, we cannot generate output
        if self.adapter is None:
            print("    No LLM adapter — skipping generation (manual mode).")
            return None, {"stage": stage_label, "error": "no adapter"}

        # Resolve max_tokens: stage config > pipeline default
        max_tokens = stage.max_tokens if stage.max_tokens is not None else _DEFAULT_MAX_TOKENS

        # Call LLM — with one retry for split_entities stages that return 0 entities
        max_attempts = 2 if stage.split_entities else 1
        entity_files: List[Tuple[str, Path]] = []
        content: Optional[str] = None
        llm_meta: Dict[str, Any] = {}
        total_retries = 0

        for attempt in range(max_attempts):
            content, llm_meta = self._call_llm(prompt, stage_label, max_tokens)
            if content is None:
                meta = {"stage": stage_label, "retries": total_retries, **llm_meta}
                return None, meta

            # Save raw response for debugging (overwritten on retry)
            if output_file:
                raw_file = output_file.parent / f"{source_id}-{stage.name or 'stage'}-raw.md"
                raw_file.parent.mkdir(parents=True, exist_ok=True)
                raw_file.write_text(content, encoding="utf-8")

            if stage.split_entities:
                entity_files = self._split_and_write_entities(stage, content, source_id)
                if entity_files:
                    break  # Got entities — proceed
                if attempt < max_attempts - 1:
                    total_retries += 1
                    print(f"    No entity delimiters found — retrying ({attempt + 2}/{max_attempts})...")
                else:
                    print(
                        f"    WARNING: No '--- ENTITY: ---' markers found after {max_attempts} attempt(s).\n"
                        f"    Check {raw_file.name} to inspect the raw LLM response."
                    )
                    meta = {"stage": stage_label, "retries": total_retries,
                            "error": "no entity delimiters", **llm_meta}
                    return None, meta  # Don't write empty view; allow re-run
            else:
                break  # Non-split stages don't need retry

        stage_meta: Dict[str, Any] = {"stage": stage_label, "retries": total_retries, **llm_meta}

        # Persist output
        if stage.split_entities:
            self._write_entity_view(source_id, entity_files, output_file)
            return content, stage_meta
        else:
            if output_file:
                output_file.parent.mkdir(parents=True, exist_ok=True)
                output_file.write_text(content, encoding="utf-8")
                print(f"    Output written to {output_file.name}")
            return content, stage_meta

    # ── Output File Resolution ────────────────────────────────────────

    def _get_output_file(
        self, stage: PipelineStage, source_id: str
    ) -> Optional[Path]:
        """Return the expected output file path for a stage, or None."""
        if not stage.output_dir or not stage.output_macro:
            return None
        return self.root / stage.output_dir / f"{source_id}-{stage.output_macro}.md"

    # ── Macro Resolution ──────────────────────────────────────────────

    def _build_macros(
        self,
        stage: PipelineStage,
        source_content: str,
        stage_outputs: Dict[str, str],
    ) -> Dict[str, str]:
        """Assemble the macro dict for template substitution."""
        macros: Dict[str, str] = {"chapter_text": source_content}

        # Static macros loaded from files
        for macro_name, rel_path in stage.macros.items():
            file_path = self.root / rel_path
            if file_path.exists():
                macros[macro_name] = file_path.read_text(encoding="utf-8")
            else:
                print(f"    WARNING: macro file not found: {rel_path}")
                macros[macro_name] = f"(File not found: {rel_path})"

        # Existing entities list (for split_entities stages)
        if stage.split_entities:
            entities_dir = self._get_entities_dir(stage)
            existing = self._list_existing_entities(entities_dir)
            if existing:
                macros["existing_entities"] = "\n".join(
                    f"- {s}" for s in existing
                )
            else:
                macros["existing_entities"] = (
                    "(none — this is the first source file)"
                )

        # Outputs from prior stages (override static macros if same name)
        macros.update(stage_outputs)

        return macros

    @staticmethod
    def _resolve_macros(template: str, macros: Dict[str, str]) -> str:
        """Substitute ``@{macro_name}`` tokens with values from *macros*."""

        def replacer(match: re.Match) -> str:
            key = match.group(1)
            return macros.get(key, match.group(0))

        return re.sub(r"@\{([^}]+)\}", replacer, template)

    # ── Entity Splitting ──────────────────────────────────────────────

    def _get_entities_dir(self, stage: Optional[PipelineStage] = None) -> Path:
        """Return the entities output directory."""
        if stage and stage.output_dir:
            return self.root / stage.output_dir
        # Fall back to the first split_entities stage's output_dir
        if self.config.pipeline:
            for s in self.config.pipeline.stages:
                if s.split_entities and s.output_dir:
                    return self.root / s.output_dir
        return self.root / "output" / "entities"

    def _list_existing_entities(self, entities_dir: Path) -> List[str]:
        """Return sorted slugs of all canonical entity files on disk."""
        if not entities_dir.is_dir():
            return []
        return sorted(
            f.stem
            for f in entities_dir.glob("*.md")
            if not f.name.endswith("-entities.md")
            and not f.name.endswith("-prompt.md")
            and not f.name.endswith("-raw.md")
        )

    @staticmethod
    def _normalize_entity_name(name: str) -> str:
        """Normalise entity name to a kebab-case filename stem."""
        slug = name.lower().strip()
        slug = slug.replace("_", "-").replace(" ", "-")
        slug = re.sub(r"[^a-z0-9-]", "", slug)
        slug = re.sub(r"-{2,}", "-", slug)
        return slug.strip("-")

    def _split_and_write_entities(
        self,
        stage: PipelineStage,
        combined_content: str,
        source_id: str = "",
    ) -> List[Tuple[str, Path]]:
        """Split ``--- ENTITY: <name> ---`` delimited output into files.

        Writes each entity to ``<output_dir>/<slug>.md``.  Existing files
        are skipped (first-occurrence wins).

        Returns:
            List of ``(entity_name, file_path)`` for all entities in
            *combined_content* (new and pre-existing alike).
        """
        entities_dir = self._get_entities_dir(stage)
        entities_dir.mkdir(parents=True, exist_ok=True)

        parts = re.split(
            r"^---\s*ENTITY:\s*(.+?)\s*---\s*$",
            combined_content,
            flags=re.MULTILINE,
        )

        entity_files: List[Tuple[str, Path]] = []
        new_count = 0
        skipped_count = 0

        for i in range(1, len(parts), 2):
            entity_name = parts[i]
            entity_content = parts[i + 1].strip() if i + 1 < len(parts) else ""

            slug = self._normalize_entity_name(entity_name)
            if not slug:
                continue

            file_path = entities_dir / f"{slug}.md"
            if file_path.exists():
                skipped_count += 1
            else:
                # Prepend provenance comment so the LLM origin is traceable
                provenance = self._provenance_comment(source_id)
                file_path.write_text(
                    provenance + entity_content + "\n", encoding="utf-8"
                )
                new_count += 1

            entity_files.append((entity_name, file_path))

        msg = f"    {new_count} new entities written"
        if skipped_count:
            msg += f", {skipped_count} pre-existing (skipped)"
        print(msg)
        return entity_files

    def _write_entity_view(
        self,
        source_id: str,
        entity_files: List[Tuple[str, Path]],
        view_file: Optional[Path],
    ) -> None:
        """Write a per-source view file with ``{{ include }}`` directives."""
        if view_file is None:
            return

        lines = [f"# Entities: {source_id}\n"]
        for _name, file_path in entity_files:
            lines.append(f'{{{{ include "{file_path.name}" }}}}')
            lines.append("")
            lines.append("---")
            lines.append("")

        # Remove trailing separator after last entity
        if lines and lines[-1] == "" and len(lines) >= 3 and lines[-2] == "---":
            lines = lines[:-2]

        view_file.parent.mkdir(parents=True, exist_ok=True)
        view_file.write_text("\n".join(lines) + "\n", encoding="utf-8")
        print(f"    View written to {view_file.name}")

    def _load_from_view(self, view_file: Path) -> str:
        """Reconstruct combined entity content from a view file.

        Parses ``{{ include "filename.md" }}`` directives and assembles
        the ``--- ENTITY: <slug> ---`` delimited content for downstream
        stages.
        """
        view_content = view_file.read_text(encoding="utf-8")
        includes = re.findall(
            r'\{\{\s*include\s+"([^"]+)"\s*\}\}', view_content
        )

        entities_dir = view_file.parent
        parts: List[str] = []
        for rel_path in includes:
            file_path = entities_dir / rel_path
            if file_path.exists():
                slug = file_path.stem
                body = file_path.read_text(encoding="utf-8").strip()
                parts.append(f"--- ENTITY: {slug} ---\n\n{body}")

        return "\n\n".join(parts) + "\n" if parts else ""

    # ── Provenance & Processing Log ───────────────────────────────────

    def _provenance_comment(self, source_id: str) -> str:
        """Return an HTML comment tagging the LLM that generated this content."""
        date = datetime.now(timezone.utc).strftime("%Y-%m-%d")
        parts = [f"date={date}", f"source={source_id}"]
        if self.provider:
            parts.insert(0, f"provider={self.provider}")
        if self.model:
            parts.insert(1, f"model={self.model}")
        return f"<!-- generated: {' '.join(parts)} -->\n\n"

    def _write_processing_log(
        self,
        source_id: str,
        stage_logs: List[Dict[str, Any]],
        success: bool,
    ) -> None:
        """Append a run record to ``output/processing-log.yaml``."""
        log_file = self.root / "output" / "processing-log.yaml"
        log_file.parent.mkdir(parents=True, exist_ok=True)

        # Load existing log
        existing: List[Dict[str, Any]] = []
        if log_file.is_file():
            try:
                raw = yaml.safe_load(log_file.read_text(encoding="utf-8"))
                if isinstance(raw, list):
                    existing = raw
            except Exception:
                pass

        # Build new entry
        total_prompt = sum(s.get("prompt_tokens", 0) for s in stage_logs)
        total_completion = sum(s.get("completion_tokens", 0) for s in stage_logs)
        total_cost = sum(s.get("cost", 0.0) for s in stage_logs)
        total_duration = sum(s.get("duration_seconds", 0.0) for s in stage_logs)
        total_retries = sum(s.get("retries", 0) for s in stage_logs)
        errors = [s["error"] for s in stage_logs if s.get("error")]

        entry: Dict[str, Any] = {
            "source_id": source_id,
            "processed_at": datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ"),
            "provider": self.provider,
            "model": self.model,
            "success": success,
            "total_prompt_tokens": total_prompt,
            "total_completion_tokens": total_completion,
            "total_cost": round(total_cost, 6),
            "total_duration_seconds": round(total_duration, 1),
            "total_retries": total_retries,
            "stages": stage_logs,
        }
        if errors:
            entry["errors"] = errors

        # Remove previous entry for the same source_id (re-run)
        existing = [e for e in existing if e.get("source_id") != source_id]
        existing.append(entry)

        log_file.write_text(
            yaml.safe_dump(existing, default_flow_style=False, sort_keys=False),
            encoding="utf-8",
        )

    # ── LLM Execution ─────────────────────────────────────────────────

    def _call_llm(
        self, prompt: str, stage_name: str, max_tokens: int = _DEFAULT_MAX_TOKENS
    ) -> Tuple[Optional[str], Dict[str, Any]]:
        """Call the LLM adapter with exponential back-off on rate limits.

        Returns:
            ``(content, metadata)`` where *content* is the response string
            (or ``None`` on failure) and *metadata* has provider, model,
            token counts, cost, finish_reason, duration, and error info.
        """
        from markitect.prompts.execution.models import RunConfig
        from markitect.llm.exceptions import LLMRateLimitError

        meta: Dict[str, Any] = {
            "provider": self.provider,
            "model": self.model,
            "prompt_tokens": 0,
            "completion_tokens": 0,
            "cost": 0.0,
            "finish_reason": None,
            "duration_seconds": 0.0,
            "error": None,
        }

        model_label = f"{self.provider}/{self.model}" if self.provider else stage_name
        print(f"    Calling LLM ({model_label})...")
        t0 = time.time()
        max_retries = 3
        response = None

        for attempt in range(max_retries + 1):
            try:
                response = self.adapter.execute_prompt(
                    prompt, RunConfig(max_tokens=max_tokens)
                )
                break
            except LLMRateLimitError as exc:
                if attempt < max_retries:
                    wait = 15 * (attempt + 1)
                    print(
                        f"    Rate limited — retrying in {wait}s "
                        f"(attempt {attempt + 1}/{max_retries})..."
                    )
                    time.sleep(wait)
                else:
                    msg = f"Rate limit exceeded after {max_retries} retries: {exc}"
                    print(f"    {msg}")
                    meta["error"] = msg
                    meta["duration_seconds"] = round(time.time() - t0, 1)
                    return None, meta
            except Exception as exc:
                msg = str(exc)
                print(f"    LLM error: {msg}")
                if attempt < max_retries:
                    wait = 5 * (attempt + 1)
                    print(f"    Retrying in {wait}s (attempt {attempt + 1}/{max_retries})...")
                    time.sleep(wait)
                else:
                    meta["error"] = msg
                    meta["duration_seconds"] = round(time.time() - t0, 1)
                    return None, meta

        if response is None:
            meta["error"] = "no response"
            return None, meta

        elapsed = round(time.time() - t0, 1)
        usage = response.usage
        prompt_tok = usage.get("prompt_tokens", 0)
        completion_tok = usage.get("completion_tokens", 0)
        cost = float(usage.get("cost", 0.0))
        finish_reason = getattr(response, "finish_reason", None) or "unknown"

        meta.update({
            "prompt_tokens": prompt_tok,
            "completion_tokens": completion_tok,
            "cost": cost,
            "finish_reason": finish_reason,
            "duration_seconds": elapsed,
        })

        cost_str = f", cost=${cost:.4f}" if cost > 0 else ""
        print(
            f"    Done in {elapsed}s — "
            f"prompt {prompt_tok} tok, completion {completion_tok} tok{cost_str}"
        )

        if finish_reason == "length":
            print(
                f"    WARNING: Output truncated at {max_tokens} tokens "
                f"(finish_reason=length). Consider raising max_tokens for "
                f"stage '{stage_name}' in infospace.yaml."
            )

        content = response.content
        if not content or not content.strip():
            print("    LLM returned empty content.")
            meta["error"] = "empty response"
            return None, meta

        return content, meta

    # ── Git Integration ───────────────────────────────────────────────

    def _git_commit(self, source_id: str) -> None:
        """Stage all output changes and commit them for *source_id*."""
        output_dir = self.root / "output"
        try:
            subprocess.run(
                ["git", "add", str(output_dir)],
                cwd=str(self.root),
                check=True,
                capture_output=True,
            )
            result = subprocess.run(
                [
                    "git", "commit", "-m",
                    f"infospace: process {source_id}\n\n"
                    f"Extract entities, map to VSM, and synthesize analysis.",
                ],
                cwd=str(self.root),
                capture_output=True,
                text=True,
            )
            if result.returncode == 0:
                print(f"  Git commit: process {source_id}")
            else:
                output = result.stdout + result.stderr
                if "nothing to commit" in output or "nothing added" in output:
                    print(f"  Git: nothing to commit for {source_id}")
                else:
                    print(f"  Warning: Git commit failed: {output.strip()}")
        except subprocess.CalledProcessError as e:
            stderr = e.stderr.decode() if isinstance(e.stderr, bytes) else (e.stderr or "")
            print(f"  Warning: Git error: {stderr.strip()}")