"""File-backed observation memory.

Observations are small markdown files under `/memories/observations/`. Each
file has stable frontmatter for future indexing plus a short body that agents
can grep and read with ordinary file tools today.
"""

from __future__ import annotations

import hashlib
import json
import os
import tempfile
import threading
from collections import OrderedDict
from dataclasses import replace
from datetime import UTC, date, datetime
from pathlib import Path

import yaml
from filelock import FileLock
from pydantic import BaseModel, ConfigDict, Field, ValidationError, field_validator

from ..search import (
    search_documents,
)
from ..types import (
    MemoryScope,
    MemorySourceType,
    MemoryType,
    ObservationReadResult,
    ObservationRecordResult,
    ObservationRelation,
    ObservationSearchDocument,
    ObservationSearchHit,
    ObservationSearchMode,
    RelatedObservationResult,
)

OBSERVATION_DIR = "/observations"


ObservationFrontmatterValue = str | dict[str, str] | list[dict[str, str]]
ObservationFrontmatterPayload = dict[str, ObservationFrontmatterValue]


class RelatedObservationEntry(BaseModel):
    model_config = ConfigDict(extra="ignore")

    id: str = Field(min_length=1, strict=True)
    relation: ObservationRelation
    reason: str = Field(min_length=1, strict=True)
    linked_at: str = Field(min_length=1, strict=True)

    @field_validator("id", "reason", "linked_at")
    @classmethod
    def _non_blank(cls, value: str) -> str:
        if not value.strip():
            raise ValueError("must not be blank")
        return value

    def to_frontmatter_dict(self) -> dict[str, str]:
        return {
            "id": self.id,
            "relation": self.relation.value,
            "reason": self.reason,
            "linked_at": self.linked_at,
        }


class ObservationSourceFrontmatter(BaseModel):
    model_config = ConfigDict(extra="ignore")

    type: MemorySourceType
    agent: str = Field(min_length=1, strict=True)
    session_id: str | None = Field(default=None, min_length=1, strict=True)

    @field_validator("agent", "session_id")
    @classmethod
    def _non_blank(cls, value: str | None) -> str | None:
        if value is not None and not value.strip():
            raise ValueError("must not be blank")
        return value

    def to_frontmatter_dict(self) -> dict[str, str]:
        payload = {
            "type": self.type.value,
            "agent": self.agent,
        }
        if self.session_id is not None:
            payload["session_id"] = self.session_id
        return payload


class ObservationFrontmatter(BaseModel):
    model_config = ConfigDict(extra="ignore", validate_assignment=True)

    id: str = Field(min_length=1, strict=True)
    created_at: str | None = Field(default=None, min_length=1, strict=True)
    summary: str = Field(min_length=1, strict=True)
    memory_type: MemoryType
    scope: MemoryScope
    project_id: str | None = Field(default=None, min_length=1, strict=True)
    source: ObservationSourceFrontmatter | None = None
    related_observations: list[RelatedObservationEntry] = Field(default_factory=list)

    @field_validator("id", "summary", "created_at", "project_id")
    @classmethod
    def _non_blank(cls, value: str | None) -> str | None:
        if value is not None and not value.strip():
            raise ValueError("must not be blank")
        return value

    @field_validator("created_at", mode="before")
    @classmethod
    def _coerce_yaml_timestamp(cls, value: object) -> object:
        if isinstance(value, datetime):
            if value.tzinfo is not None:
                value = value.astimezone(UTC)
                return value.strftime("%Y-%m-%dT%H:%M:%SZ")
            return value.strftime("%Y-%m-%dT%H:%M:%S")
        if isinstance(value, date):
            return value.isoformat()
        return value

    def to_frontmatter_dict(self) -> ObservationFrontmatterPayload:
        payload: ObservationFrontmatterPayload = {
            "id": self.id,
        }
        if self.created_at is not None:
            payload["created_at"] = self.created_at
        payload["summary"] = self.summary
        payload["memory_type"] = self.memory_type.value
        payload["scope"] = self.scope.value
        if self.project_id is not None:
            payload["project_id"] = self.project_id
        if self.source is not None:
            payload["source"] = self.source.to_frontmatter_dict()
        if self.related_observations:
            payload["related_observations"] = [
                entry.to_frontmatter_dict() for entry in self.related_observations
            ]
        return payload


def _normalize(text: str) -> str:
    """Collapse whitespace before deriving the dedupe id."""
    return " ".join(text.strip().split())


def _observation_id(
    *,
    memory_type: MemoryType,
    scope: MemoryScope,
    observation: str,
    why_it_matters: str,
) -> str:
    """Return a deterministic id for semantically identical observations."""
    key = "\n".join(
        [
            memory_type.value,
            scope.value,
            _normalize(observation).casefold(),
            _normalize(why_it_matters).casefold(),
        ]
    )
    digest = hashlib.sha256(key.encode("utf-8")).hexdigest()[:16]
    return f"O-{digest}"


def _agent_path(memory_path: str) -> str:
    """Translate a memory-relative path to the virtual path agents see."""
    return f"/memories{memory_path}"


def _memory_path(
    *,
    observation_id: str,
    scope: MemoryScope,
    project_id: str,
) -> str:
    """Return the memory-relative path for an observation id."""
    if scope == MemoryScope.PROJECT:
        return f"{OBSERVATION_DIR}/projects/{project_id}/{observation_id}.md"
    return f"{OBSERVATION_DIR}/global/{observation_id}.md"


def _json_string(value: str) -> str:
    """Render a string as a YAML-safe JSON scalar."""
    return json.dumps(value, ensure_ascii=False)


def _read_observation_document_with_text(
    path: str | Path,
) -> tuple[ObservationFrontmatter, str, str] | None:
    """Read an observation markdown document, body, and original text."""
    document_path = Path(path).expanduser()
    try:
        text = document_path.read_text(encoding="utf-8")
    except (OSError, UnicodeDecodeError):
        return None
    if not text.startswith("---\n"):
        return None
    try:
        frontmatter, body = text.removeprefix("---\n").split("\n---\n", 1)
        metadata = ObservationFrontmatter.model_validate(yaml.safe_load(frontmatter))
    except (ValueError, ValidationError, yaml.YAMLError):
        return None
    return metadata, body, text


def read_observation_document(
    path: str | Path,
) -> tuple[ObservationFrontmatter, str] | None:
    """Read an observation markdown document and parse its frontmatter."""
    document = _read_observation_document_with_text(path)
    if document is None:
        return None
    metadata, body, _text = document
    return metadata, body


def write_observation_document(
    path: str | Path,
    *,
    metadata: ObservationFrontmatter,
    body: str,
) -> None:
    """Write an observation markdown document with frontmatter."""
    frontmatter = yaml.safe_dump(
        metadata.to_frontmatter_dict(),
        allow_unicode=True,
        sort_keys=False,
    )
    _atomic_write_text(Path(path), f"---\n{frontmatter}---\n{body}")


def _atomic_write_text(path: Path, content: str) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    fd, temporary = tempfile.mkstemp(
        prefix=f".{path.name}.", suffix=".tmp", dir=path.parent
    )
    try:
        with os.fdopen(fd, "w", encoding="utf-8") as handle:
            handle.write(content)
            handle.flush()
            os.fsync(handle.fileno())
        os.replace(temporary, path)
    finally:
        try:
            os.unlink(temporary)
        except FileNotFoundError:
            pass


def read_observation_id_from_path(path: str | Path) -> str | None:
    """Read an observation id from a concrete markdown file path."""
    document = read_observation_document(path)
    if document is None:
        return None
    metadata, _body = document
    return metadata.id.strip()


def related_observation_entries(
    metadata: ObservationFrontmatter,
) -> list[RelatedObservationEntry]:
    """Return related-observation frontmatter entries."""
    return list(metadata.related_observations)


def _observation_files(
    *,
    memory_dir: str | Path,
    project_id: str,
    scope: MemoryScope | None,
) -> list[Path]:
    """Return candidate observation files for the current project context."""
    root = Path(memory_dir).expanduser()
    paths: list[Path] = []
    if scope in {None, MemoryScope.GLOBAL}:
        paths.extend(_global_files(root))
    if scope in {None, MemoryScope.PROJECT}:
        paths.extend(_project_files(root, project_id))
    return paths


def _all_observation_files(root: Path) -> list[Path]:
    observation_root = root / OBSERVATION_DIR.lstrip("/")
    try:
        return sorted(path for path in observation_root.rglob("*.md") if path.is_file())
    except OSError:
        return []


def _resolve_related_observations(
    entries: list[RelatedObservationEntry],
    *,
    documents_by_id: dict[str, ObservationSearchDocument],
) -> tuple[RelatedObservationResult, ...]:
    related_observations: list[RelatedObservationResult] = []
    for entry in entries:
        related_id = entry.id
        if related_id not in documents_by_id:
            continue
        target = documents_by_id[related_id]
        related: RelatedObservationResult = {
            "observation_id": target.observation_id,
            "path": target.path,
            "memory_type": target.memory_type,
            "scope": target.scope,
            "summary": target.summary,
            "relation": entry.relation,
            "reason": entry.reason,
        }
        related_observations.append(related)
    return tuple(related_observations)


def _parse_observation_search_document(
    *,
    root: Path,
    path: Path,
) -> tuple[ObservationSearchDocument, list[RelatedObservationEntry]] | None:
    document = _read_observation_document_with_text(path)
    if document is None:
        return None
    metadata, body, text = document
    try:
        memory_path = "/" + path.relative_to(root).as_posix()
    except ValueError:
        return None

    return (
        ObservationSearchDocument(
            observation_id=metadata.id,
            path=_agent_path(memory_path),
            memory_type=metadata.memory_type,
            scope=metadata.scope,
            summary=metadata.summary,
            body=body,
            text=text,
        ),
        related_observation_entries(metadata),
    )


def _resolve_document_links(
    parsed: list[tuple[ObservationSearchDocument, list[RelatedObservationEntry]]],
    *,
    root: Path,
    touched: set[str],
) -> list[ObservationSearchDocument]:
    documents_by_id = {document.observation_id: document for document, _ in parsed}
    missing_related_ids = {
        entry.id
        for _document, entries in parsed
        for entry in entries
        if entry.id not in documents_by_id
    }
    if missing_related_ids:
        for path in _all_observation_files(root):
            if not missing_related_ids:
                break
            parsed_document = _parse_with_cache(root, path, touched)
            if parsed_document is None:
                continue
            document, _entries = parsed_document
            if document.observation_id not in missing_related_ids:
                continue
            documents_by_id[document.observation_id] = document
            missing_related_ids.remove(document.observation_id)

    return [
        replace(
            document,
            related_observations=_resolve_related_observations(
                entries,
                documents_by_id=documents_by_id,
            ),
        )
        for document, entries in parsed
    ]


# ── Parsed-document cache ─────────────────────────────────────────────
#
# One layer: a per-file parse cache keyed on the observation file path.
# Each value carries the file's ``(st_mtime_ns, st_size)`` signature, so a
# rewrite invalidates exactly that entry and replaces it in place.  Scope
# filtering is just which directories get globbed, and link resolution
# reruns on every call over the memoized parses — its fallback walk parses
# through the same cache, so fallback visits are dict hits instead of
# whole-tree reparses.
#
# Deletion needs no invalidation: a deleted file is never globbed, so its
# cached entry can never be served again; the LRU bounds the leftover
# memory.  Eviction runs only at the end of a call, down to
# ``max(cap, entries touched by the call)``, so a call never evicts its own
# working set and a store larger than the cap temporarily exceeds it
# instead of thrashing.
#
# The cap is ``config.memory_observation_cache_max_files`` (default 2048,
# env var ``EVOSCIENTIST_MAX_CACHED_FILES``), read lazily from config on
# first call.  With the working-set rule it bounds retained memory for
# inactive workspaces rather than correctness.
#
_FileParseValue = tuple[
    tuple[int, int],
    tuple[ObservationSearchDocument, list[RelatedObservationEntry]],
]

_file_parse_cache: OrderedDict[str, _FileParseValue] = OrderedDict()

# Serializes cache transactions (lookup+recency, insert+recency, trim) so
# concurrent calls cannot evict a key between another call's lookup and its
# recency update. Parsing stays outside the lock; only dict mutations hold it.
_cache_lock = threading.Lock()

_cached_max_files: int | None = None


def _max_cached_files() -> int:
    """Return the configured cache cap.

    Read once from ``get_effective_config()`` on first call and cached at
    module level; a runtime config change requires a process restart to take
    effect.
    """
    global _cached_max_files
    if _cached_max_files is None:
        from ...config import get_effective_config

        _cached_max_files = get_effective_config().memory_observation_cache_max_files
    return _cached_max_files


def _parse_with_cache(
    root: Path,
    path: Path,
    touched: set[str],
) -> tuple[ObservationSearchDocument, list[RelatedObservationEntry]] | None:
    """Return the parsed document for *path*, memoized per file.

    The cached entry is validated against the file's current
    ``(st_mtime_ns, st_size)`` signature, so a rewrite replaces it in
    place.  Parse failures are not cached; the file is retried on the next
    call.  No copy on read or write: callers only iterate the documents or
    build new ones via ``replace``.
    """
    key = str(path)
    try:
        st = path.stat()
    except OSError:
        return None
    signature = (st.st_mtime_ns, st.st_size)
    with _cache_lock:
        cached = _file_parse_cache.get(key)
        if cached is not None and cached[0] == signature:
            _file_parse_cache.move_to_end(key)
            touched.add(key)
            return cached[1]
    parsed_document = _parse_observation_search_document(root=root, path=path)
    if parsed_document is None:
        return None
    with _cache_lock:
        _file_parse_cache[key] = (signature, parsed_document)
        _file_parse_cache.move_to_end(key)
        touched.add(key)
    return parsed_document


def _trim_parse_cache(touched: set[str]) -> None:
    """Trim the cache at the end of a call down to ``max(cap, len(touched))``.

    Every entry touched by the call was moved or appended to the
    most-recent end, so trimming from the least-recent end down to at least
    ``len(touched)`` entries never evicts the call's own working set.
    """
    target = max(_max_cached_files(), len(touched))
    with _cache_lock:
        while len(_file_parse_cache) > target:
            _file_parse_cache.popitem(last=False)


def _global_files(root: Path) -> list[Path]:
    """Glob the global observation directory."""
    directory = root / OBSERVATION_DIR.lstrip("/") / "global"
    try:
        return sorted(directory.glob("*.md"))
    except OSError:
        return []


def _project_files(root: Path, project_id: str) -> list[Path]:
    """Glob a project's observation directory."""
    directory = root / OBSERVATION_DIR.lstrip("/") / "projects" / project_id
    try:
        return sorted(directory.glob("*.md"))
    except OSError:
        return []


def list_observation_documents(
    *,
    memory_dir: str | Path,
    project_id: str,
    scope: MemoryScope | None = None,
    memory_type: MemoryType | None = None,
) -> list[ObservationSearchDocument]:
    """Read candidate observations for the current filters.

    Observation files are parsed once per file and shared across every
    project_id and scope; each cached entry is validated against the file's
    current ``(st_mtime_ns, st_size)`` signature.  A deleted file simply
    drops out of the glob.  Link resolution reruns on each call over the
    memoized parses.
    """
    root = Path(memory_dir).expanduser()
    paths = _observation_files(memory_dir=root, project_id=project_id, scope=scope)

    touched: set[str] = set()
    parsed: list[tuple[ObservationSearchDocument, list[RelatedObservationEntry]]] = []
    for path in paths:
        parsed_document = _parse_with_cache(root, path, touched)
        if parsed_document is not None:
            parsed.append(parsed_document)

    documents = _resolve_document_links(parsed, root=root, touched=touched)
    _trim_parse_cache(touched)

    if memory_type is not None:
        return [
            document for document in documents if document.memory_type == memory_type
        ]
    return list(documents)


def search_observation_files(
    *,
    memory_dir: str | Path,
    project_id: str,
    query: str,
    scope: MemoryScope | None = None,
    memory_type: MemoryType | None = None,
    limit: int = 8,
    mode: ObservationSearchMode = ObservationSearchMode.RANKED,
) -> list[ObservationSearchHit]:
    """Search global/current-project observations by ranked relevance by default."""
    query_text = query.strip()
    if not query_text:
        return []
    search_mode = ObservationSearchMode(mode)

    documents = list_observation_documents(
        memory_dir=memory_dir,
        project_id=project_id,
        scope=scope,
        memory_type=memory_type,
    )
    return search_documents(
        documents=documents,
        query=query_text,
        limit=limit,
        mode=search_mode,
    )


def read_observation_file(
    *,
    memory_dir: str | Path,
    project_id: str,
    observation_id: str,
) -> ObservationReadResult | None:
    """Read a full observation document by frontmatter id."""
    requested_id = observation_id.strip()
    if not requested_id:
        return None

    root = Path(memory_dir).expanduser()
    for document in list_observation_documents(
        memory_dir=root,
        project_id=project_id,
        scope=None,
    ):
        if document.observation_id != requested_id:
            continue
        result: ObservationReadResult = {
            "observation_id": document.observation_id,
            "path": document.path,
            "memory_type": document.memory_type,
            "scope": document.scope,
            "summary": document.summary,
            "text": document.text,
        }
        if document.related_observations:
            result["related_observations"] = list(document.related_observations)
        return result
    return None


def observation_document_by_id(
    *,
    memory_dir: str | Path,
    project_id: str,
    observation_id: str,
) -> tuple[Path, ObservationFrontmatter, str] | None:
    """Return the stored document tuple for one observation id."""
    requested_id = observation_id.strip()
    if not requested_id:
        return None

    root = Path(memory_dir).expanduser()
    for path in _observation_files(
        memory_dir=root,
        project_id=project_id,
        scope=None,
    ):
        document = read_observation_document(path)
        if document is None:
            continue
        metadata, body = document
        if metadata.id == requested_id:
            return path, metadata, body
    return None


def _format_frontmatter(
    *,
    observation_id: str,
    created_at: str,
    memory_type: MemoryType,
    summary: str,
    scope: MemoryScope,
    source_type: MemorySourceType,
    source_agent: str,
    source_session_id: str,
    project_id: str,
) -> str:
    """Build the frontmatter block for an observation file."""
    lines = [
        "---",
        f"id: {_json_string(observation_id)}",
        f"created_at: {_json_string(created_at)}",
        f"summary: {_json_string(summary)}",
        f"memory_type: {memory_type.value}",
        f"scope: {scope.value}",
    ]
    if scope == MemoryScope.PROJECT:
        lines.append(f"project_id: {_json_string(project_id)}")
    lines.extend(
        [
            "source:",
            f"  type: {source_type.value}",
            f"  agent: {_json_string(source_agent)}",
        ]
    )
    lines.append(f"  session_id: {_json_string(source_session_id.strip())}")
    lines.append("---")
    return "\n".join(lines)


def _format_observation_markdown(
    *,
    observation_id: str,
    created_at: str,
    memory_type: MemoryType,
    summary: str,
    observation: str,
    why_it_matters: str,
    evidence: str | None,
    scope: MemoryScope,
    source_type: MemorySourceType,
    source_agent: str,
    source_session_id: str,
    project_id: str,
) -> str:
    """Render a complete observation markdown document."""
    frontmatter = _format_frontmatter(
        observation_id=observation_id,
        created_at=created_at,
        memory_type=memory_type,
        summary=summary,
        scope=scope,
        source_type=source_type,
        source_agent=source_agent,
        source_session_id=source_session_id,
        project_id=project_id,
    )
    body = (
        f"{frontmatter}\n\n"
        "## Observation\n\n"
        f"{observation.strip()}\n\n"
        "## Why It Matters\n\n"
        f"{why_it_matters.strip()}\n"
    )
    if evidence and evidence.strip():
        body += f"\n## Evidence\n\n{evidence.strip()}\n"
    return body


def record_observation_file(
    *,
    memory_dir: str | Path,
    project_id: str,
    memory_type: MemoryType,
    summary: str,
    observation: str,
    why_it_matters: str,
    scope: MemoryScope,
    source_type: MemorySourceType,
    source_session_id: str,
    source_agent: str,
    evidence: str | None = None,
) -> ObservationRecordResult:
    """Create an observation markdown file unless an equivalent one exists.

    The id is derived from the normalized observation text, rationale, type, and
    scope, so repeated attempts to save the same observation return the existing
    path instead of creating duplicates.
    """

    summary_text = summary.strip()
    observation_text = observation.strip()
    why_text = why_it_matters.strip()
    if not summary_text:
        raise ValueError("summary must not be empty")
    if not observation_text:
        raise ValueError("observation must not be empty")
    if not why_text:
        raise ValueError("why_it_matters must not be empty")
    if not source_session_id.strip():
        raise ValueError("source_session_id must not be empty")

    observation_id = _observation_id(
        memory_type=memory_type,
        scope=scope,
        observation=observation_text,
        why_it_matters=why_text,
    )
    memory_path = _memory_path(
        observation_id=observation_id,
        scope=scope,
        project_id=project_id,
    )
    path = Path(memory_dir).expanduser() / memory_path.lstrip("/")
    created = False
    memory_root = Path(memory_dir).expanduser()
    with FileLock(str(memory_root / ".observation-write.lock"), timeout=30):
        if not path.exists():
            created_at = datetime.now(UTC).strftime("%Y-%m-%dT%H:%M:%SZ")
            content = _format_observation_markdown(
                observation_id=observation_id,
                created_at=created_at,
                memory_type=memory_type,
                summary=summary_text,
                observation=observation_text,
                why_it_matters=why_text,
                evidence=evidence.strip() if evidence else None,
                scope=scope,
                source_type=source_type,
                source_agent=source_agent,
                source_session_id=source_session_id,
                project_id=project_id,
            )
            _atomic_write_text(path, content)
            created = True

    result: ObservationRecordResult = {
        "observation_id": observation_id,
        "path": _agent_path(memory_path),
        "created": created,
        "memory_type": memory_type,
        "scope": scope,
    }
    if scope == MemoryScope.PROJECT:
        result["project_id"] = project_id
    return result