# SPDX-License-Identifier: AGPL-3.0-only
# Copyright (c) 2026 sol pbc

"""Shared utilities and types for AI providers.

This module contains:
- Event TypedDicts emitted by providers during talent execution
- GenerateResult TypedDict returned by run_generate/run_agenerate
- JSONEventCallback for event emission
- Utility functions for common provider operations
"""

from __future__ import annotations

import json
import math
import re
from typing import Any, Callable, Literal, Mapping, Optional, Union

from typing_extensions import Required, TypedDict

from solstone.think.utils import now_ms

# ---------------------------------------------------------------------------
# Event Types
# ---------------------------------------------------------------------------


class ToolStartEvent(TypedDict, total=False):
    """Event emitted when a tool starts."""

    event: Literal["tool_start"]
    ts: int
    tool: str
    args: Optional[dict[str, Any]]
    call_id: Optional[str]  # Unique ID to pair with tool_end event
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


class ToolEndEvent(TypedDict, total=False):
    """Event emitted when a tool finishes."""

    event: Literal["tool_end"]
    ts: int
    tool: str
    args: Optional[dict[str, Any]]
    result: Any
    call_id: Optional[str]  # Matches the call_id from tool_start
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


class StartEvent(TypedDict, total=False):
    """Event emitted when a talent run begins."""

    event: Required[Literal["start"]]
    ts: Required[int]
    prompt: Required[str]
    name: Required[str]
    model: Required[str]
    provider: Required[str]
    session_id: Optional[str]  # solstone-owned session ID for continuation
    chat_id: Optional[str]  # Chat ID for reverse lookup
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


class FinishEvent(TypedDict, total=False):
    """Event emitted when a talent run finishes successfully."""

    event: Required[Literal["finish"]]
    ts: Required[int]
    result: Required[str]
    usage: Optional[dict[str, Any]]
    cli_session_id: Optional[
        str
    ]  # solstone-owned session ID persisted under journal/.cache/cogitate-history/
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


class ErrorEvent(TypedDict, total=False):
    """Event emitted when an error occurs."""

    event: Literal["error"]
    ts: int
    error: str
    reason: Optional[str]
    reason_code: Optional[str]
    provider: Optional[str]
    trace: Optional[str]
    reset_at_ms: Optional[int]
    terminal: Optional[bool]
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


class TalentUpdatedEvent(TypedDict, total=False):
    """Event emitted when the talent context changes."""

    event: Required[Literal["talent_updated"]]
    ts: Required[int]
    talent: Required[str]
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


class ThinkingEvent(TypedDict, total=False):
    """Event emitted when thinking/reasoning summaries are available.

    For Anthropic models, may include a signature for verification when
    passing thinking blocks back during tool use continuations.
    For redacted thinking, summary will contain "[redacted]" and
    redacted_data will contain the encrypted content.
    """

    event: Required[Literal["thinking"]]
    ts: Required[int]
    summary: Required[str]
    model: Optional[str]
    signature: Optional[str]  # Anthropic thinking block signature
    redacted_data: Optional[str]  # Encrypted data for redacted thinking
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


class TextDeltaEvent(TypedDict, total=False):
    """Event emitted when streamed text content is available."""

    event: Required[Literal["text_delta"]]
    ts: Required[int]
    delta: Required[str]
    model: Optional[str]
    raw: Optional[list[dict[str, Any]]]  # Original provider JSON event(s)


Event = Union[
    ToolStartEvent,
    ToolEndEvent,
    StartEvent,
    FinishEvent,
    ErrorEvent,
    ThinkingEvent,
    TextDeltaEvent,
    TalentUpdatedEvent,
]


# ---------------------------------------------------------------------------
# Provider Error Classification
# ---------------------------------------------------------------------------

_CLI_UNAVAILABLE_PATTERNS = ("not installed", "command not found", "missing")
_CLI_TIMEOUT_PATTERNS = ("timed out", "timeout")
_CLI_AUTH_PATTERNS = (
    "authentication",
    "unauthorized",
    " 401",
    " 403",
    "401 ",
    "403 ",
    "401:",
    "403:",
    "permission denied",
    "forbidden",
    "invalid api key",
)
_CONTEXT_WINDOW_PATTERNS = (
    "exceeds the available context size",
    "context size has been exceeded",
    "exceeds the context window",
    "maximum context length",
    "context length exceeded",
)


def _status_code(exc: BaseException) -> int | None:
    for attr in ("status_code", "_status_code", "code"):
        value = getattr(exc, attr, None)
        if isinstance(value, int):
            return value
    response = getattr(exc, "response", None)
    value = getattr(response, "status_code", None)
    return value if isinstance(value, int) else None


def _contains_any(text: str, patterns: tuple[str, ...]) -> bool:
    return any(pattern in text for pattern in patterns)


def _module_matches(module_name: str, package: str) -> bool:
    return module_name == package or module_name.startswith(f"{package}.")


def _exception_name_matches(
    exc_name: str, exc_qualname: str, names: tuple[str, ...]
) -> bool:
    return exc_name in names or any(exc_qualname.endswith(f".{name}") for name in names)


RUNTIME_REASON_CODES = frozenset(
    {
        "context_window_exceeded",
        "context_budget_exceeded",
        "local_capacity_exhausted",
        "provider_quota_exceeded",
        "provider_key_invalid",
        "chat_timeout",
        "local_queue_timeout",
        "max_turns_exhausted",
        "network_unreachable",
        "provider_unavailable",
        "provider_response_invalid",
        "incomplete_json_length",
        "incomplete_text_length",
        "unknown",
    }
)


def classify_provider_error(exc: BaseException, provider: str) -> str:
    """Return a chat reason code for a provider exception."""
    try:
        exc_name = type(exc).__name__
        exc_qualname = type(exc).__qualname__
        exc_module = type(exc).__module__
        exc_name_lower = exc_name.lower()
        exc_identity_lower = f"{exc_module}.{exc_qualname}".lower()
        message_lower = str(exc).lower()
        explicit_reason_code = getattr(exc, "reason_code", None)
        if isinstance(explicit_reason_code, str) and explicit_reason_code:
            return explicit_reason_code

        if exc_name == "QuotaExhaustedError":
            return "provider_quota_exceeded"
        if exc_name == "ContextWindowExceededError":
            return "context_window_exceeded"
        if exc_name in {"LLMContextWindowExceedError", "LLMContextWindowTooSmallError"}:
            return "context_window_exceeded"
        if exc_name == "LLMAuthenticationError":
            return "provider_key_invalid"
        if exc_name == "LLMRateLimitError":
            return "provider_quota_exceeded"
        if exc_name == "LLMTimeoutError":
            return "chat_timeout"
        if exc_name == "LLMServiceUnavailableError":
            return "provider_unavailable"
        if exc_name in {"LLMNoResponseError", "LLMResponseError"}:
            return "provider_response_invalid"
        if _contains_any(message_lower, _CONTEXT_WINDOW_PATTERNS):
            return "context_window_exceeded"
        # MaxIterationsReached is OpenHands' event-code spelling of the same limit;
        # our MaxTurnsExhausted (cogitate_policy) is what actually reaches here.
        if exc_name in ("MaxTurnsExhausted", "MaxIterationsReached"):
            return "max_turns_exhausted"

        if isinstance(exc, ValueError) and "no response from model" in message_lower:
            return "provider_response_invalid"

        is_anthropic = _module_matches(exc_module, "anthropic")
        is_openai = _module_matches(exc_module, "openai")
        is_httpx = _module_matches(exc_module, "httpx")
        status_code = _status_code(exc)

        if (is_anthropic or is_openai) and _exception_name_matches(
            exc_name,
            exc_qualname,
            ("AuthenticationError", "PermissionDeniedError"),
        ):
            return "provider_key_invalid"

        if (is_anthropic or is_openai) and _exception_name_matches(
            exc_name, exc_qualname, ("RateLimitError",)
        ):
            return "provider_quota_exceeded"

        if (is_anthropic or is_openai) and _exception_name_matches(
            exc_name, exc_qualname, ("APITimeoutError",)
        ):
            return "chat_timeout"
        if is_httpx and (
            "timeout" in exc_name_lower
            or _exception_name_matches(
                exc_name,
                exc_qualname,
                (
                    "TimeoutException",
                    "ConnectTimeout",
                    "PoolTimeout",
                    "ReadTimeout",
                    "WriteTimeout",
                ),
            )
        ):
            return "chat_timeout"

        if (is_anthropic or is_openai) and _exception_name_matches(
            exc_name, exc_qualname, ("APIConnectionError",)
        ):
            return "network_unreachable"
        if is_httpx and (
            _exception_name_matches(
                exc_name,
                exc_qualname,
                ("NetworkError", "RequestError", "ConnectError"),
            )
            or "connection" in exc_name_lower
            or "connect" in exc_name_lower
        ):
            return "network_unreachable"
        if isinstance(exc, ConnectionError):
            return "network_unreachable"

        if is_openai and _exception_name_matches(
            exc_name, exc_qualname, ("InternalServerError",)
        ):
            return "provider_unavailable"
        if (
            (
                (is_anthropic or is_openai)
                and _exception_name_matches(exc_name, exc_qualname, ("APIStatusError",))
            )
            or (
                is_httpx
                and _exception_name_matches(
                    exc_name, exc_qualname, ("HTTPStatusError",)
                )
            )
        ) and (status_code or 0) >= 500:
            return "provider_unavailable"

        if isinstance(exc, RuntimeError):
            if _contains_any(message_lower, _CLI_UNAVAILABLE_PATTERNS):
                return "provider_unavailable"
            if _contains_any(message_lower, _CLI_TIMEOUT_PATTERNS):
                return "chat_timeout"
            if _contains_any(message_lower, _CLI_AUTH_PATTERNS):
                return "provider_key_invalid"
            return "unknown"

        if (
            "authenticationerror" in exc_name_lower
            or "permissiondeniederror" in exc_name_lower
            or "unauthorized" in exc_name_lower
            or "forbidden" in exc_name_lower
        ):
            return "provider_key_invalid"
        if (
            "ratelimit" in exc_name_lower
            or "toomanyrequests" in exc_name_lower
            or "resourceexhausted" in exc_name_lower
        ):
            return "provider_quota_exceeded"
        if "timeout" in exc_name_lower:
            return "chat_timeout"
        if "connection" in exc_name_lower or "network" in exc_name_lower:
            return "network_unreachable"
        if (
            "responsevalidation" in exc_name_lower
            or "unknownapiresponse" in exc_identity_lower
        ):
            return "provider_response_invalid"
        if "internalservererror" in exc_name_lower or "servererror" in exc_name_lower:
            return "provider_unavailable"

        return "unknown"
    except Exception:
        return "unknown"


# ---------------------------------------------------------------------------
# Usage Schema
# ---------------------------------------------------------------------------

# Canonical keys for the normalized usage dict returned by all providers.
# log_token_usage() passes through exactly these keys (when present and non-zero).
USAGE_KEYS = frozenset(
    {
        "input_tokens",
        "output_tokens",
        "total_tokens",
        "cached_tokens",
        "reasoning_tokens",
        "cache_creation_tokens",
        "requests",
    }
)

# ---------------------------------------------------------------------------
# GenerateResult
# ---------------------------------------------------------------------------

CANNED_GENERATE_PROMPT = "Reply with the single word OK."
CANNED_COGITATE_PROBE_PROMPT = (
    "Call the emit_final tool exactly once with the content OK. "
    "Do not reply with plain text and do not call any other tool."
)
CANNED_GENERATE_MAX_OUTPUT_TOKENS = 512
CANNED_GENERATE_THINKING_BUDGET = 0
CANNED_GENERATE_NUM_RETRIES = 0
CANNED_GENERATE_TIMEOUT_S = 30
CannedGenerateVerdict = Literal["pass", "starved", "invalid"]
_MAX_SAFE_TOKEN_COUNT = 1_000_000_000_000
_MAX_SAFE_FINISH_REASON_LENGTH = 64
_UNKNOWN_FINISH_REASON = "unknown"


class GenerateResult(TypedDict, total=False):
    """Result from provider run_generate/run_agenerate functions.

    Structured result that allows the wrapper to handle cross-cutting concerns
    like token logging and JSON validation centrally.

    The thinking field contains dicts with: summary (str), signature (optional str),
    redacted_data (optional str for Anthropic redacted thinking).
    """

    text: Required[str]  # Response text
    model: Optional[str]  # Resolved model identifier when provider returns one
    usage: Optional[dict]  # Normalized usage dict (input_tokens, output_tokens, etc.)
    finish_reason: Optional[str]  # Normalized: "stop", "max_tokens", "safety", etc.
    thinking: Optional[list]  # List of thinking block dicts
    schema_validation: Optional[dict]  # Validation result when json_schema is supplied
    input_budget: Optional[
        dict
    ]  # Out-of-band truncation metadata when the bundled-local input was clipped
    inference: Optional[dict]  # Content-free local inference timing/admission record


def _has_reasoning_usage(result: GenerateResult) -> bool:
    thinking = result.get("thinking")
    if isinstance(thinking, list) and bool(thinking):
        return True
    usage = result.get("usage")
    if not isinstance(usage, dict):
        return False
    for key, value in usage.items():
        if "reasoning" not in str(key):
            continue
        if isinstance(value, bool):
            continue
        if isinstance(value, (int, float)) and value > 0:
            return True
    return False


def _is_blank_visible_output(result: GenerateResult) -> bool:
    text = result.get("text")
    return not isinstance(text, str) or not text.strip()


def _coerce_token_count(value: Any) -> int | None:
    if isinstance(value, bool):
        return None
    if not isinstance(value, (int, float)):
        return None
    if isinstance(value, float) and not math.isfinite(value):
        return None
    count = int(value)
    if count < 0:
        return None
    return min(count, _MAX_SAFE_TOKEN_COUNT)


def _safe_token_counts(usage: Any) -> dict[str, int]:
    if not isinstance(usage, Mapping):
        return {}

    token_counts: dict[str, int] = {}
    aliases = {
        "input_tokens": ("input_tokens", "prompt_tokens"),
        "output_tokens": ("output_tokens", "completion_tokens"),
        "reasoning_tokens": ("reasoning_tokens",),
        "total_tokens": ("total_tokens",),
    }
    for normalized_key, candidate_keys in aliases.items():
        for key in candidate_keys:
            if key not in usage:
                continue
            count = _coerce_token_count(usage.get(key))
            if count is not None:
                token_counts[normalized_key] = count
            break
    return token_counts


_SAFE_FINISH_REASON_PATTERN = re.compile(r"^[a-z0-9_]+$")


def _safe_finish_reason(value: Any) -> str | None:
    """Reduce a provider finish value to a bounded safe token, or None."""
    if not isinstance(value, str):
        return None
    if len(value) > _MAX_SAFE_FINISH_REASON_LENGTH:
        return _UNKNOWN_FINISH_REASON
    normalized = value.strip().lower()
    if _SAFE_FINISH_REASON_PATTERN.fullmatch(normalized):
        return normalized
    return _UNKNOWN_FINISH_REASON


def validate_generate_result_strict(
    result: GenerateResult,
    *,
    json_output: bool,
    model: str | None = None,
) -> None:
    """Validate provider result shape and visible output after usage logging."""
    from solstone.think.models import (
        ProviderResponseInvalidError,
        finish_reason_error,
    )

    if not isinstance(result, Mapping):
        raise ProviderResponseInvalidError(
            "malformed_result",
            model=model,
        )

    token_counts = _safe_token_counts(result.get("usage"))
    result_model = result.get("model")
    error_model = (
        result_model if isinstance(result_model, str) and result_model else model
    )
    finish_reason = result.get("finish_reason")
    safe_finish_reason = _safe_finish_reason(finish_reason)

    if finish_reason is not None and not isinstance(finish_reason, str):
        raise ProviderResponseInvalidError(
            "malformed_finish_reason",
            finish_reason=safe_finish_reason,
            model=error_model,
            token_counts=token_counts,
        )

    if json_output:
        error = finish_reason_error(
            {**result, "finish_reason": safe_finish_reason}, json_output=True
        )
        if error is not None:
            raise error

    if safe_finish_reason in {None, "stop"} and _is_blank_visible_output(result):
        raise ProviderResponseInvalidError(
            "blank_visible_output",
            finish_reason=safe_finish_reason,
            model=error_model,
            token_counts=token_counts,
        )


def classify_canned_generate(result: GenerateResult) -> CannedGenerateVerdict:
    """Classify a canned generate result without using brain-state vocabulary.

    Brain refresh maps this later as: pass -> generate ok, starved ->
    probe_output_starved, invalid -> provider_response_invalid.
    """

    finish_reason = result.get("finish_reason")
    if finish_reason == "max_tokens":
        return "starved"

    if not _is_blank_visible_output(result):
        return "pass"

    if _has_reasoning_usage(result) or finish_reason not in {"stop"}:
        return "starved"
    return "invalid"


# ---------------------------------------------------------------------------
# JSONEventCallback
# ---------------------------------------------------------------------------


class JSONEventCallback:
    """Emit JSON events via a callback."""

    def __init__(self, callback: Optional[Callable[[Event], None]] = None) -> None:
        self.callback = callback

    def emit(self, data: Event) -> None:
        if "ts" not in data:
            data = {**data, "ts": now_ms()}
        if self.callback:
            self.callback(data)

    def close(self) -> None:
        pass


# ---------------------------------------------------------------------------
# Raw Event Trimming
# ---------------------------------------------------------------------------

# Structural keys preserved when trimming oversized raw events.
_RAW_STRUCTURAL_KEYS = frozenset(
    {
        "type",
        "id",
        "tool_id",
        "tool_name",
        "role",
        "event_type",
        "timestamp",
    }
)

_RAW_BYTE_LIMIT = 16_384  # 16 KB


def safe_raw(
    events: list[dict[str, Any]],
    limit: int = _RAW_BYTE_LIMIT,
) -> list[dict[str, Any]]:
    """Return *events* as-is if small enough, otherwise a trimmed version.

    When the JSON-serialized size exceeds *limit* bytes, each event is reduced
    to its structural keys and a ``_raw_trimmed`` dict is appended with the
    original byte count and the limit that was applied.
    """
    serialized = json.dumps(events, ensure_ascii=False)
    if len(serialized.encode("utf-8")) <= limit:
        return events

    trimmed = [
        {k: v for k, v in e.items() if k in _RAW_STRUCTURAL_KEYS} for e in events
    ]
    trimmed.append(
        {"_raw_trimmed": {"original_bytes": len(serialized), "limit": limit}}
    )
    return trimmed


__all__ = [
    "CANNED_COGITATE_PROBE_PROMPT",
    "CANNED_GENERATE_MAX_OUTPUT_TOKENS",
    "CANNED_GENERATE_NUM_RETRIES",
    "CANNED_GENERATE_PROMPT",
    "CANNED_GENERATE_THINKING_BUDGET",
    "CANNED_GENERATE_TIMEOUT_S",
    "CannedGenerateVerdict",
    "Event",
    "GenerateResult",
    "JSONEventCallback",
    "RUNTIME_REASON_CODES",
    "ThinkingEvent",
    "USAGE_KEYS",
    "classify_canned_generate",
    "classify_provider_error",
    "safe_raw",
]
