from __future__ import annotations

import base64
import json
import re
import time
from dataclasses import dataclass, field
from typing import Any, Iterable, Iterator

import tiktoken

from services.account_service import account_service
from services.config import config
from services.image_storage_service import image_storage_service
from services.openai_backend_api import ImagePollTimeoutError, OpenAIBackendAPI
from utils.helper import IMAGE_MODELS, extract_image_from_message_content
from utils.log import logger


class ImageGenerationError(Exception):
    def __init__(
        self,
        message: str,
        status_code: int = 502,
        error_type: str = "server_error",
        code: str | None = "upstream_error",
        param: str | None = None,
    ) -> None:
        super().__init__(message)
        self.status_code = status_code
        self.error_type = error_type
        self.code = code
        self.param = param

    def to_openai_error(self) -> dict[str, Any]:
        return {
            "error": {
                "message": str(self),
                "type": self.error_type,
                "param": self.param,
                "code": self.code,
            }
        }


def is_token_invalid_error(message: str) -> bool:
    text = str(message or "").lower()
    return (
        "token_invalidated" in text
        or "token_revoked" in text
        or "authentication token has been invalidated" in text
        or "invalidated oauth token" in text
    )


def image_stream_error_message(message: str) -> str:
    text = str(message or "")
    lower = text.lower()
    if is_token_invalid_error(text):
        return "image generation failed"
    if "curl: (35)" in lower or "tls connect error" in lower or "openssl_internal" in lower:
        return "upstream image connection failed, please retry later"
    return text or "image generation failed"


def encode_images(images: Iterable[tuple[bytes, str, str]]) -> list[str]:
    return [base64.b64encode(data).decode("ascii") for data, _, _ in images if data]


def save_image_bytes(image_data: bytes, base_url: str | None = None) -> str:
    return image_storage_service.save(image_data, base_url).url


def message_text(content: Any) -> str:
    if isinstance(content, str):
        return content
    if isinstance(content, list):
        parts = []
        for item in content:
            if isinstance(item, str):
                parts.append(item)
            elif isinstance(item, dict) and str(item.get("type") or "") in {"text", "input_text", "output_text"}:
                parts.append(str(item.get("text") or ""))
        return "".join(parts)
    return ""


def normalize_messages(messages: object, system: Any = None) -> list[dict[str, Any]]:
    normalized = []
    if config.global_system_prompt:
        normalized.append({"role": "system", "content": config.global_system_prompt})
    system_text = message_text(system)
    if system_text:
        normalized.append({"role": "system", "content": system_text})
    if isinstance(messages, list):
        for message in messages:
            if not isinstance(message, dict):
                continue
            role = message.get("role", "user")
            content = message.get("content", "")
            text = message_text(content)
            images: list[tuple[bytes, str]] = []
            if role == "user":
                images.extend(extract_image_from_message_content(content))
                if isinstance(content, list):
                    for part in content:
                        if not isinstance(part, dict) or part.get("type") != "image":
                            continue
                        data = part.get("data")
                        if isinstance(data, (bytes, bytearray)):
                            images.append((bytes(data), str(part.get("mime") or "image/png")))
            if images:
                parts: list[Any] = []
                if text:
                    parts.append({"type": "text", "text": text})
                for data, mime in images:
                    parts.append({"type": "image", "data": data, "mime": mime})
                normalized.append({"role": role, "content": parts})
            else:
                normalized.append({"role": role, "content": text})
    return normalized


def prompt_with_global_system(prompt: str) -> str:
    return f"{config.global_system_prompt}\n\n{prompt}" if config.global_system_prompt else prompt


def assistant_history_text(messages: list[dict[str, Any]]) -> str:
    return "".join(str(item.get("content") or "") for item in messages if item.get("role") == "assistant")


def assistant_history_messages(messages: list[dict[str, Any]]) -> list[str]:
    return [str(item.get("content") or "") for item in messages if item.get("role") == "assistant" and item.get("content")]


def build_image_prompt(prompt: str, size: str | None) -> str:
    if not size:
        return prompt
    if size not in {"1:1", "16:9", "9:16", "4:3", "3:4"}:
        return f"{prompt.strip()}\n\n输出图片，宽高比为 {size}。"
    hint = {
        "1:1": "输出为 1:1 正方形构图，主体居中，适合正方形画幅。",
        "16:9": "输出为 16:9 横屏构图，适合宽画幅展示。",
        "9:16": "输出为 9:16 竖屏构图，适合竖版画幅展示。",
        "4:3": "输出为 4:3 比例，兼顾宽度与高度，适合展示画面细节。",
        "3:4": "输出为 3:4 比例，纵向构图，适合人物肖像或竖向场景。",
    }[size]
    return f"{prompt.strip()}\n\n{hint}"


def encoding_for_model(model: str):
    try:
        return tiktoken.encoding_for_model(model)
    except KeyError:
        try:
            return tiktoken.get_encoding("o200k_base")
        except KeyError:
            return tiktoken.get_encoding("cl100k_base")


def count_message_tokens(messages: list[dict[str, Any]], model: str) -> int:
    encoding = encoding_for_model(model)
    total = 0
    for message in messages:
        total += 3
        for key, value in message.items():
            if not isinstance(value, str):
                continue
            total += len(encoding.encode(value))
            if key == "name":
                total += 1
    return total + 3


def count_text_tokens(text: str, model: str) -> int:
    return len(encoding_for_model(model).encode(text))


def format_image_result(
    items: list[dict[str, Any]],
    prompt: str,
    response_format: str,
    base_url: str | None = None,
    created: int | None = None,
    message: str = "",
) -> dict[str, Any]:
    data: list[dict[str, Any]] = []
    for item in items:
        b64_json = str(item.get("b64_json") or "").strip()
        if not b64_json:
            continue
        revised_prompt = str(item.get("revised_prompt") or prompt).strip() or prompt
        if response_format == "b64_json":
            data.append({
                "b64_json": b64_json,
                "url": save_image_bytes(base64.b64decode(b64_json), base_url),
                "revised_prompt": revised_prompt,
            })
        else:
            data.append({
                "url": save_image_bytes(base64.b64decode(b64_json), base_url),
                "revised_prompt": revised_prompt,
            })
    result: dict[str, Any] = {"created": created or int(time.time()), "data": data}
    if message and not data:
        result["message"] = message
    return result


@dataclass
class ConversationRequest:
    model: str = "auto"
    prompt: str = ""
    messages: list[dict[str, Any]] | None = None
    images: list[str] | None = None
    n: int = 1
    size: str | None = None
    response_format: str = "b64_json"
    base_url: str | None = None
    message_as_error: bool = False


@dataclass
class ConversationState:
    text: str = ""
    conversation_id: str = ""
    file_ids: list[str] = field(default_factory=list)
    sediment_ids: list[str] = field(default_factory=list)
    blocked: bool = False
    tool_invoked: bool | None = None
    turn_use_case: str = ""


@dataclass
class ImageOutput:
    kind: str
    model: str
    index: int
    total: int
    created: int = field(default_factory=lambda: int(time.time()))
    text: str = ""
    upstream_event_type: str = ""
    data: list[dict[str, Any]] = field(default_factory=list)

    def to_chunk(self) -> dict[str, Any]:
        chunk: dict[str, Any] = {
            "object": "image.generation.chunk",
            "created": self.created,
            "model": self.model,
            "index": self.index,
            "total": self.total,
            "progress_text": self.text,
            "upstream_event_type": self.upstream_event_type,
            "data": [],
        }
        if self.kind == "message":
            chunk.update({
                "object": "image.generation.message",
                "message": self.text,
            })
            chunk.pop("progress_text", None)
            chunk.pop("upstream_event_type", None)
        elif self.kind == "result":
            chunk.update({
                "object": "image.generation.result",
                "data": self.data,
            })
            chunk.pop("progress_text", None)
            chunk.pop("upstream_event_type", None)
        return chunk


def assistant_message_text(message: dict[str, Any]) -> str:
    content = message.get("content") or {}
    parts = content.get("parts") or []
    if not isinstance(parts, list):
        return ""
    return "".join(part for part in parts if isinstance(part, str))


def strip_history(text: str, history_text: str = "") -> str:
    text = str(text or "")
    history_text = str(history_text or "")
    while history_text and text.startswith(history_text):
        text = text[len(history_text):]
    return text


def assistant_text(event: dict[str, Any], current_text: str = "", history_text: str = "") -> str:
    for candidate in (event, event.get("v")):
        if not isinstance(candidate, dict):
            continue
        message = candidate.get("message")
        if not isinstance(message, dict):
            continue
        role = str((message.get("author") or {}).get("role") or "").strip().lower()
        if role != "assistant":
            continue
        text = assistant_message_text(message)
        if text:
            return strip_history(text, history_text)
    return apply_text_patch(event, current_text, history_text)


def event_assistant_text(event: dict[str, Any], history_text: str = "") -> str:
    for candidate in (event, event.get("v")):
        if not isinstance(candidate, dict):
            continue
        message = candidate.get("message")
        if isinstance(message, dict) and (message.get("author") or {}).get("role") == "assistant":
            return strip_history(assistant_message_text(message), history_text)
    return ""


def apply_text_patch(event: dict[str, Any], current_text: str = "", history_text: str = "") -> str:
    if event.get("p") == "/message/content/parts/0":
        return apply_patch_op(event, current_text, history_text)

    operations = event.get("v")
    if isinstance(operations, str) and current_text and not event.get("p") and not event.get("o"):
        return current_text + operations

    if event.get("o") == "patch" and isinstance(operations, list):
        text = current_text
        for item in operations:
            if isinstance(item, dict):
                text = apply_text_patch(item, text, history_text)
        return text

    if not isinstance(operations, list):
        return current_text

    text = current_text
    for item in operations:
        if isinstance(item, dict):
            text = apply_text_patch(item, text, history_text)
    return text


def apply_patch_op(operation: dict[str, Any], current_text: str, history_text: str = "") -> str:
    op = operation.get("o")
    value = str(operation.get("v") or "")
    if op == "append":
        return current_text + value
    if op == "replace":
        return strip_history(value, history_text)
    return current_text


def add_unique(values: list[str], candidates: list[str]) -> None:
    for candidate in candidates:
        if candidate and candidate not in values:
            values.append(candidate)


def extract_conversation_ids(payload: str) -> tuple[str, list[str], list[str]]:
    conversation_match = re.search(r'"conversation_id"\s*:\s*"([^"]+)"', payload)
    conversation_id = conversation_match.group(1) if conversation_match else ""
    # Negative lookahead excludes "file-service" (URI prefix, not a real id).
    file_ids = re.findall(r"(file[-_](?!service\b)[A-Za-z0-9]+)", payload)
    sediment_ids = re.findall(r"sediment://([A-Za-z0-9_-]+)", payload)
    return conversation_id, file_ids, sediment_ids


def is_image_tool_event(event: dict[str, Any]) -> bool:
    value = event.get("v")
    message = event.get("message") or (value.get("message") if isinstance(value, dict) else None)
    if not isinstance(message, dict):
        return False
    metadata = message.get("metadata") or {}
    author = message.get("author") or {}
    content = message.get("content") or {}
    if author.get("role") != "tool":
        return False
    if metadata.get("async_task_type") == "image_gen":
        return True
    if content.get("content_type") != "multimodal_text":
        return False
    return any(
        isinstance(part, dict) and (
                part.get("content_type") == "image_asset_pointer"
                or str(part.get("asset_pointer") or "").startswith(("file-service://", "sediment://"))
        )
        for part in content.get("parts") or []
    )


def update_conversation_state(state: ConversationState, payload: str, event: dict[str, Any] | None = None) -> None:
    conversation_id, file_ids, sediment_ids = extract_conversation_ids(payload)
    if conversation_id and not state.conversation_id:
        state.conversation_id = conversation_id
    # Accept file_id / sediment_id when any of:
    #   1) event is a complete image_gen tool message
    #   2) prior server_ste_metadata already flipped tool_invoked True (in an image_gen turn)
    #   3) patch event whose payload references asset_pointer / file-service://
    # User messages (type=conversation.message) never satisfy these, so attacker-controlled
    # substrings in user input cannot inject file ids into state.
    is_patch_event = isinstance(event, dict) and event.get("o") == "patch"
    image_context = (
        (isinstance(event, dict) and is_image_tool_event(event))
        or state.tool_invoked is True
        or (is_patch_event and ("asset_pointer" in payload or "file-service://" in payload))
    )
    if image_context:
        add_unique(state.file_ids, file_ids)
        add_unique(state.sediment_ids, sediment_ids)
    if not isinstance(event, dict):
        return
    state.conversation_id = str(event.get("conversation_id") or state.conversation_id)
    value = event.get("v")
    if isinstance(value, dict):
        state.conversation_id = str(value.get("conversation_id") or state.conversation_id)
    if event.get("type") == "moderation":
        moderation = event.get("moderation_response")
        if isinstance(moderation, dict) and moderation.get("blocked") is True:
            state.blocked = True
    if event.get("type") == "server_ste_metadata":
        metadata = event.get("metadata")
        if isinstance(metadata, dict):
            if isinstance(metadata.get("tool_invoked"), bool):
                state.tool_invoked = metadata["tool_invoked"]
            state.turn_use_case = str(metadata.get("turn_use_case") or state.turn_use_case)


def conversation_base_event(event_type: str, state: ConversationState, **extra: Any) -> dict[str, Any]:
    return {
        "type": event_type,
        "text": state.text,
        "conversation_id": state.conversation_id,
        "file_ids": list(state.file_ids),
        "sediment_ids": list(state.sediment_ids),
        "blocked": state.blocked,
        "tool_invoked": state.tool_invoked,
        "turn_use_case": state.turn_use_case,
        **extra,
    }


def iter_conversation_payloads(payloads: Iterator[str], history_text: str = "",
                               history_messages: list[str] | None = None) -> Iterator[dict[str, Any]]:
    state = ConversationState()
    history_messages = history_messages or []
    history_index = 0
    for payload in payloads:
        # print(f"[upstream_sse] {payload}", flush=True)
        if not payload:
            continue
        if payload == "[DONE]":
            yield conversation_base_event("conversation.done", state, done=True)
            break
        try:
            event = json.loads(payload)
        except json.JSONDecodeError:
            update_conversation_state(state, payload)
            yield conversation_base_event("conversation.raw", state, payload=payload)
            continue
        if not isinstance(event, dict):
            yield conversation_base_event("conversation.event", state, raw=event)
            continue
        update_conversation_state(state, payload, event)
        if history_index < len(history_messages) and event_assistant_text(event, history_text) == history_messages[history_index]:
            history_index += 1
            state.text = ""
            continue
        next_text = assistant_text(event, state.text, history_text)
        if next_text != state.text:
            delta = next_text[len(state.text):] if next_text.startswith(state.text) else next_text
            state.text = next_text
            yield conversation_base_event("conversation.delta", state, raw=event, delta=delta)
            continue
        yield conversation_base_event("conversation.event", state, raw=event)


def conversation_events(
    backend: OpenAIBackendAPI,
    messages: list[dict[str, Any]] | None = None,
    model: str = "auto",
    prompt: str = "",
    images: list[str] | None = None,
    size: str | None = None,
) -> Iterator[dict[str, Any]]:
    normalized = normalize_messages(messages or ([{"role": "user", "content": prompt}] if prompt else []))
    image_model = str(model or "").strip() in IMAGE_MODELS
    history_text = "" if image_model else assistant_history_text(normalized)
    history_messages = [] if image_model else assistant_history_messages(normalized)
    final_prompt = prompt_with_global_system(build_image_prompt(prompt, size)) if image_model else prompt
    payloads = backend.stream_conversation(
        messages=normalized,
        model=model,
        prompt=final_prompt,
        images=images if image_model else None,
        system_hints=["picture_v2"] if image_model else None,
    )
    yield from iter_conversation_payloads(payloads, history_text, history_messages)


def text_backend() -> OpenAIBackendAPI:
    return OpenAIBackendAPI(access_token=account_service.get_text_access_token())


def stream_text_deltas(backend: OpenAIBackendAPI, request: ConversationRequest) -> Iterator[str]:
    attempted_tokens: set[str] = set()
    token = getattr(backend, "access_token", "")
    emitted = False
    while True:
        if token and token in attempted_tokens:
            raise RuntimeError("no available text account")
        if token:
            attempted_tokens.add(token)
        try:
            active_backend = OpenAIBackendAPI(access_token=token)
            for event in conversation_events(active_backend, messages=request.messages, model=request.model, prompt=request.prompt):
                if event.get("type") != "conversation.delta":
                    continue
                delta = str(event.get("delta") or "")
                if delta:
                    emitted = True
                    yield delta
            account_service.mark_text_used(token)
            return
        except Exception as exc:
            error_message = str(exc)
            if token and not emitted and is_token_invalid_error(error_message):
                account_service.remove_invalid_token(token, "text_stream")
                token = account_service.get_text_access_token(attempted_tokens)
                if token:
                    continue
            raise


def collect_text(backend: OpenAIBackendAPI, request: ConversationRequest) -> str:
    return "".join(stream_text_deltas(backend, request))


def stream_image_outputs(
        backend: OpenAIBackendAPI,
        request: ConversationRequest,
        index: int = 1,
        total: int = 1,
) -> Iterator[ImageOutput]:
    last: dict[str, Any] = {}
    for event in conversation_events(
            backend,
            prompt=request.prompt,
            model=request.model,
            images=request.images or [],
            size=request.size,
    ):
        last = event
        if event.get("type") == "conversation.delta":
            yield ImageOutput(
                kind="progress",
                model=request.model,
                index=index,
                total=total,
                text=str(event.get("delta") or ""),
                upstream_event_type="conversation.delta",
            )
            continue
        if event.get("type") == "conversation.event":
            raw = event.get("raw")
            raw_type = str(raw.get("type") or "") if isinstance(raw, dict) else ""
            yield ImageOutput(
                kind="progress",
                model=request.model,
                index=index,
                total=total,
                upstream_event_type=raw_type,
            )

    conversation_id = str(last.get("conversation_id") or "")
    file_ids = [str(item) for item in last.get("file_ids") or []]
    sediment_ids = [str(item) for item in last.get("sediment_ids") or []]
    message = str(last.get("text") or "").strip()
    logger.info({
        "event": "image_stream_resolve_start",
        "conversation_id": conversation_id,
        "file_ids": file_ids,
        "sediment_ids": sediment_ids,
        "tool_invoked": last.get("tool_invoked"),
        "turn_use_case": last.get("turn_use_case"),
    })
    if message and not file_ids and not sediment_ids and last.get("blocked"):
        yield ImageOutput(kind="message", model=request.model, index=index, total=total, text=message)
        return
    should_poll_for_image = bool(request.images) or last.get("turn_use_case") == "image gen"
    if message and not file_ids and not sediment_ids and not should_poll_for_image:
        yield ImageOutput(kind="message", model=request.model, index=index, total=total, text=message)
        return

    image_urls = backend.resolve_conversation_image_urls(conversation_id, file_ids, sediment_ids)
    if image_urls:
        image_items = [
            {"b64_json": base64.b64encode(image_data).decode("ascii")}
            for image_data in backend.download_image_bytes(image_urls)
        ]
        data = format_image_result(
            image_items,
            request.prompt,
            request.response_format,
            request.base_url,
            int(time.time()),
        )["data"]
        if data:
            yield ImageOutput(kind="result", model=request.model, index=index, total=total, data=data)
        return

    if message:
        yield ImageOutput(kind="message", model=request.model, index=index, total=total, text=message)


def stream_image_outputs_with_pool(request: ConversationRequest) -> Iterator[ImageOutput]:
    if str(request.model or "").strip() not in IMAGE_MODELS:
        raise ImageGenerationError("unsupported image model,supported models: " + ", ".join(IMAGE_MODELS))

    emitted = False
    last_error = ""
    for index in range(1, request.n + 1):
        while True:
            try:
                token = account_service.get_available_access_token()
            except RuntimeError as exc:
                if emitted:
                    return
                raise ImageGenerationError(str(exc) or "image generation failed") from exc

            emitted_for_token = False
            returned_message = False
            returned_result = False
            try:
                backend = OpenAIBackendAPI(access_token=token)
                for output in stream_image_outputs(backend, request, index, request.n):
                    if output.kind == "message" and request.message_as_error:
                        raise ImageGenerationError(
                            output.text or "Image generation was rejected by upstream policy.",
                            status_code=400,
                            error_type="invalid_request_error",
                            code="content_policy_violation",
                        )
                    emitted = True
                    emitted_for_token = True
                    returned_message = output.kind == "message"
                    returned_result = returned_result or output.kind == "result"
                    yield output
                if returned_message or not returned_result:
                    account_service.mark_image_result(token, False)
                    return
                account_service.mark_image_result(token, True)
                break
            except ImagePollTimeoutError:
                raise
            except ImageGenerationError:
                account_service.mark_image_result(token, False)
                raise
            except Exception as exc:
                account_service.mark_image_result(token, False)
                last_error = str(exc)
                logger.warning({"event": "image_stream_fail", "request_token": token, "error": last_error})
                if not emitted_for_token and is_token_invalid_error(last_error):
                    account_service.remove_invalid_token(token, "image_stream")
                    continue
                raise ImageGenerationError(image_stream_error_message(last_error)) from exc

    if not emitted:
        if not last_error:
            last_error = "no account in the pool could generate images — check account quota and rate-limit status"
        raise ImageGenerationError(image_stream_error_message(last_error))


def stream_image_chunks(outputs: Iterable[ImageOutput]) -> Iterator[dict[str, Any]]:
    for output in outputs:
        yield output.to_chunk()


def collect_image_outputs(outputs: Iterable[ImageOutput]) -> dict[str, Any]:
    created = None
    data: list[dict[str, Any]] = []
    message = ""
    progress_parts: list[str] = []
    for output in outputs:
        created = created or output.created
        if output.kind == "progress" and output.text:
            progress_parts.append(output.text)
        elif output.kind == "message":
            message = output.text
        elif output.kind == "result":
            data.extend(output.data)

    result: dict[str, Any] = {"created": created or int(time.time()), "data": data}
    if not data:
        text = message or "".join(progress_parts).strip()
        if text:
            result["message"] = text
    return result