import tempfile
from binascii import hexlify, unhexlify
from collections.abc import Generator, Mapping
from enum import Enum
from operator import itemgetter
from typing import Any

from pydantic import BaseModel

from core.base.tts.audio_mime import get_model_audio_mime_type, inspect_audio_stream
from core.credit_usage import CreditUsageCreatedBy
from core.llm_generator.output_parser.structured_output import invoke_llm_with_structured_output
from core.model_context import get_credit_usage_metadata, with_credit_usage_created_by
from core.model_manager import ModelManager
from core.plugin.backwards_invocation.base import BaseBackwardsInvocation
from core.plugin.entities.request import (
    InvokableModelCatalogItem,
    InvokableModelCatalogPage,
    InvokableModelTokenLimits,
    RequestInvokeLLM,
    RequestInvokeLLMWithStructuredOutput,
    RequestInvokeModeration,
    RequestInvokeMultimodalEmbedding,
    RequestInvokeRerank,
    RequestInvokeSpeech2Text,
    RequestInvokeSummary,
    RequestInvokeTextEmbedding,
    RequestInvokeTTS,
    RequestListModels,
)
from core.plugin.impl.model_runtime_factory import create_plugin_provider_manager
from core.plugin.plugin_service import PluginService
from core.tools.entities.tool_entities import ToolProviderType
from core.tools.utils.model_invocation_utils import ModelInvocationUtils
from graphon.model_runtime.entities.llm_entities import (
    LLMResult,
    LLMResultChunk,
    LLMResultChunkDelta,
    LLMResultChunkWithStructuredOutput,
    LLMResultWithStructuredOutput,
)
from graphon.model_runtime.entities.message_entities import (
    PromptMessage,
    SystemPromptMessage,
    UserPromptMessage,
)
from graphon.model_runtime.entities.model_entities import ModelType
from models.account import Tenant
from models.provider_ids import ModelProviderID


def _json_compatible(value: Any) -> Any:
    """Convert model-runtime metadata into stable JSON-compatible values."""
    if isinstance(value, BaseModel):
        return value.model_dump(mode="json")
    if isinstance(value, Enum):
        return value.value
    if isinstance(value, Mapping):
        return {str(_json_compatible(key)): _json_compatible(child) for key, child in value.items()}
    if isinstance(value, list | tuple | set):
        return [_json_compatible(child) for child in value]
    return value


class PluginModelBackwardsInvocation(BaseBackwardsInvocation):
    @classmethod
    def _model_token_limits(
        cls, *, tenant_id: str, user_id: str, provider: str, model: str
    ) -> InvokableModelTokenLimits | None:
        """Resolve the selected model only; listing a catalog must not cause N schema RPCs.

        Custom-model settings (including the configured max-token rule) are resolved by the same
        tenant-bound runtime as invocation. Never infer an output maximum from a rule's default.
        """
        instance = cls._get_bound_model_instance(
            tenant_id=tenant_id, user_id=user_id, provider=provider, model_type=ModelType.LLM, model=model
        )
        schema = instance.get_model_schema()
        if schema is None:
            return None

        def positive_limit(value: Any) -> int | None:
            if isinstance(value, bool) or not isinstance(value, int | float):
                return None
            if not 0 < value <= 1_000_000_000 or value != int(value):
                return None
            return int(value)

        output_rules = [
            rule
            for rule in schema.parameter_rules
            if rule.name in {"max_tokens", "max_output_tokens", "max_completion_tokens", "maxtoken"}
            or rule.use_template == "max_tokens"
        ]
        bounded_rules = [(rule, positive_limit(rule.max)) for rule in output_rules]
        bounded_rules = [(rule, limit) for rule, limit in bounded_rules if limit is not None]
        # Multiple declared aliases must not let the caller bypass the tighter bound.
        selected = min(bounded_rules, key=itemgetter(1)) if bounded_rules else None
        return InvokableModelTokenLimits(
            context_tokens=positive_limit(schema.model_properties.get("context_size")),
            max_output_tokens=selected[1] if selected else None,
            output_parameter=selected[0].name if selected else None,
        )

    @staticmethod
    @with_credit_usage_created_by(CreditUsageCreatedBy.PLUGIN_API)
    def _get_bound_model_instance(
        *,
        tenant_id: str,
        user_id: str | None,
        provider: str,
        model_type: ModelType,
        model: str,
        request_metadata: Mapping[str, object] | None = None,
    ):
        # Bind explicit caller attribution before the decorator exits. Streaming
        # invocations settle quota later, outside this context manager's lifetime.
        metadata = {**(get_credit_usage_metadata() or {}), **(request_metadata or {})}
        return ModelManager.for_tenant(
            tenant_id=tenant_id, user_id=user_id, request_metadata=metadata
        ).get_model_instance(
            tenant_id=tenant_id,
            provider=provider,
            model_type=model_type,
            model=model,
        )

    @classmethod
    def invoke_llm(
        cls, user_id: str, tenant: Tenant, payload: RequestInvokeLLM
    ) -> Generator[LLMResultChunk] | LLMResult:
        """
        invoke llm
        """
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        # invoke model
        response = model_instance.invoke_llm(
            prompt_messages=payload.prompt_messages,
            model_parameters=payload.completion_params,
            tools=payload.tools,
            stop=payload.stop,
            stream=True if payload.stream is None else payload.stream,
        )

        if isinstance(response, Generator):

            def handle() -> Generator[LLMResultChunk]:
                for chunk in response:
                    chunk.prompt_messages = []
                    yield chunk

            return handle()
        else:

            def handle_non_streaming(response: LLMResult) -> Generator[LLMResultChunk]:
                yield LLMResultChunk(
                    model=response.model,
                    prompt_messages=[],
                    system_fingerprint=response.system_fingerprint,
                    delta=LLMResultChunkDelta(
                        index=0,
                        message=response.message,
                        usage=response.usage,
                        finish_reason="",
                    ),
                )

            return handle_non_streaming(response)

    @classmethod
    def invoke_llm_with_structured_output(
        cls, user_id: str, tenant: Tenant, payload: RequestInvokeLLMWithStructuredOutput
    ):
        """
        invoke llm with structured output
        """
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        model_schema = model_instance.model_type_instance.get_model_schema(payload.model, model_instance.credentials)

        if not model_schema:
            raise ValueError(f"Model schema not found for {payload.model}")

        response = invoke_llm_with_structured_output(
            provider=payload.provider,
            model_schema=model_schema,
            model_instance=model_instance,
            prompt_messages=payload.prompt_messages,
            json_schema=payload.structured_output_schema,
            tools=payload.tools,
            stop=payload.stop,
            stream=True if payload.stream is None else payload.stream,
            model_parameters=payload.completion_params,
        )

        if isinstance(response, Generator):

            def handle() -> Generator[LLMResultChunkWithStructuredOutput]:
                for chunk in response:
                    chunk.prompt_messages = []
                    yield chunk

            return handle()
        else:

            def handle_non_streaming(
                response: LLMResultWithStructuredOutput,
            ) -> Generator[LLMResultChunkWithStructuredOutput]:
                yield LLMResultChunkWithStructuredOutput(
                    model=response.model,
                    prompt_messages=[],
                    system_fingerprint=response.system_fingerprint,
                    structured_output=response.structured_output,
                    delta=LLMResultChunkDelta(
                        index=0,
                        message=response.message,
                        usage=response.usage,
                        finish_reason="",
                    ),
                )

            return handle_non_streaming(response)

    @classmethod
    def invoke_text_embedding(cls, user_id: str, tenant: Tenant, payload: RequestInvokeTextEmbedding):
        """
        invoke text embedding
        """
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        # invoke model
        response = model_instance.invoke_text_embedding(texts=payload.texts, input_type=payload.input_type)

        return response

    @classmethod
    def invoke_multimodal_embedding(
        cls,
        user_id: str,
        tenant: Tenant,
        payload: RequestInvokeMultimodalEmbedding,
    ):
        """Invoke multimodal embedding through the tenant-bound model instance."""
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        response = model_instance.invoke_multimodal_embedding(
            multimodel_documents=[document.model_dump(exclude_none=True) for document in payload.documents],
            input_type=payload.input_type,
        )

        return response

    @classmethod
    def invoke_rerank(cls, user_id: str, tenant: Tenant, payload: RequestInvokeRerank):
        """
        invoke rerank
        """
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        # invoke model
        response = model_instance.invoke_rerank(
            query=payload.query,
            docs=payload.docs,
            score_threshold=payload.score_threshold,
            top_n=payload.top_n,
        )

        return response

    @classmethod
    def list_models(
        cls,
        tenant_id: str,
        user_id: str,
        payload: RequestListModels,
    ) -> InvokableModelCatalogPage:
        """List only models that are active for the tenant's Dify configuration."""
        provider_manager = create_plugin_provider_manager(tenant_id=tenant_id, user_id=user_id)
        active_models = provider_manager.get_configurations(tenant_id).get_models(
            model_type=payload.model_type,
            only_active=True,
        )

        installed_identities: dict[str, str] = {}
        for plugin in PluginService.list(tenant_id):
            existing = installed_identities.get(plugin.plugin_id)
            if existing is not None and existing != plugin.plugin_unique_identifier:
                raise ValueError(f"Ambiguous installed identity for model plugin {plugin.plugin_id}")
            installed_identities[plugin.plugin_id] = plugin.plugin_unique_identifier

        requested_provider = str(ModelProviderID(payload.provider)) if payload.provider else None
        matched_models = [
            model
            for model in active_models
            if (requested_provider is None or model.provider.provider == requested_provider)
            and (payload.model is None or model.model == payload.model)
        ]
        matched_models.sort(key=lambda model: (model.provider.provider, model.model))

        page_models = matched_models[payload.offset : payload.offset + payload.limit]
        items: list[InvokableModelCatalogItem] = []
        for model in page_models:
            provider_id = ModelProviderID(model.provider.provider)
            unique_identifier = installed_identities.get(provider_id.plugin_id)
            if unique_identifier is None:
                raise ValueError(f"Installed identity not found for active model plugin {provider_id.plugin_id}")
            items.append(
                InvokableModelCatalogItem(
                    plugin_id=provider_id.plugin_id,
                    plugin_unique_identifier=unique_identifier,
                    provider=provider_id.provider_name,
                    model=model.model,
                    model_type=model.model_type,
                    capabilities={
                        "deprecated": model.deprecated,
                        "features": _json_compatible(model.features or []),
                        "fetchFrom": _json_compatible(model.fetch_from),
                        "modelProperties": _json_compatible(model.model_properties),
                        "modelType": model.model_type.value,
                        "status": _json_compatible(model.status),
                    },
                    token_limits=cls._model_token_limits(
                        tenant_id=tenant_id,
                        user_id=user_id,
                        provider=model.provider.provider,
                        model=model.model,
                    )
                    if payload.model and model.model_type == ModelType.LLM
                    else None,
                )
            )

        next_offset = payload.offset + len(page_models)
        return InvokableModelCatalogPage(
            items=items,
            next_offset=next_offset if next_offset < len(matched_models) else None,
        )

    @classmethod
    def invoke_tts(cls, user_id: str, tenant: Tenant, payload: RequestInvokeTTS):
        """
        invoke tts
        """
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        def handle() -> Generator[dict[str, Any]]:
            response = model_instance.invoke_tts(content_text=payload.content_text, voice=payload.voice)
            audio_stream, mime_type = inspect_audio_stream(response, get_model_audio_mime_type(model_instance))
            for chunk in audio_stream:
                yield {"result": hexlify(chunk).decode("utf-8"), "mime_type": mime_type}

        return handle()

    @classmethod
    def invoke_speech2text(cls, user_id: str, tenant: Tenant, payload: RequestInvokeSpeech2Text):
        """
        invoke speech2text
        """
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        # invoke model
        with tempfile.NamedTemporaryFile(suffix=".mp3", mode="wb", delete=True) as temp:
            temp.write(unhexlify(payload.file))
            temp.flush()
            temp.seek(0)

            response = model_instance.invoke_speech2text(file=temp)

            return {
                "result": response,
            }

    @classmethod
    def invoke_moderation(cls, user_id: str, tenant: Tenant, payload: RequestInvokeModeration):
        """
        invoke moderation
        """
        model_instance = cls._get_bound_model_instance(
            tenant_id=tenant.id,
            user_id=user_id,
            provider=payload.provider,
            model_type=payload.model_type,
            model=payload.model,
            request_metadata=payload.credit_usage_metadata,
        )

        # invoke model
        response = model_instance.invoke_moderation(text=payload.text)

        return {
            "result": response,
        }

    @classmethod
    def get_system_model_max_tokens(cls, tenant_id: str, user_id: str | None = None) -> int:
        """
        get system model max tokens
        """
        return ModelInvocationUtils.get_max_llm_context_tokens(tenant_id=tenant_id, user_id=user_id)

    @classmethod
    def get_prompt_tokens(cls, tenant_id: str, prompt_messages: list[PromptMessage], user_id: str | None = None) -> int:
        """
        get prompt tokens
        """
        return ModelInvocationUtils.calculate_tokens(
            tenant_id=tenant_id,
            prompt_messages=prompt_messages,
            user_id=user_id,
        )

    @classmethod
    def invoke_system_model(
        cls,
        user_id: str,
        tenant: Tenant,
        prompt_messages: list[PromptMessage],
    ) -> LLMResult:
        """
        invoke system model
        """
        return ModelInvocationUtils.invoke(
            user_id=user_id,
            tenant_id=tenant.id,
            tool_type=ToolProviderType.PLUGIN,
            tool_name="plugin",
            prompt_messages=prompt_messages,
            caller_user_id=user_id,
        )

    @classmethod
    def invoke_summary(cls, user_id: str, tenant: Tenant, payload: RequestInvokeSummary):
        """
        invoke summary
        """
        max_tokens = cls.get_system_model_max_tokens(tenant_id=tenant.id, user_id=user_id)
        content = payload.text

        summary_prompt = """You are a professional language researcher, you are interested in the language
and you can quickly aimed at the main point of an webpage and reproduce it in your own words but
retain the original meaning and keep the key points.
however, the text you got is too long, what you got is possible a part of the text.
Please summarize the text you got.

Here is the extra instruction you need to follow:
<extra_instruction>
{payload.instruction}
</extra_instruction>
"""

        if (
            cls.get_prompt_tokens(
                tenant_id=tenant.id,
                prompt_messages=[UserPromptMessage(content=content)],
                user_id=user_id,
            )
            < max_tokens * 0.6
        ):
            return content

        def get_prompt_tokens(content: str) -> int:
            return cls.get_prompt_tokens(
                tenant_id=tenant.id,
                prompt_messages=[
                    SystemPromptMessage(content=summary_prompt.replace("{payload.instruction}", payload.instruction)),
                    UserPromptMessage(content=content),
                ],
                user_id=user_id,
            )

        def summarize(content: str) -> str:
            summary = cls.invoke_system_model(
                user_id=user_id,
                tenant=tenant,
                prompt_messages=[
                    SystemPromptMessage(content=summary_prompt.replace("{payload.instruction}", payload.instruction)),
                    UserPromptMessage(content=content),
                ],
            )

            assert isinstance(summary.message.content, str)
            return summary.message.content

        lines = content.split("\n")
        new_lines: list[str] = []
        # split long line into multiple lines
        for i in range(len(lines)):
            line = lines[i]
            if not line.strip():
                continue
            if len(line) < max_tokens * 0.5:
                new_lines.append(line)
            elif get_prompt_tokens(line) > max_tokens * 0.7:
                while get_prompt_tokens(line) > max_tokens * 0.7:
                    new_lines.append(line[: int(max_tokens * 0.5)])
                    line = line[int(max_tokens * 0.5) :]
                new_lines.append(line)
            else:
                new_lines.append(line)

        # merge lines into messages with max tokens
        messages: list[str] = []
        for line in new_lines:
            if len(messages) == 0:
                messages.append(line)
            else:
                if len(messages[-1]) + len(line) < max_tokens * 0.5:
                    messages[-1] += line
                elif get_prompt_tokens(messages[-1] + line) > max_tokens * 0.7:
                    messages.append(line)
                else:
                    messages[-1] += line

        summaries = []
        for i in range(len(messages)):
            message = messages[i]
            summary = summarize(message)
            summaries.append(summary)

        result = "\n".join(summaries)

        if (
            cls.get_prompt_tokens(
                tenant_id=tenant.id,
                prompt_messages=[UserPromptMessage(content=result)],
                user_id=user_id,
            )
            > max_tokens * 0.7
        ):
            return cls.invoke_summary(
                user_id=user_id,
                tenant=tenant,
                payload=RequestInvokeSummary(text=result, instruction=payload.instruction),
            )

        return result