import os
import json
from threading import Lock
from typing import List, Optional, Union, Iterator, Dict
from typing_extensions import TypedDict, Literal

import llama_cpp

from fastapi import Depends, FastAPI
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, BaseSettings, Field, create_model_from_typeddict
from sse_starlette.sse import EventSourceResponse


class Settings(BaseSettings):
    model: str = os.environ.get("MODEL", "null")
    n_ctx: int = 2048
    n_batch: int = 512
    n_threads: int = max((os.cpu_count() or 2) // 2, 1)
    f16_kv: bool = True
    use_mlock: bool = False  # This causes a silent failure on platforms that don't support mlock (e.g. Windows) took forever to figure out...
    use_mmap: bool = True
    embedding: bool = True
    last_n_tokens_size: int = 64
    logits_all: bool = False
    cache: bool = False  # WARNING: This is an experimental feature
    vocab_only: bool = False


app = FastAPI(
    title="🦙 llama.cpp Python API",
    version="0.0.1",
)
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

llama: llama_cpp.Llama = None
def init_llama(settings: Settings = None):
    if settings is None:
        settings = Settings()
    global llama
    llama = llama_cpp.Llama(
        settings.model,
        f16_kv=settings.f16_kv,
        use_mlock=settings.use_mlock,
        use_mmap=settings.use_mmap,
        embedding=settings.embedding,
        logits_all=settings.logits_all,
        n_threads=settings.n_threads,
        n_batch=settings.n_batch,
        n_ctx=settings.n_ctx,
        last_n_tokens_size=settings.last_n_tokens_size,
        vocab_only=settings.vocab_only,
    )
    if settings.cache:
        cache = llama_cpp.LlamaCache()
        llama.set_cache(cache)

llama_lock = Lock()
def get_llama():
    with llama_lock:
        yield llama

class CreateCompletionRequest(BaseModel):
    prompt: Union[str, List[str]]
    suffix: Optional[str] = Field(None)
    max_tokens: int = 16
    temperature: float = 0.8
    top_p: float = 0.95
    echo: bool = False
    stop: Optional[List[str]] = []
    stream: bool = False

    # ignored or currently unsupported
    model: Optional[str] = Field(None)
    n: Optional[int] = 1
    logprobs: Optional[int] = Field(None)
    presence_penalty: Optional[float] = 0
    frequency_penalty: Optional[float] = 0
    best_of: Optional[int] = 1
    logit_bias: Optional[Dict[str, float]] = Field(None)
    user: Optional[str] = Field(None)

    # llama.cpp specific parameters
    top_k: int = 40
    repeat_penalty: float = 1.1

    class Config:
        schema_extra = {
            "example": {
                "prompt": "\n\n### Instructions:\nWhat is the capital of France?\n\n### Response:\n",
                "stop": ["\n", "###"],
            }
        }


CreateCompletionResponse = create_model_from_typeddict(llama_cpp.Completion)


@app.post(
    "/v1/completions",
    response_model=CreateCompletionResponse,
)
def create_completion(
    request: CreateCompletionRequest, llama: llama_cpp.Llama = Depends(get_llama)
):
    if isinstance(request.prompt, list):
        request.prompt = "".join(request.prompt)

    completion_or_chunks = llama(
        **request.dict(
            exclude={
                "model",
                "n",
                "frequency_penalty",
                "presence_penalty",
                "best_of",
                "logit_bias",
                "user",
            }
        )
    )
    if request.stream:
        chunks: Iterator[llama_cpp.CompletionChunk] = completion_or_chunks  # type: ignore
        return EventSourceResponse(dict(data=json.dumps(chunk)) for chunk in chunks)
    completion: llama_cpp.Completion = completion_or_chunks  # type: ignore
    return completion


class CreateEmbeddingRequest(BaseModel):
    model: Optional[str]
    input: str
    user: Optional[str]

    class Config:
        schema_extra = {
            "example": {
                "input": "The food was delicious and the waiter...",
            }
        }


CreateEmbeddingResponse = create_model_from_typeddict(llama_cpp.Embedding)


@app.post(
    "/v1/embeddings",
    response_model=CreateEmbeddingResponse,
)
def create_embedding(
    request: CreateEmbeddingRequest, llama: llama_cpp.Llama = Depends(get_llama)
):
    return llama.create_embedding(**request.dict(exclude={"model", "user"}))


class ChatCompletionRequestMessage(BaseModel):
    role: Union[Literal["system"], Literal["user"], Literal["assistant"]]
    content: str
    user: Optional[str] = None


class CreateChatCompletionRequest(BaseModel):
    model: Optional[str]
    messages: List[ChatCompletionRequestMessage]
    temperature: float = 0.8
    top_p: float = 0.95
    stream: bool = False
    stop: Optional[List[str]] = []
    max_tokens: int = 128

    # ignored or currently unsupported
    model: Optional[str] = Field(None)
    n: Optional[int] = 1
    presence_penalty: Optional[float] = 0
    frequency_penalty: Optional[float] = 0
    logit_bias: Optional[Dict[str, float]] = Field(None)
    user: Optional[str] = Field(None)

    # llama.cpp specific parameters
    repeat_penalty: float = 1.1

    class Config:
        schema_extra = {
            "example": {
                "messages": [
                    ChatCompletionRequestMessage(
                        role="system", content="You are a helpful assistant."
                    ),
                    ChatCompletionRequestMessage(
                        role="user", content="What is the capital of France?"
                    ),
                ]
            }
        }


CreateChatCompletionResponse = create_model_from_typeddict(llama_cpp.ChatCompletion)


@app.post(
    "/v1/chat/completions",
    response_model=CreateChatCompletionResponse,
)
def create_chat_completion(
    request: CreateChatCompletionRequest,
    llama: llama_cpp.Llama = Depends(get_llama),
) -> Union[llama_cpp.ChatCompletion, EventSourceResponse]:
    completion_or_chunks = llama.create_chat_completion(
        **request.dict(
            exclude={
                "model",
                "n",
                "presence_penalty",
                "frequency_penalty",
                "logit_bias",
                "user",
            }
        ),
    )

    if request.stream:

        async def server_sent_events(
            chat_chunks: Iterator[llama_cpp.ChatCompletionChunk],
        ):
            for chat_chunk in chat_chunks:
                yield dict(data=json.dumps(chat_chunk))
            yield dict(data="[DONE]")

        chunks: Iterator[llama_cpp.ChatCompletionChunk] = completion_or_chunks  # type: ignore

        return EventSourceResponse(
            server_sent_events(chunks),
        )
    completion: llama_cpp.ChatCompletion = completion_or_chunks  # type: ignore
    return completion


class ModelData(TypedDict):
    id: str
    object: Literal["model"]
    owned_by: str
    permissions: List[str]


class ModelList(TypedDict):
    object: Literal["list"]
    data: List[ModelData]


GetModelResponse = create_model_from_typeddict(ModelList)


@app.get("/v1/models", response_model=GetModelResponse)
def get_models() -> ModelList:
    return {
        "object": "list",
        "data": [
            {
                "id": llama.model_path,
                "object": "model",
                "owned_by": "me",
                "permissions": [],
            }
        ],
    }