import time
from typing import (AsyncGenerator, AsyncIterator, Callable, Dict, List,
                    Optional, Tuple)

from fastapi import Request

from vllm.engine.async_llm_engine import AsyncLLMEngine
from vllm.entrypoints.openai.protocol import (CompletionRequest,
                                              CompletionResponse,
                                              CompletionResponseChoice,
                                              CompletionResponseStreamChoice,
                                              CompletionStreamResponse,
                                              LogProbs, UsageInfo)
from vllm.entrypoints.openai.serving_engine import LoRA, OpenAIServing
from vllm.logger import init_logger
from vllm.model_executor.guided_decoding import (
    get_guided_decoding_logits_processor)
from vllm.outputs import RequestOutput
from vllm.utils import merge_async_iterators, random_uuid

logger = init_logger(__name__)

TypeTokenIDs = List[int]
TypeTopLogProbs = List[Optional[Dict[int, float]]]
TypeCreateLogProbsFn = Callable[
    [TypeTokenIDs, TypeTopLogProbs, Optional[int], int], LogProbs]


def parse_prompt_format(prompt) -> Tuple[bool, list]:
    # get the prompt, openai supports the following
    # "a string, array of strings, array of tokens, or array of token arrays."
    prompt_is_tokens = False
    prompts = [prompt]  # case 1: a string
    if isinstance(prompt, list):
        if len(prompt) == 0:
            raise ValueError("please provide at least one prompt")
        elif isinstance(prompt[0], str):
            prompt_is_tokens = False
            prompts = prompt  # case 2: array of strings
        elif isinstance(prompt[0], int):
            prompt_is_tokens = True
            prompts = [prompt]  # case 3: array of tokens
        elif isinstance(prompt[0], list) and isinstance(prompt[0][0], int):
            prompt_is_tokens = True
            prompts = prompt  # case 4: array of token arrays
        else:
            raise ValueError("prompt must be a string, array of strings, "
                             "array of tokens, or array of token arrays")
    return prompt_is_tokens, prompts


class OpenAIServingCompletion(OpenAIServing):

    def __init__(self,
                 engine: AsyncLLMEngine,
                 served_model: str,
                 lora_modules: Optional[List[LoRA]] = None):
        super().__init__(engine=engine,
                         served_model=served_model,
                         lora_modules=lora_modules)

    async def create_completion(self, request: CompletionRequest,
                                raw_request: Request):
        """Completion API similar to OpenAI's API.

        See https://platform.openai.com/docs/api-reference/completions/create
        for the API specification. This API mimics the OpenAI Completion API.

        NOTE: Currently we do not support the following feature:
            - suffix (the language models we currently support do not support
            suffix)
        """
        error_check_ret = await self._check_model(request)
        if error_check_ret is not None:
            return error_check_ret

        # Return error for unsupported features.
        if request.suffix is not None:
            return self.create_error_response(
                "suffix is not currently supported")

        model_name = request.model
        request_id = f"cmpl-{random_uuid()}"
        created_time = int(time.time())

        # Schedule the request and get the result generator.
        generators = []
        try:
            sampling_params = request.to_sampling_params()
            lora_request = self._maybe_get_lora(request)
            guided_decode_logit_processor = (
                await get_guided_decoding_logits_processor(
                    request, await self.engine.get_tokenizer()))
            if guided_decode_logit_processor is not None:
                if sampling_params.logits_processors is None:
                    sampling_params.logits_processors = []
                sampling_params.logits_processors.append(
                    guided_decode_logit_processor)
            prompt_is_tokens, prompts = parse_prompt_format(request.prompt)

            for i, prompt in enumerate(prompts):
                if prompt_is_tokens:
                    prompt_formats = self._validate_prompt_and_tokenize(
                        request,
                        prompt_ids=prompt,
                        truncate_prompt_tokens=sampling_params.
                        truncate_prompt_tokens)
                else:
                    prompt_formats = self._validate_prompt_and_tokenize(
                        request,
                        prompt=prompt,
                        truncate_prompt_tokens=sampling_params.
                        truncate_prompt_tokens)
                prompt_ids, prompt_text = prompt_formats

                generators.append(
                    self.engine.generate(prompt_text,
                                         sampling_params,
                                         f"{request_id}-{i}",
                                         prompt_token_ids=prompt_ids,
                                         lora_request=lora_request))
        except ValueError as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))

        result_generator: AsyncIterator[Tuple[
            int, RequestOutput]] = merge_async_iterators(*generators)

        # Similar to the OpenAI API, when n != best_of, we do not stream the
        # results. In addition, we do not stream the results when use
        # beam search.
        stream = (request.stream
                  and (request.best_of is None or request.n == request.best_of)
                  and not request.use_beam_search)

        # Streaming response
        if stream:
            return self.completion_stream_generator(request,
                                                    raw_request,
                                                    result_generator,
                                                    request_id,
                                                    created_time,
                                                    model_name,
                                                    num_prompts=len(prompts))

        # Non-streaming response
        final_res_batch: RequestOutput = [None] * len(prompts)
        try:
            async for i, res in result_generator:
                if await raw_request.is_disconnected():
                    # Abort the request if the client disconnects.
                    await self.engine.abort(f"{request_id}-{i}")
                    return self.create_error_response("Client disconnected")
                final_res_batch[i] = res
            response = self.request_output_to_completion_response(
                final_res_batch, request, request_id, created_time, model_name)
        except ValueError as e:
            # TODO: Use a vllm-specific Validation Error
            return self.create_error_response(str(e))

        # When user requests streaming but we don't stream, we still need to
        # return a streaming response with a single event.
        if request.stream:
            response_json = response.model_dump_json()

            async def fake_stream_generator() -> AsyncGenerator[str, None]:
                yield f"data: {response_json}\n\n"
                yield "data: [DONE]\n\n"

            return fake_stream_generator()

        return response

    async def completion_stream_generator(
        self,
        request: CompletionRequest,
        raw_request: Request,
        result_generator: AsyncIterator[Tuple[int, RequestOutput]],
        request_id: str,
        created_time: int,
        model_name: str,
        num_prompts: int,
    ) -> AsyncGenerator[str, None]:
        previous_texts = [""] * request.n * num_prompts
        previous_num_tokens = [0] * request.n * num_prompts
        has_echoed = [False] * request.n * num_prompts

        try:
            async for prompt_idx, res in result_generator:

                # Abort the request if the client disconnects.
                if await raw_request.is_disconnected():
                    await self.engine.abort(f"{request_id}-{prompt_idx}")
                    raise StopAsyncIteration()

                for output in res.outputs:
                    i = output.index + prompt_idx * request.n
                    # TODO(simon): optimize the performance by avoiding full
                    # text O(n^2) sending.

                    if request.echo and request.max_tokens == 0:
                        # only return the prompt
                        delta_text = res.prompt
                        delta_token_ids = res.prompt_token_ids
                        top_logprobs = res.prompt_logprobs
                        has_echoed[i] = True
                    elif (request.echo and request.max_tokens > 0
                          and not has_echoed[i]):
                        # echo the prompt and first token
                        delta_text = res.prompt + output.text
                        delta_token_ids = (res.prompt_token_ids +
                                           output.token_ids)
                        top_logprobs = res.prompt_logprobs + (output.logprobs
                                                              or [])
                        has_echoed[i] = True
                    else:
                        # return just the delta
                        delta_text = output.text[len(previous_texts[i]):]
                        delta_token_ids = output.token_ids[
                            previous_num_tokens[i]:]
                        top_logprobs = output.logprobs[previous_num_tokens[
                            i]:] if output.logprobs else None

                    if request.logprobs is not None:
                        logprobs = self._create_logprobs(
                            token_ids=delta_token_ids,
                            top_logprobs=top_logprobs,
                            num_output_top_logprobs=request.logprobs,
                            initial_text_offset=len(previous_texts[i]),
                        )
                    else:
                        logprobs = None

                    previous_texts[i] = output.text
                    previous_num_tokens[i] = len(output.token_ids)
                    finish_reason = output.finish_reason
                    stop_reason = output.stop_reason
                    if output.finish_reason is not None:  # return final usage
                        prompt_tokens = len(res.prompt_token_ids)
                        completion_tokens = len(output.token_ids)
                        final_usage = UsageInfo(
                            prompt_tokens=prompt_tokens,
                            completion_tokens=completion_tokens,
                            total_tokens=prompt_tokens + completion_tokens,
                        )
                    else:
                        final_usage = None
                    response_json = CompletionStreamResponse(
                        id=request_id,
                        created=created_time,
                        model=model_name,
                        choices=[
                            CompletionResponseStreamChoice(
                                index=i,
                                text=delta_text,
                                logprobs=logprobs,
                                finish_reason=finish_reason,
                                stop_reason=stop_reason,
                            )
                        ],
                        usage=final_usage,
                    ).model_dump_json(exclude_unset=True)
                    yield f"data: {response_json}\n\n"
        except ValueError as e:
            # TODO: Use a vllm-specific Validation Error
            data = self.create_streaming_error_response(str(e))
            yield f"data: {data}\n\n"
        yield "data: [DONE]\n\n"

    def request_output_to_completion_response(
        self,
        final_res_batch: List[RequestOutput],
        request: CompletionRequest,
        request_id: str,
        created_time: int,
        model_name: str,
    ) -> CompletionResponse:
        choices = []
        num_prompt_tokens = 0
        num_generated_tokens = 0
        for final_res in final_res_batch:
            assert final_res is not None
            prompt_token_ids = final_res.prompt_token_ids
            prompt_logprobs = final_res.prompt_logprobs
            prompt_text = final_res.prompt

            for output in final_res.outputs:
                if request.echo and request.max_tokens == 0:
                    token_ids = prompt_token_ids
                    top_logprobs = prompt_logprobs
                    output_text = prompt_text
                elif request.echo and request.max_tokens > 0:
                    token_ids = prompt_token_ids + output.token_ids
                    top_logprobs = (prompt_logprobs + output.logprobs
                                    if request.logprobs else None)
                    output_text = prompt_text + output.text
                else:
                    token_ids = output.token_ids
                    top_logprobs = output.logprobs
                    output_text = output.text

                if request.logprobs is not None:
                    assert top_logprobs is not None, (
                        "top_logprobs must be provided when logprobs "
                        "is requested")
                    logprobs = self._create_logprobs(
                        token_ids=token_ids,
                        top_logprobs=top_logprobs,
                        num_output_top_logprobs=request.logprobs,
                    )
                else:
                    logprobs = None

                choice_data = CompletionResponseChoice(
                    index=len(choices),
                    text=output_text,
                    logprobs=logprobs,
                    finish_reason=output.finish_reason,
                    stop_reason=output.stop_reason,
                )
                choices.append(choice_data)

            num_prompt_tokens += len(prompt_token_ids)
            num_generated_tokens += sum(
                len(output.token_ids) for output in final_res.outputs)

        usage = UsageInfo(
            prompt_tokens=num_prompt_tokens,
            completion_tokens=num_generated_tokens,
            total_tokens=num_prompt_tokens + num_generated_tokens,
        )

        return CompletionResponse(
            id=request_id,
            created=created_time,
            model=model_name,
            choices=choices,
            usage=usage,
        )
