# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
from typing import (TYPE_CHECKING, Literal, NamedTuple, Optional, TypedDict,
                    Union, cast, overload)

from typing_extensions import TypeIs

from vllm.utils import is_list_of

from .data import (EmbedsPrompt, ExplicitEncoderDecoderPrompt, ProcessorInputs,
                   PromptType, SingletonInputs, SingletonPrompt, TextPrompt,
                   TokensPrompt)

if TYPE_CHECKING:
    import torch


class ParsedText(TypedDict):
    content: str
    is_tokens: Literal[False]


class ParsedTokens(TypedDict):
    content: list[int]
    is_tokens: Literal[True]


@overload
def parse_and_batch_prompt(
    prompt: Union[str, list[str]], ) -> Sequence[ParsedText]:
    ...


@overload
def parse_and_batch_prompt(
    prompt: Union[list[int], list[list[int]]], ) -> Sequence[ParsedTokens]:
    ...


def parse_and_batch_prompt(
    prompt: Union[str, list[str], list[int], list[list[int]]],
) -> Union[Sequence[ParsedText], Sequence[ParsedTokens]]:
    if isinstance(prompt, str):
        # case 1: a string
        return [ParsedText(content=prompt, is_tokens=False)]

    if isinstance(prompt, list):
        if len(prompt) == 0:
            raise ValueError("please provide at least one prompt")

        if is_list_of(prompt, str):
            # case 2: array of strings
            prompt = cast(list[str], prompt)
            return [
                ParsedText(content=elem, is_tokens=False) for elem in prompt
            ]
        if is_list_of(prompt, int):
            # case 3: array of tokens
            prompt = cast(list[int], prompt)
            return [ParsedTokens(content=prompt, is_tokens=True)]
        if is_list_of(prompt, list):
            prompt = cast(list[list[int]], prompt)
            if len(prompt[0]) == 0:
                raise ValueError("please provide at least one prompt")

            if is_list_of(prompt[0], int):
                # case 4: array of token arrays
                return [
                    ParsedTokens(content=elem, is_tokens=True)
                    for elem in prompt
                ]

    raise TypeError("prompt must be a string, array of strings, "
                    "array of tokens, or array of token arrays")


class ParsedStrPrompt(TypedDict):
    type: Literal["str"]
    content: str


class ParsedTextPrompt(TypedDict):
    type: Literal["text"]
    content: TextPrompt


class ParsedTokensPrompt(TypedDict):
    type: Literal["tokens"]
    content: TokensPrompt


class ParsedEmbedsPrompt(TypedDict):
    type: Literal["embeds"]
    content: EmbedsPrompt


ParsedSingletonPrompt = Union[ParsedStrPrompt, ParsedTextPrompt,
                              ParsedTokensPrompt, ParsedEmbedsPrompt]


@overload
def parse_singleton_prompt(prompt: str) -> ParsedStrPrompt:
    ...


@overload
def parse_singleton_prompt(prompt: TextPrompt) -> ParsedTextPrompt:
    ...


@overload
def parse_singleton_prompt(prompt: TokensPrompt) -> ParsedTokensPrompt:
    ...


@overload
def parse_singleton_prompt(prompt: EmbedsPrompt) -> ParsedEmbedsPrompt:
    ...


def parse_singleton_prompt(prompt: SingletonPrompt) -> ParsedSingletonPrompt:
    if isinstance(prompt, str):
        return ParsedStrPrompt(type="str", content=prompt)
    elif isinstance(prompt, dict):
        # Type ignores are because mypy does not correctly infer the TypedDicts
        # Pyright does succeed.
        if "prompt_embeds" in prompt:
            return ParsedEmbedsPrompt(
                type="embeds", content=prompt)  # type: ignore[typeddict-item]
        elif "prompt_token_ids" in prompt:
            return ParsedTokensPrompt(
                type="tokens", content=prompt)  # type: ignore[typeddict-item]
        elif "prompt" in prompt:
            return ParsedTextPrompt(type="text", content=prompt)
    raise TypeError(
        "inputs must be a string, TextPrompt, TokensPrompt, or EmbedsPrompt")


def is_explicit_encoder_decoder_prompt(
    prompt: PromptType, ) -> TypeIs[ExplicitEncoderDecoderPrompt]:
    return isinstance(prompt, dict) and "encoder_prompt" in prompt


def split_enc_dec_inputs(
    inputs: ProcessorInputs,
) -> tuple[Optional[SingletonInputs], SingletonInputs]:
    if "encoder" in inputs and "decoder" in inputs:
        # NOTE: This passes pyright but not mypy
        return (
            inputs["encoder"],  # type: ignore[typeddict-item]
            inputs["decoder"],  # type: ignore[typeddict-item]
        )

    return None, inputs


class PromptComponents(NamedTuple):
    text: Optional[str] = None
    token_ids: Optional[list[int]] = None
    embeds: Optional["torch.Tensor"] = None


def get_prompt_components(prompt: PromptType) -> PromptComponents:
    if isinstance(prompt, str):
        return PromptComponents(text=prompt)

    if (encoder_prompt := prompt.get("encoder_prompt")):
        return get_prompt_components(encoder_prompt)  # type: ignore[arg-type]

    return PromptComponents(
        text=prompt.get("prompt"),  # type: ignore[arg-type]
        token_ids=prompt.get("prompt_token_ids"),  # type: ignore[arg-type]
        embeds=prompt.get("prompt_embeds"),
    )
