"""
Pydantic schemas for Inference API
"""
from __future__ import annotations

import time
import uuid
from typing import Literal, Optional, List

from pydantic import BaseModel, Field


class LoadRequest(BaseModel):
    """Request to load a model for inference"""
    model_path: str = Field(..., description="Model identifier or local path")
    hf_token: Optional[str] = Field(None, description="HuggingFace token for gated models")
    max_seq_length: int = Field(2048, ge=128, le=32768, description="Maximum sequence length")
    load_in_4bit: bool = Field(True, description="Load model in 4-bit quantization")
    is_lora: bool = Field(False, description="Whether this is a LoRA adapter")


class UnloadRequest(BaseModel):
    """Request to unload a model"""
    model_path: str = Field(..., description="Model identifier to unload")


class GenerateRequest(BaseModel):
    """Request for text generation"""
    messages: List[dict] = Field(..., description="Chat messages in OpenAI format")
    system_prompt: str = Field("You are a helpful AI assistant.", description="System prompt")
    temperature: float = Field(0.7, ge=0.0, le=2.0, description="Sampling temperature")
    top_p: float = Field(0.9, ge=0.0, le=1.0, description="Top-p sampling")
    top_k: int = Field(40, ge=1, le=100, description="Top-k sampling")
    max_new_tokens: int = Field(512, ge=1, le=4096, description="Maximum tokens to generate")
    repetition_penalty: float = Field(1.1, ge=1.0, le=2.0, description="Repetition penalty")
    image_base64: Optional[str] = Field(None, description="Base64 encoded image for vision models")


class LoadResponse(BaseModel):
    """Response after loading a model"""
    status: str = Field(..., description="Load status")
    model: str = Field(..., description="Model identifier")
    display_name: str = Field(..., description="Display name of the model")
    is_vision: bool = Field(False, description="Whether model is a vision model")
    is_lora: bool = Field(False, description="Whether model is a LoRA adapter")


class UnloadResponse(BaseModel):
    """Response after unloading a model"""
    status: str = Field(..., description="Unload status")
    model: str = Field(..., description="Model identifier that was unloaded")


class InferenceStatusResponse(BaseModel):
    """Current inference backend status"""
    active_model: Optional[str] = Field(None, description="Currently active model identifier")
    is_vision: bool = Field(False, description="Whether the active model is a vision model")
    loading: List[str] = Field(default_factory=list, description="Models currently being loaded")
    loaded: List[str] = Field(default_factory=list, description="Models currently loaded")


# =====================================================================
# OpenAI-Compatible Chat Completions Models
# =====================================================================


class ChatMessage(BaseModel):
    """A single message in the conversation."""
    role: Literal["system", "user", "assistant"] = Field(..., description="Message role")
    content: str = Field(..., description="Message content")


class ChatCompletionRequest(BaseModel):
    """
    OpenAI-compatible chat completion request.

    Extensions (non-OpenAI fields) are marked with 'x-unsloth'.
    """
    model: str = Field("default", description="Model identifier (informational; the active model is used)")
    messages: list[ChatMessage] = Field(..., description="Conversation messages")
    stream: bool = Field(True, description="Whether to stream the response via SSE")
    temperature: float = Field(0.7, ge=0.0, le=2.0)
    top_p: float = Field(0.9, ge=0.0, le=1.0)
    max_tokens: Optional[int] = Field(512, ge=1, le=4096, description="Maximum tokens to generate")

    # ── Unsloth extensions (ignored by standard OpenAI clients) ──
    top_k: int = Field(40, ge=1, le=100, description="[x-unsloth] Top-k sampling")
    repetition_penalty: float = Field(1.1, ge=1.0, le=2.0, description="[x-unsloth] Repetition penalty")
    image_base64: Optional[str] = Field(None, description="[x-unsloth] Base64-encoded image for vision models")


# ── Streaming response chunks ────────────────────────────────────


class ChoiceDelta(BaseModel):
    """Delta content for a streaming chunk."""
    role: Optional[str] = None
    content: Optional[str] = None


class ChunkChoice(BaseModel):
    """A single choice in a streaming chunk."""
    index: int = 0
    delta: ChoiceDelta
    finish_reason: Optional[Literal["stop", "length"]] = None


class ChatCompletionChunk(BaseModel):
    """A single SSE chunk in OpenAI streaming format."""
    id: str = Field(default_factory=lambda: f"chatcmpl-{uuid.uuid4().hex[:12]}")
    object: Literal["chat.completion.chunk"] = "chat.completion.chunk"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str = "default"
    choices: list[ChunkChoice]


# ── Non-streaming response ───────────────────────────────────────


class CompletionMessage(BaseModel):
    """The assistant's complete response message."""
    role: Literal["assistant"] = "assistant"
    content: str


class CompletionChoice(BaseModel):
    """A single choice in a non-streaming response."""
    index: int = 0
    message: CompletionMessage
    finish_reason: Literal["stop", "length"] = "stop"


class CompletionUsage(BaseModel):
    """Token usage statistics (approximate)."""
    prompt_tokens: int = 0
    completion_tokens: int = 0
    total_tokens: int = 0


class ChatCompletion(BaseModel):
    """Non-streaming chat completion response."""
    id: str = Field(default_factory=lambda: f"chatcmpl-{uuid.uuid4().hex[:12]}")
    object: Literal["chat.completion"] = "chat.completion"
    created: int = Field(default_factory=lambda: int(time.time()))
    model: str = "default"
    choices: list[CompletionChoice]
    usage: CompletionUsage = Field(default_factory=CompletionUsage)