# SPDX-License-Identifier: AGPL-3.0-only
# Copyright 2026-present the Unsloth AI Inc. team. All rights reserved. See /studio/LICENSE.AGPL-3.0

"""
Pydantic schemas for Inference API
"""

from __future__ import annotations

import time
import uuid
from typing import Annotated, Any, Dict, Literal, Optional, List, Union

from pydantic import BaseModel, Discriminator, Field, Tag, model_validator


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(
        0,
        ge = 0,
        le = 1048576,
        description = "Maximum sequence length (0 = model default for GGUF)",
    )
    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")
    gguf_variant: Optional[str] = Field(
        None, description = "GGUF quantization variant (e.g. 'Q4_K_M')"
    )
    trust_remote_code: bool = Field(
        False,
        description = "Allow loading models with custom code (e.g. NVIDIA Nemotron). Only enable for repos you trust.",
    )
    chat_template_override: Optional[str] = Field(
        None,
        description = "Custom Jinja2 chat template to use instead of the model's default",
    )
    cache_type_kv: Optional[str] = Field(
        None,
        description = "KV cache data type for both K and V (e.g. 'f16', 'bf16', 'q8_0', 'q4_1', 'q5_1')",
    )
    gpu_ids: Optional[List[int]] = Field(
        None,
        description = "Physical GPU indices to use, for example [0, 1]. Omit or pass [] to use automatic selection. Explicit gpu_ids are unsupported when the parent CUDA_VISIBLE_DEVICES uses UUID/MIG entries. Not supported for GGUF models.",
    )
    speculative_type: Optional[str] = Field(
        None,
        description = "Speculative decoding mode for GGUF models (e.g. 'ngram-simple', 'ngram-mod'). Ignored for non-GGUF and vision models.",
    )


class UnloadRequest(BaseModel):
    """Request to unload a model"""

    model_path: str = Field(..., description = "Model identifier to unload")


class ValidateModelRequest(BaseModel):
    """
    Lightweight validation request to check whether a model identifier
    *can be resolved* into a ModelConfig.

    This does NOT actually load weights into GPU memory.
    """

    model_path: str = Field(..., description = "Model identifier or local path")
    hf_token: Optional[str] = Field(
        None, description = "HuggingFace token for gated models"
    )
    gguf_variant: Optional[str] = Field(
        None, description = "GGUF quantization variant (e.g. 'Q4_K_M')"
    )


class ValidateModelResponse(BaseModel):
    """
    Result of model validation.

    valid == True means ModelConfig.from_identifier() succeeded and basic
    introspection (GGUF / LoRA / vision flags) is available.
    """

    valid: bool = Field(..., description = "Whether the model identifier looks valid")
    message: str = Field(..., description = "Human-readable validation message")
    identifier: Optional[str] = Field(None, description = "Resolved model identifier")
    display_name: Optional[str] = Field(
        None, description = "Display name derived from identifier"
    )
    is_gguf: bool = Field(False, description = "Whether this is a GGUF model (llama.cpp)")
    is_lora: bool = Field(False, description = "Whether this is a LoRA adapter")
    is_vision: bool = Field(False, description = "Whether this is a vision-capable model")
    requires_trust_remote_code: bool = Field(
        False,
        description = "Whether the model defaults require trust_remote_code to be enabled for loading.",
    )


class GenerateRequest(BaseModel):
    """Request for text generation (legacy /generate/stream endpoint)"""

    messages: List[dict] = Field(..., description = "Chat messages in OpenAI format")
    system_prompt: str = Field("", description = "System prompt")
    temperature: float = Field(0.6, ge = 0.0, le = 2.0, description = "Sampling temperature")
    top_p: float = Field(0.95, ge = 0.0, le = 1.0, description = "Top-p sampling")
    top_k: int = Field(20, ge = -1, le = 100, description = "Top-k sampling")
    max_new_tokens: int = Field(
        2048, ge = 1, le = 4096, description = "Maximum tokens to generate"
    )
    repetition_penalty: float = Field(
        1.0, ge = 1.0, le = 2.0, description = "Repetition penalty"
    )
    presence_penalty: float = Field(0.0, ge = 0.0, le = 2.0, description = "Presence 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")
    is_gguf: bool = Field(
        False, description = "Whether model is a GGUF model (llama.cpp)"
    )
    is_audio: bool = Field(False, description = "Whether model is a TTS audio model")
    audio_type: Optional[str] = Field(
        None, description = "Audio codec type: snac, csm, bicodec, dac"
    )
    has_audio_input: bool = Field(
        False, description = "Whether model accepts audio input (ASR)"
    )
    inference: dict = Field(
        ..., description = "Inference parameters (temperature, top_p, top_k, min_p)"
    )
    requires_trust_remote_code: bool = Field(
        False,
        description = "Whether the model defaults require trust_remote_code to be enabled for loading.",
    )
    context_length: Optional[int] = Field(
        None, description = "Model's native context length (from GGUF metadata)"
    )
    max_context_length: Optional[int] = Field(
        None, description = "Maximum context length currently available on this hardware"
    )
    native_context_length: Optional[int] = Field(
        None,
        description = "Model's native context length from GGUF metadata (not capped by VRAM)",
    )
    supports_reasoning: bool = Field(
        False,
        description = "Whether model supports thinking/reasoning mode (enable_thinking or reasoning_effort)",
    )
    reasoning_style: Literal["enable_thinking", "reasoning_effort"] = Field(
        "enable_thinking",
        description = "Reasoning control style: 'enable_thinking' (boolean) or 'reasoning_effort' (low|medium|high)",
    )
    reasoning_always_on: bool = Field(
        False,
        description = "Whether reasoning is always on (hardcoded <think> tags, not toggleable)",
    )
    supports_preserve_thinking: bool = Field(
        False,
        description = "Whether the template understands the optional preserve_thinking kwarg (Qwen3.6-style)",
    )
    supports_tools: bool = Field(
        False,
        description = "Whether model supports tool calling (web search, etc.)",
    )
    cache_type_kv: Optional[str] = Field(
        None,
        description = "KV cache data type for K and V (e.g. 'f16', 'bf16', 'q8_0')",
    )
    chat_template: Optional[str] = Field(
        None,
        description = "Jinja2 chat template string (from GGUF metadata or tokenizer)",
    )
    speculative_type: Optional[str] = Field(
        None,
        description = "Active speculative decoding mode (e.g. 'ngram-simple', 'ngram-mod'), or None if disabled",
    )


class UnloadResponse(BaseModel):
    """Response after unloading a model"""

    status: str = Field(..., description = "Unload status")
    model: str = Field(..., description = "Model identifier that was unloaded")


class LoadProgressResponse(BaseModel):
    """Progress of the active GGUF load, sampled on demand.

    Used by the UI to show a real progress bar during the
    post-download warmup window (mmap + CUDA upload), rather than a
    generic "Starting model..." spinner that freezes for minutes on
    large MoE models.
    """

    phase: Optional[str] = Field(
        None,
        description = (
            "Load phase: 'mmap' (weights paging into RAM via mmap), "
            "'ready' (llama-server reported healthy), or null when no "
            "load is in flight."
        ),
    )
    bytes_loaded: int = Field(
        0,
        description = (
            "Bytes of the model already resident in the llama-server "
            "process (VmRSS on Linux)."
        ),
    )
    bytes_total: int = Field(
        0,
        description = "Total bytes across all GGUF shards for the active model.",
    )
    fraction: float = Field(
        0.0, description = "bytes_loaded / bytes_total, clamped to 0..1."
    )


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"
    )
    is_gguf: bool = Field(
        False, description = "Whether the active model is a GGUF model (llama.cpp)"
    )
    gguf_variant: Optional[str] = Field(
        None, description = "GGUF quantization variant (e.g. Q4_K_M)"
    )
    is_audio: bool = Field(
        False, description = "Whether the active model is a TTS audio model"
    )
    audio_type: Optional[str] = Field(
        None, description = "Audio codec type: snac, csm, bicodec, dac"
    )
    has_audio_input: bool = Field(
        False, description = "Whether model accepts audio input (ASR)"
    )
    loading: List[str] = Field(
        default_factory = list, description = "Models currently being loaded"
    )
    loaded: List[str] = Field(
        default_factory = list, description = "Models currently loaded"
    )
    inference: Optional[Dict[str, Any]] = Field(
        None, description = "Recommended inference parameters for the active model"
    )
    requires_trust_remote_code: bool = Field(
        False,
        description = "Whether the active model requires trust_remote_code to be enabled for loading.",
    )
    supports_reasoning: bool = Field(
        False, description = "Whether the active model supports reasoning/thinking mode"
    )
    reasoning_style: Literal["enable_thinking", "reasoning_effort"] = Field(
        "enable_thinking",
        description = "Reasoning control style: 'enable_thinking' (boolean) or 'reasoning_effort' (low|medium|high)",
    )
    reasoning_always_on: bool = Field(
        False, description = "Whether reasoning is always on (not toggleable)"
    )
    supports_preserve_thinking: bool = Field(
        False,
        description = "Whether the active model's template understands the optional preserve_thinking kwarg",
    )
    supports_tools: bool = Field(
        False, description = "Whether the active model supports tool calling"
    )
    context_length: Optional[int] = Field(
        None, description = "Context length of the active model"
    )
    max_context_length: Optional[int] = Field(
        None,
        description = "Maximum context length currently available for the active model",
    )
    native_context_length: Optional[int] = Field(
        None,
        description = "Model's native context length from GGUF metadata (not capped by VRAM)",
    )
    speculative_type: Optional[str] = Field(
        None,
        description = "Active speculative decoding mode (e.g. 'ngram-simple', 'ngram-mod'), or None if disabled",
    )


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


# ── Multimodal content parts (OpenAI vision format) ──────────────


class TextContentPart(BaseModel):
    """Text content part in a multimodal message."""

    type: Literal["text"]
    text: str


class ImageUrl(BaseModel):
    """Image URL object — supports data URIs and remote URLs."""

    url: str = Field(..., description = "data:image/png;base64,... or https://...")
    detail: Optional[Literal["auto", "low", "high"]] = "auto"


class ImageContentPart(BaseModel):
    """Image content part in a multimodal message."""

    type: Literal["image_url"]
    image_url: ImageUrl


def _content_part_discriminator(v):
    if isinstance(v, dict):
        return v.get("type")
    return getattr(v, "type", None)


ContentPart = Annotated[
    Union[
        Annotated[TextContentPart, Tag("text")],
        Annotated[ImageContentPart, Tag("image_url")],
    ],
    Discriminator(_content_part_discriminator),
]
"""Union type for multimodal content parts, discriminated by the 'type' field."""


# ── Messages ─────────────────────────────────────────────────────


class ChatMessage(BaseModel):
    """
    A single message in the conversation.

    ``content`` may be a plain string (text-only) or a list of
    content parts for multimodal messages (OpenAI vision format).
    Assistant messages that only contain tool calls may set ``content``
    to ``None`` with ``tool_calls`` populated. ``role="tool"`` messages
    carry the result of a client-executed tool call and require
    ``tool_call_id`` per the OpenAI spec.
    """

    role: Literal["system", "user", "assistant", "tool"] = Field(
        ..., description = "Message role"
    )
    content: Optional[Union[str, list[ContentPart]]] = Field(
        None, description = "Message content (string or multimodal parts)"
    )
    tool_call_id: Optional[str] = Field(
        None,
        description = "OpenAI tool-result messages: id of the tool call this result belongs to.",
    )
    tool_calls: Optional[list[dict]] = Field(
        None,
        description = "OpenAI assistant messages: structured tool calls the model decided to make.",
    )
    name: Optional[str] = Field(
        None,
        description = "OpenAI tool-result messages: name of the tool whose result this is.",
    )

    @model_validator(mode = "after")
    def _validate_role_shape(self) -> "ChatMessage":
        # Enforce the per-role OpenAI spec shape at the request boundary.
        # Without this, malformed messages (e.g. user entries with no
        # content, tool_calls on a user/system role, role="tool" without
        # tool_call_id) would be silently forwarded to llama-server via
        # the passthrough path, surfacing as opaque upstream errors or
        # broken tool-call reconciliation downstream.

        # Tool-call metadata must appear only on the appropriate role.
        if self.tool_calls is not None and self.role != "assistant":
            raise ValueError('"tool_calls" is only valid on role="assistant" messages.')
        if self.tool_call_id is not None and self.role != "tool":
            raise ValueError('"tool_call_id" is only valid on role="tool" messages.')
        if self.name is not None and self.role != "tool":
            raise ValueError('"name" is only valid on role="tool" messages.')

        # Per-role content requirements.
        if self.role == "tool":
            if not self.tool_call_id:
                raise ValueError(
                    'role="tool" messages require "tool_call_id" per the OpenAI spec.'
                )
            if not self.content:
                raise ValueError('role="tool" messages require non-empty "content".')
        elif self.role == "assistant":
            # Assistant messages may omit content when tool_calls is set.
            if not self.content and not self.tool_calls:
                raise ValueError(
                    'role="assistant" messages require either "content" or "tool_calls".'
                )
        else:  # "user" | "system"
            if not self.content:
                raise ValueError(
                    f'role="{self.role}" messages require non-empty "content".'
                )
        return self


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

    Extensions (non-OpenAI fields) are marked with 'x-unsloth'.
    """

    # Accept unknown fields defensively so future OpenAI fields (seed,
    # response_format, logprobs, frequency_penalty, etc.) don't get
    # silently dropped by Pydantic before route code runs. Mirrors
    # AnthropicMessagesRequest and ResponsesRequest.
    model_config = {"extra": "allow"}

    model: str = Field(
        "default",
        description = "Model identifier (informational; the active model is used)",
    )
    messages: list[ChatMessage] = Field(..., description = "Conversation messages")
    stream: bool = Field(
        False,
        description = (
            "Whether to stream the response via SSE. Default matches OpenAI's "
            "spec (`false`); opt into streaming by sending `stream: true`."
        ),
    )
    temperature: float = Field(0.6, ge = 0.0, le = 2.0)
    top_p: float = Field(0.95, ge = 0.0, le = 1.0)
    max_tokens: Optional[int] = Field(
        None, ge = 1, description = "Maximum tokens to generate (None = until EOS)"
    )
    presence_penalty: float = Field(0.0, ge = 0.0, le = 2.0, description = "Presence penalty")
    stop: Optional[Union[str, list[str]]] = Field(
        None,
        description = "OpenAI stop sequences: a single string or list of strings at which generation halts.",
    )
    tools: Optional[list[dict]] = Field(
        None,
        description = (
            "OpenAI function-tool definitions. When provided without `enable_tools=true`, "
            "Studio forwards the tools to the backend so the model returns structured "
            "tool_calls for the client to execute (standard OpenAI function calling)."
        ),
    )
    tool_choice: Optional[Union[str, dict]] = Field(
        None,
        description = (
            "OpenAI tool choice: 'auto' | 'required' | 'none' | "
            "{'type': 'function', 'function': {'name': ...}}"
        ),
    )

    # ── Unsloth extensions (ignored by standard OpenAI clients) ──
    top_k: int = Field(20, ge = -1, le = 100, description = "[x-unsloth] Top-k sampling")
    min_p: float = Field(
        0.01, ge = 0.0, le = 1.0, description = "[x-unsloth] Min-p sampling threshold"
    )
    repetition_penalty: float = Field(
        1.0, 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"
    )
    audio_base64: Optional[str] = Field(
        None, description = "[x-unsloth] Base64-encoded WAV for audio-input models (ASR)"
    )
    use_adapter: Optional[Union[bool, str]] = Field(
        None,
        description = (
            "[x-unsloth] Adapter control for compare mode. "
            "null = no change (default), "
            "false = disable adapters (base model), "
            "true = enable the current adapter, "
            "string = enable a specific adapter by name."
        ),
    )
    enable_thinking: Optional[bool] = Field(
        None,
        description = "[x-unsloth] Enable/disable thinking/reasoning mode for supported models",
    )
    reasoning_effort: Optional[Literal["low", "medium", "high"]] = Field(
        None,
        description = "[x-unsloth] Reasoning effort level ('low'|'medium'|'high') for Harmony-style reasoning models (e.g. gpt-oss). Overrides enable_thinking when the active model uses reasoning_effort style.",
    )
    preserve_thinking: Optional[bool] = Field(
        None,
        description = "[x-unsloth] When true, keep historical <think> blocks from past assistant turns in the prompt (Qwen3.6 templates). Independent of enable_thinking / reasoning_effort.",
    )
    enable_tools: Optional[bool] = Field(
        None,
        description = "[x-unsloth] Enable tool calling for supported models",
    )
    enabled_tools: Optional[list[str]] = Field(
        None,
        description = "[x-unsloth] List of enabled tool names (e.g. ['web_search', 'python', 'terminal']). If None, all tools are enabled.",
    )
    auto_heal_tool_calls: Optional[bool] = Field(
        True,
        description = "[x-unsloth] Auto-detect and fix malformed tool calls from model output.",
    )
    max_tool_calls_per_message: Optional[int] = Field(
        25,
        ge = 0,
        description = "[x-unsloth] Maximum number of tool call iterations per message (0 = disabled, 9999 = unlimited).",
    )
    tool_call_timeout: Optional[int] = Field(
        300,
        ge = 1,
        description = "[x-unsloth] Timeout in seconds for each tool call execution (9999 = no limit).",
    )
    session_id: Optional[str] = Field(
        None,
        description = "[x-unsloth] Session/thread ID for scoping tool execution sandbox.",
    )
    cancel_id: Optional[str] = Field(
        None,
        description = "[x-unsloth] Per-request cancellation token. Frontend sends a fresh UUID per run so /inference/cancel matches one specific generation.",
    )


# ── 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]
    usage: Optional[CompletionUsage] = None
    timings: Optional[dict] = None


# ── 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)


# =====================================================================
# OpenAI Responses API Models  (/v1/responses)
# =====================================================================


# ── Request models ──────────────────────────────────────────────


class ResponsesInputTextPart(BaseModel):
    """Text content part in a Responses API message (type=input_text)."""

    type: Literal["input_text"]
    text: str


class ResponsesInputImagePart(BaseModel):
    """Image content part in a Responses API message (type=input_image)."""

    type: Literal["input_image"]
    image_url: str = Field(..., description = "data:image/png;base64,... or https://...")
    detail: Optional[Literal["auto", "low", "high"]] = "auto"


class ResponsesOutputTextPart(BaseModel):
    """Assistant ``output_text`` content part replayed on subsequent turns.

    When a client (OpenAI Codex CLI, OpenAI Python SDK agents) loops on a
    stateless Responses endpoint, prior assistant messages are round-tripped
    as ``{"role":"assistant","content":[{"type":"output_text","text":...,
    "annotations":[],"logprobs":[]}]}``. We preserve the text and ignore
    the annotations/logprobs metadata when flattening into Chat Completions.
    """

    type: Literal["output_text"]
    text: str
    annotations: Optional[list] = None
    logprobs: Optional[list] = None

    model_config = {"extra": "allow"}


class ResponsesUnknownContentPart(BaseModel):
    """Catch-all for content-part types we don't model explicitly.

    Keeps validation green when a client sends newer part types (e.g.
    ``input_audio``, ``input_file``) we haven't mapped; these are silently
    skipped during normalisation rather than rejected with a 422.
    """

    type: str

    model_config = {"extra": "allow"}


ResponsesContentPart = Union[
    ResponsesInputTextPart,
    ResponsesInputImagePart,
    ResponsesOutputTextPart,
    ResponsesUnknownContentPart,
]


class ResponsesInputMessage(BaseModel):
    """A single message in the Responses API input array."""

    type: Optional[Literal["message"]] = None
    role: Literal["system", "user", "assistant", "developer"]
    content: Union[str, list[ResponsesContentPart]]

    # Codex (gpt-5.3-codex+) attaches a `phase` field ("commentary" |
    # "final_answer") to assistant messages and requires clients to preserve
    # it on subsequent turns. We accept and round-trip it; llama-server does
    # not care about it.
    model_config = {"extra": "allow"}


class ResponsesFunctionCallInputItem(BaseModel):
    """A prior assistant function_call being replayed in a multi-turn Responses input.

    The Responses API represents tool calls as top-level input items (not
    nested inside assistant messages), correlated across turns by ``call_id``.
    """

    type: Literal["function_call"]
    id: Optional[str] = Field(
        None, description = "Item id assigned by the server (e.g. fc_...)"
    )
    call_id: str = Field(
        ...,
        description = "Correlation id matching a function_call_output on the next turn.",
    )
    name: str
    arguments: str = Field(
        ..., description = "JSON string of the arguments the model produced."
    )
    status: Optional[Literal["in_progress", "completed", "incomplete"]] = None


class ResponsesFunctionCallOutputInputItem(BaseModel):
    """A tool result supplied by the client for a prior function_call.

    Replaces Chat Completions' ``role="tool"`` message. Correlated to the
    originating call by ``call_id``.
    """

    type: Literal["function_call_output"]
    id: Optional[str] = None
    call_id: str
    output: Union[str, list] = Field(
        ..., description = "String or content-array result of the tool call."
    )
    status: Optional[Literal["in_progress", "completed", "incomplete"]] = None


class ResponsesUnknownInputItem(BaseModel):
    """Catch-all for Responses input item types we don't model explicitly.

    Covers ``reasoning`` items (replayed from prior o-series / gpt-5 turns)
    and any future item types the client may send. These items are dropped
    during normalisation — llama-server-backed GGUFs cannot consume them —
    but keeping them in the request-model union stops unrelated turns from
    failing validation with a 422.
    """

    type: str

    model_config = {"extra": "allow"}


def _responses_input_item_discriminator(v: Any) -> str:
    """Route a Responses input item to the correct tagged variant.

    Pydantic's default smart-union matching fails when one variant in the
    union is tagged with a strict ``Literal`` (``function_call`` /
    ``function_call_output``) and the incoming dict uses a different
    ``type`` — the other variants' validation errors are hidden and the
    outer ``Union[str, list[...]]`` reports a misleading "Input should be a
    valid string" error. An explicit discriminator makes the routing
    deterministic and lets us fall through to the catch-all.
    """
    if isinstance(v, dict):
        t = v.get("type")
        r = v.get("role")
    else:
        t = getattr(v, "type", None)
        r = getattr(v, "role", None)
    if t == "function_call":
        return "function_call"
    if t == "function_call_output":
        return "function_call_output"
    if r is not None or t == "message":
        return "message"
    return "unknown"


ResponsesInputItem = Annotated[
    Union[
        Annotated[ResponsesInputMessage, Tag("message")],
        Annotated[ResponsesFunctionCallInputItem, Tag("function_call")],
        Annotated[ResponsesFunctionCallOutputInputItem, Tag("function_call_output")],
        Annotated[ResponsesUnknownInputItem, Tag("unknown")],
    ],
    Discriminator(_responses_input_item_discriminator),
]


class ResponsesFunctionTool(BaseModel):
    """Flat function-tool definition used by the Responses API request.

    Unlike Chat Completions (which nests ``{"name": ..., "parameters": ...}``
    inside a ``"function"`` key), the Responses API uses a flat shape with
    ``type``, ``name``, ``description``, ``parameters``, and ``strict`` at the
    top level of each tool entry.
    """

    type: Literal["function"]
    name: str
    description: Optional[str] = None
    parameters: Optional[dict] = None
    strict: Optional[bool] = None


class ResponsesRequest(BaseModel):
    """OpenAI Responses API request."""

    model: str = Field("default", description = "Model identifier")
    input: Union[str, list[ResponsesInputItem]] = Field(
        default = [],
        description = "Input text or list of messages / function_call / function_call_output items",
    )
    instructions: Optional[str] = Field(
        None, description = "System / developer instructions"
    )
    temperature: Optional[float] = Field(None, ge = 0.0, le = 2.0)
    top_p: Optional[float] = Field(None, ge = 0.0, le = 1.0)
    max_output_tokens: Optional[int] = Field(None, ge = 1)
    stream: bool = Field(False, description = "Whether to stream the response via SSE")

    # OpenAI function-calling fields — forwarded to llama-server via the
    # Chat Completions pass-through (see routes/inference.py). Typed as a
    # plain list so built-in tool shapes (``web_search``, ``file_search``,
    # ``mcp``, ...) round-trip without validation errors — the translator
    # picks out only ``type=="function"`` entries for forwarding.
    tools: Optional[list[dict]] = Field(
        None,
        description = (
            "Responses-shape function tool definitions. Entries with "
            '`type="function"` are translated to the Chat Completions nested '
            "shape before being forwarded to llama-server; other tool types "
            "(built-in web_search, file_search, mcp, ...) are accepted for SDK "
            "compatibility but ignored on the llama-server passthrough."
        ),
    )
    tool_choice: Optional[Any] = Field(
        None,
        description = (
            "'auto' | 'required' | 'none' | {'type': 'function', 'name': ...} — "
            "the Responses-shape forcing object is translated to the Chat "
            "Completions nested shape internally."
        ),
    )
    parallel_tool_calls: Optional[bool] = None

    previous_response_id: Optional[str] = None
    store: Optional[bool] = None
    metadata: Optional[dict] = None
    truncation: Optional[Any] = None
    user: Optional[str] = None
    text: Optional[Any] = None
    reasoning: Optional[Any] = None

    model_config = {"extra": "allow"}


# ── Response models ─────────────────────────────────────────────


class ResponsesOutputTextContent(BaseModel):
    """A text content block inside an output message."""

    type: Literal["output_text"] = "output_text"
    text: str
    annotations: list = Field(default_factory = list)


class ResponsesOutputMessage(BaseModel):
    """An output message in the Responses API response."""

    type: Literal["message"] = "message"
    id: str = Field(default_factory = lambda: f"msg_{uuid.uuid4().hex[:12]}")
    status: Literal["completed", "in_progress"] = "completed"
    role: Literal["assistant"] = "assistant"
    content: list[ResponsesOutputTextContent] = Field(default_factory = list)


class ResponsesOutputFunctionCall(BaseModel):
    """A function-call output item in the Responses API response.

    Unlike Chat Completions (which nests tool calls inside the assistant
    message), the Responses API emits each tool call as its own top-level
    ``output`` item so clients can correlate results via ``call_id`` on the
    next turn.
    """

    type: Literal["function_call"] = "function_call"
    id: str = Field(default_factory = lambda: f"fc_{uuid.uuid4().hex[:12]}")
    call_id: str
    name: str
    arguments: str = Field(
        ..., description = "JSON string of the arguments the model produced."
    )
    status: Literal["completed", "in_progress", "incomplete"] = "completed"


ResponsesOutputItem = Union[ResponsesOutputMessage, ResponsesOutputFunctionCall]


class ResponsesUsage(BaseModel):
    """Token usage for a Responses API response (input_tokens, not prompt_tokens)."""

    input_tokens: int = 0
    output_tokens: int = 0
    total_tokens: int = 0


class ResponsesResponse(BaseModel):
    """Top-level Responses API response object."""

    id: str = Field(default_factory = lambda: f"resp_{uuid.uuid4().hex[:12]}")
    object: Literal["response"] = "response"
    created_at: int = Field(default_factory = lambda: int(time.time()))
    status: Literal["completed", "in_progress", "failed"] = "completed"
    model: str = "default"
    output: list[ResponsesOutputItem] = Field(default_factory = list)
    usage: ResponsesUsage = Field(default_factory = ResponsesUsage)
    error: Optional[Any] = None
    incomplete_details: Optional[Any] = None
    instructions: Optional[str] = None
    metadata: dict = Field(default_factory = dict)
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    max_output_tokens: Optional[int] = None
    previous_response_id: Optional[str] = None
    text: Optional[Any] = None
    tool_choice: Optional[Any] = None
    tools: list = Field(default_factory = list)
    truncation: Optional[Any] = None


# =====================================================================
# Anthropic Messages API Models  (/v1/messages)
# =====================================================================


# ── Request models ─────────────────────────────────────────────


class AnthropicTextBlock(BaseModel):
    type: Literal["text"]
    text: str


class AnthropicImageSource(BaseModel):
    type: Literal["base64", "url"]
    media_type: Optional[str] = None
    data: Optional[str] = None
    url: Optional[str] = None


class AnthropicImageBlock(BaseModel):
    type: Literal["image"]
    source: AnthropicImageSource


class AnthropicToolUseBlock(BaseModel):
    type: Literal["tool_use"]
    id: str
    name: str
    input: dict


class AnthropicToolResultBlock(BaseModel):
    type: Literal["tool_result"]
    tool_use_id: str
    content: Union[str, list] = ""


AnthropicContentBlock = Union[
    AnthropicTextBlock,
    AnthropicImageBlock,
    AnthropicToolUseBlock,
    AnthropicToolResultBlock,
]


class AnthropicMessage(BaseModel):
    role: Literal["user", "assistant"]
    content: Union[str, list[AnthropicContentBlock]]


class AnthropicTool(BaseModel):
    name: str
    description: Optional[str] = None
    input_schema: dict


class AnthropicMessagesRequest(BaseModel):
    model: str = "default"
    max_tokens: Optional[int] = None
    messages: list[AnthropicMessage]
    system: Optional[Union[str, list]] = None
    tools: Optional[list[AnthropicTool]] = None
    tool_choice: Optional[Any] = None
    stream: bool = False
    temperature: Optional[float] = None
    top_p: Optional[float] = None
    top_k: Optional[int] = None
    stop_sequences: Optional[list[str]] = None
    metadata: Optional[dict] = None
    # [x-unsloth] extensions — mirror the OpenAI endpoint convenience fields
    min_p: Optional[float] = Field(
        None, ge = 0.0, le = 1.0, description = "[x-unsloth] Min-p sampling threshold"
    )
    repetition_penalty: Optional[float] = Field(
        None, ge = 1.0, le = 2.0, description = "[x-unsloth] Repetition penalty"
    )
    presence_penalty: Optional[float] = Field(
        None, ge = 0.0, le = 2.0, description = "[x-unsloth] Presence penalty"
    )
    enable_tools: Optional[bool] = None
    enabled_tools: Optional[list[str]] = None
    session_id: Optional[str] = None
    cancel_id: Optional[str] = None
    model_config = {"extra": "allow"}


# ── Response models ────────────────────────────────────────────


class AnthropicUsage(BaseModel):
    input_tokens: int = 0
    output_tokens: int = 0


class AnthropicResponseTextBlock(BaseModel):
    type: Literal["text"] = "text"
    text: str


class AnthropicResponseToolUseBlock(BaseModel):
    type: Literal["tool_use"] = "tool_use"
    id: str
    name: str
    input: dict


AnthropicResponseBlock = Union[
    AnthropicResponseTextBlock, AnthropicResponseToolUseBlock
]


class AnthropicMessagesResponse(BaseModel):
    id: str = Field(default_factory = lambda: f"msg_{uuid.uuid4().hex[:24]}")
    type: Literal["message"] = "message"
    role: Literal["assistant"] = "assistant"
    content: list[AnthropicResponseBlock] = Field(default_factory = list)
    model: str = "default"
    stop_reason: Optional[str] = None
    stop_sequence: Optional[str] = None
    usage: AnthropicUsage = Field(default_factory = AnthropicUsage)