venv/lib/python3.11/site-packages/huggingface_hub/inference/_providers/together.py

import base64
from abc import ABC
from typing import Any, Dict, Optional, Union

from huggingface_hub.hf_api import InferenceProviderMapping
from huggingface_hub.inference._common import RequestParameters, _as_dict
from huggingface_hub.inference._providers._common import (
    BaseConversationalTask,
    BaseTextGenerationTask,
    TaskProviderHelper,
    filter_none,
)


_PROVIDER = "together"
_BASE_URL = "https://api.together.xyz"


class TogetherTask(TaskProviderHelper, ABC):
    """Base class for Together API tasks."""

    def __init__(self, task: str):
        super().__init__(provider=_PROVIDER, base_url=_BASE_URL, task=task)

    def _prepare_route(self, mapped_model: str, api_key: str) -> str:
        if self.task == "text-to-image":
            return "/v1/images/generations"
        elif self.task == "conversational":
            return "/v1/chat/completions"
        elif self.task == "text-generation":
            return "/v1/completions"
        raise ValueError(f"Unsupported task '{self.task}' for Together API.")


class TogetherTextGenerationTask(BaseTextGenerationTask):
    def __init__(self):
        super().__init__(provider=_PROVIDER, base_url=_BASE_URL)

    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
        output = _as_dict(response)["choices"][0]
        return {
            "generated_text": output["text"],
            "details": {
                "finish_reason": output.get("finish_reason"),
                "seed": output.get("seed"),
            },
        }


class TogetherConversationalTask(BaseConversationalTask):
    def __init__(self):
        super().__init__(provider=_PROVIDER, base_url=_BASE_URL)


class TogetherTextToImageTask(TogetherTask):
    def __init__(self):
        super().__init__("text-to-image")

    def _prepare_payload_as_dict(
        self, inputs: Any, parameters: Dict, provider_mapping_info: InferenceProviderMapping
    ) -> Optional[Dict]:
        mapped_model = provider_mapping_info.provider_id
        parameters = filter_none(parameters)
        if "num_inference_steps" in parameters:
            parameters["steps"] = parameters.pop("num_inference_steps")
        if "guidance_scale" in parameters:
            parameters["guidance"] = parameters.pop("guidance_scale")

        return {"prompt": inputs, "response_format": "base64", **parameters, "model": mapped_model}

    def get_response(self, response: Union[bytes, Dict], request_params: Optional[RequestParameters] = None) -> Any:
        response_dict = _as_dict(response)
        return base64.b64decode(response_dict["data"][0]["b64_json"])