two version of R2R are here HEAD master

1 files changed, 137 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/litellm/llms/petals/completion/transformation.py b/.venv/lib/python3.12/site-packages/litellm/llms/petals/completion/transformation.py
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+++ b/.venv/lib/python3.12/site-packages/litellm/llms/petals/completion/transformation.py
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+from typing import Any, List, Optional, Union
+
+from httpx import Headers, Response
+
+import litellm
+from litellm.llms.base_llm.chat.transformation import (
+    BaseConfig,
+    BaseLLMException,
+    LiteLLMLoggingObj,
+)
+from litellm.types.llms.openai import AllMessageValues
+from litellm.types.utils import ModelResponse
+
+from ..common_utils import PetalsError
+
+
+class PetalsConfig(BaseConfig):
+    """
+    Reference: https://github.com/petals-infra/chat.petals.dev#post-apiv1generate
+    The `PetalsConfig` class encapsulates the configuration for the Petals API. The properties of this class are described below:
+
+    - `max_length` (integer): This represents the maximum length of the generated text (including the prefix) in tokens.
+
+    - `max_new_tokens` (integer): This represents the maximum number of newly generated tokens (excluding the prefix).
+
+    The generation parameters are compatible with `.generate()` from Hugging Face's Transformers library:
+
+    - `do_sample` (boolean, optional): If set to 0 (default), the API runs greedy generation. If set to 1, the API performs sampling using the parameters below:
+
+    - `temperature` (float, optional): This value sets the temperature for sampling.
+
+    - `top_k` (integer, optional): This value sets the limit for top-k sampling.
+
+    - `top_p` (float, optional): This value sets the limit for top-p (nucleus) sampling.
+
+    - `repetition_penalty` (float, optional): This helps apply the repetition penalty during text generation, as discussed in this paper.
+    """
+
+    max_length: Optional[int] = None
+    max_new_tokens: Optional[int] = (
+        litellm.max_tokens
+    )  # petals requires max tokens to be set
+    do_sample: Optional[bool] = None
+    temperature: Optional[float] = None
+    top_k: Optional[int] = None
+    top_p: Optional[float] = None
+    repetition_penalty: Optional[float] = None
+
+    def __init__(
+        self,
+        max_length: Optional[int] = None,
+        max_new_tokens: Optional[
+            int
+        ] = litellm.max_tokens,  # petals requires max tokens to be set
+        do_sample: Optional[bool] = None,
+        temperature: Optional[float] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        repetition_penalty: Optional[float] = None,
+    ) -> None:
+        locals_ = locals().copy()
+        for key, value in locals_.items():
+            if key != "self" and value is not None:
+                setattr(self.__class__, key, value)
+
+    @classmethod
+    def get_config(cls):
+        return super().get_config()
+
+    def get_error_class(
+        self, error_message: str, status_code: int, headers: Union[dict, Headers]
+    ) -> BaseLLMException:
+        return PetalsError(
+            status_code=status_code, message=error_message, headers=headers
+        )
+
+    def get_supported_openai_params(self, model: str) -> List:
+        return ["max_tokens", "temperature", "top_p", "stream"]
+
+    def map_openai_params(
+        self,
+        non_default_params: dict,
+        optional_params: dict,
+        model: str,
+        drop_params: bool,
+    ) -> dict:
+        for param, value in non_default_params.items():
+            if param == "max_tokens":
+                optional_params["max_new_tokens"] = value
+            if param == "temperature":
+                optional_params["temperature"] = value
+            if param == "top_p":
+                optional_params["top_p"] = value
+            if param == "stream":
+                optional_params["stream"] = value
+        return optional_params
+
+    def transform_request(
+        self,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        headers: dict,
+    ) -> dict:
+        raise NotImplementedError(
+            "Petals transformation currently done in handler.py. [TODO] Move to the transformation.py"
+        )
+
+    def transform_response(
+        self,
+        model: str,
+        raw_response: Response,
+        model_response: ModelResponse,
+        logging_obj: LiteLLMLoggingObj,
+        request_data: dict,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        litellm_params: dict,
+        encoding: Any,
+        api_key: Optional[str] = None,
+        json_mode: Optional[bool] = None,
+    ) -> ModelResponse:
+        raise NotImplementedError(
+            "Petals transformation currently done in handler.py. [TODO] Move to the transformation.py"
+        )
+
+    def validate_environment(
+        self,
+        headers: dict,
+        model: str,
+        messages: List[AllMessageValues],
+        optional_params: dict,
+        api_key: Optional[str] = None,
+        api_base: Optional[str] = None,
+    ) -> dict:
+        return {}