(fix) litellm.text_completion raises a non-blocking error on simple usage (#6546)

* unit test test_huggingface_text_completion_logprobs

* fix return TextCompletionHandler convert_chat_to_text_completion

* fix hf rest api

* fix test_huggingface_text_completion_logprobs

* fix linting errors

* fix importLiteLLMResponseObjectHandler

* fix test for LiteLLMResponseObjectHandler

* fix test text completion

litellm/llms/huggingface_restapi.py

@@ -15,6 +15,7 @@ import litellm
 from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
 from litellm.secret_managers.main import get_secret_str
 from litellm.types.completion import ChatCompletionMessageToolCallParam
+from litellm.types.utils import Logprobs as TextCompletionLogprobs
 from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
 
 from .base import BaseLLM
@@ -1183,3 +1184,73 @@ class Huggingface(BaseLLM):
             input=input,
             encoding=encoding,
         )
+
+    def _transform_logprobs(
+        self, hf_response: Optional[List]
+    ) -> Optional[TextCompletionLogprobs]:
+        """
+        Transform Hugging Face logprobs to OpenAI.Completion() format
+        """
+        if hf_response is None:
+            return None
+
+        # Initialize an empty list for the transformed logprobs
+        _logprob: TextCompletionLogprobs = TextCompletionLogprobs(
+            text_offset=[],
+            token_logprobs=[],
+            tokens=[],
+            top_logprobs=[],
+        )
+
+        # For each Hugging Face response, transform the logprobs
+        for response in hf_response:
+            # Extract the relevant information from the response
+            response_details = response["details"]
+            top_tokens = response_details.get("top_tokens", {})
+
+            for i, token in enumerate(response_details["prefill"]):
+                # Extract the text of the token
+                token_text = token["text"]
+
+                # Extract the logprob of the token
+                token_logprob = token["logprob"]
+
+                # Add the token information to the 'token_info' list
+                _logprob.tokens.append(token_text)
+                _logprob.token_logprobs.append(token_logprob)
+
+                # stub this to work with llm eval harness
+                top_alt_tokens = {"": -1.0, "": -2.0, "": -3.0}  # noqa: F601
+                _logprob.top_logprobs.append(top_alt_tokens)
+
+            # For each element in the 'tokens' list, extract the relevant information
+            for i, token in enumerate(response_details["tokens"]):
+                # Extract the text of the token
+                token_text = token["text"]
+
+                # Extract the logprob of the token
+                token_logprob = token["logprob"]
+
+                top_alt_tokens = {}
+                temp_top_logprobs = []
+                if top_tokens != {}:
+                    temp_top_logprobs = top_tokens[i]
+
+                # top_alt_tokens should look like this: { "alternative_1": -1, "alternative_2": -2, "alternative_3": -3 }
+                for elem in temp_top_logprobs:
+                    text = elem["text"]
+                    logprob = elem["logprob"]
+                    top_alt_tokens[text] = logprob
+
+                # Add the token information to the 'token_info' list
+                _logprob.tokens.append(token_text)
+                _logprob.token_logprobs.append(token_logprob)
+                _logprob.top_logprobs.append(top_alt_tokens)
+
+                # Add the text offset of the token
+                # This is computed as the sum of the lengths of all previous tokens
+                _logprob.text_offset.append(
+                    sum(len(t["text"]) for t in response_details["tokens"][:i])
+                )
+
+        return _logprob