(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

tests/llm_translation/test_text_completion.py

@@ -0,0 +1,141 @@
+import json
+import os
+import sys
+from datetime import datetime
+
+sys.path.insert(
+    0, os.path.abspath("../..")
+)  # Adds the parent directory to the system path
+
+import litellm
+import pytest
+
+from litellm.utils import (
+    LiteLLMResponseObjectHandler,
+)
+
+
+from datetime import timedelta
+
+from litellm.types.utils import (
+    ModelResponse,
+    TextCompletionResponse,
+    TextChoices,
+    Logprobs as TextCompletionLogprobs,
+    Usage,
+)
+
+
+def test_convert_chat_to_text_completion():
+    """Test converting chat completion to text completion"""
+    chat_response = ModelResponse(
+        id="chat123",
+        created=1234567890,
+        model="gpt-3.5-turbo",
+        choices=[
+            {
+                "index": 0,
+                "message": {"content": "Hello, world!"},
+                "finish_reason": "stop",
+            }
+        ],
+        usage={"total_tokens": 10, "completion_tokens": 10},
+        _hidden_params={"api_key": "test"},
+    )
+
+    text_completion = TextCompletionResponse()
+    result = LiteLLMResponseObjectHandler.convert_chat_to_text_completion(
+        response=chat_response, text_completion_response=text_completion
+    )
+
+    assert isinstance(result, TextCompletionResponse)
+    assert result.id == "chat123"
+    assert result.object == "text_completion"
+    assert result.created == 1234567890
+    assert result.model == "gpt-3.5-turbo"
+    assert result.choices[0].text == "Hello, world!"
+    assert result.choices[0].finish_reason == "stop"
+    assert result.usage == Usage(
+        completion_tokens=10,
+        prompt_tokens=0,
+        total_tokens=10,
+        completion_tokens_details=None,
+        prompt_tokens_details=None,
+    )
+
+
+def test_convert_provider_response_logprobs():
+    """Test converting provider logprobs to text completion logprobs"""
+    response = ModelResponse(
+        id="test123",
+        _hidden_params={
+            "original_response": {
+                "details": {"tokens": [{"text": "hello", "logprob": -1.0}]}
+            }
+        },
+    )
+
+    result = LiteLLMResponseObjectHandler._convert_provider_response_logprobs_to_text_completion_logprobs(
+        response=response, custom_llm_provider="huggingface"
+    )
+
+    # Note: The actual assertion here depends on the implementation of
+    # litellm.huggingface._transform_logprobs, but we can at least test the function call
+    assert (
+        result is not None or result is None
+    )  # Will depend on the actual implementation
+
+
+def test_convert_provider_response_logprobs_non_huggingface():
+    """Test converting provider logprobs for non-huggingface provider"""
+    response = ModelResponse(id="test123", _hidden_params={})
+
+    result = LiteLLMResponseObjectHandler._convert_provider_response_logprobs_to_text_completion_logprobs(
+        response=response, custom_llm_provider="openai"
+    )
+
+    assert result is None
+
+
+def test_convert_chat_to_text_completion_multiple_choices():
+    """Test converting chat completion to text completion with multiple choices"""
+    chat_response = ModelResponse(
+        id="chat456",
+        created=1234567890,
+        model="gpt-3.5-turbo",
+        choices=[
+            {
+                "index": 0,
+                "message": {"content": "First response"},
+                "finish_reason": "stop",
+            },
+            {
+                "index": 1,
+                "message": {"content": "Second response"},
+                "finish_reason": "length",
+            },
+        ],
+        usage={"total_tokens": 20},
+        _hidden_params={"api_key": "test"},
+    )
+
+    text_completion = TextCompletionResponse()
+    result = LiteLLMResponseObjectHandler.convert_chat_to_text_completion(
+        response=chat_response, text_completion_response=text_completion
+    )
+
+    assert isinstance(result, TextCompletionResponse)
+    assert result.id == "chat456"
+    assert result.object == "text_completion"
+    assert len(result.choices) == 2
+    assert result.choices[0].text == "First response"
+    assert result.choices[0].finish_reason == "stop"
+    assert result.choices[1].text == "Second response"
+    assert result.choices[1].finish_reason == "length"
+    assert result.usage == Usage(
+        completion_tokens=0,
+        prompt_tokens=0,
+        total_tokens=20,
+        completion_tokens_details=None,
+        prompt_tokens_details=None,
+    )