chore: import upstream snapshot with attribution

test/utils/test_hf.py

@@ -0,0 +1,129 @@
+# SPDX-FileCopyrightText: 2022-present deepset GmbH <info@deepset.ai>
+#
+# SPDX-License-Identifier: Apache-2.0
+
+import pytest
+
+from haystack.dataclasses import ChatMessage, ChatRole, ImageContent, ReasoningContent, TextContent, ToolCall
+from haystack.dataclasses.chat_message import ToolCallResult
+from haystack.utils.hf import convert_message_to_hf_format
+
+
+def test_convert_message_to_hf_format():
+    message = ChatMessage.from_system("You are good assistant")
+    assert convert_message_to_hf_format(message) == {"role": "system", "content": "You are good assistant"}
+
+    message = ChatMessage.from_user("I have a question")
+    assert convert_message_to_hf_format(message) == {"role": "user", "content": "I have a question"}
+
+    message = ChatMessage.from_assistant(text="I have an answer", meta={"finish_reason": "stop"})
+    assert convert_message_to_hf_format(message) == {"role": "assistant", "content": "I have an answer"}
+
+    message = ChatMessage.from_assistant(
+        tool_calls=[ToolCall(id="123", tool_name="weather", arguments={"city": "Paris"})]
+    )
+    assert convert_message_to_hf_format(message) == {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {"id": "123", "type": "function", "function": {"name": "weather", "arguments": {"city": "Paris"}}}
+        ],
+    }
+
+    message = ChatMessage.from_assistant(tool_calls=[ToolCall(tool_name="weather", arguments={"city": "Paris"})])
+    assert convert_message_to_hf_format(message) == {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [{"type": "function", "function": {"name": "weather", "arguments": {"city": "Paris"}}}],
+    }
+
+    tool_result = '{"weather": "sunny", "temperature": "25"}'
+    message = ChatMessage.from_tool(
+        tool_result=tool_result, origin=ToolCall(id="123", tool_name="weather", arguments={"city": "Paris"})
+    )
+    assert convert_message_to_hf_format(message) == {"role": "tool", "content": tool_result, "tool_call_id": "123"}
+
+    message = ChatMessage.from_tool(
+        tool_result=tool_result, origin=ToolCall(tool_name="weather", arguments={"city": "Paris"})
+    )
+    assert convert_message_to_hf_format(message) == {"role": "tool", "content": tool_result}
+
+
+def test_convert_message_to_hf_invalid():
+    message = ChatMessage(_role=ChatRole.ASSISTANT, _content=[])
+    with pytest.raises(ValueError):
+        convert_message_to_hf_format(message)
+
+    message = ChatMessage(
+        _role=ChatRole.USER,
+        _content=[
+            TextContent(text="I have an answer"),
+            ToolCallResult(
+                result="result!",
+                origin=ToolCall(id="123", tool_name="weather", arguments={"city": "Paris"}),
+                error=None,  # type: ignore[arg-type]
+            ),
+        ],
+    )
+    with pytest.raises(ValueError):
+        convert_message_to_hf_format(message)
+
+
+def test_convert_message_to_hf_format_with_multiple_images(base64_image_string):
+    image1 = ImageContent(base64_image=base64_image_string)
+    image2 = ImageContent(base64_image=base64_image_string)
+    message = ChatMessage.from_user(content_parts=["Compare these images", image1, image2])
+
+    result = convert_message_to_hf_format(message)
+    expected = {
+        "role": "user",
+        "content": [
+            {"type": "text", "text": "Compare these images"},
+            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image_string}"}},
+            {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{base64_image_string}"}},
+        ],
+    }
+    assert result == expected
+
+
+def test_convert_message_to_hf_format_skips_reasoning_content():
+    """
+    Test that ReasoningContent is properly skipped during conversion.
+
+    ReasoningContent is for human transparency only and is not sent to the HuggingFace API
+    because the API (which follows OpenAI-compatible format) does not support reasoning
+    in input messages.
+    """
+    # Assistant message with text and reasoning
+    message = ChatMessage(
+        _role=ChatRole.ASSISTANT,
+        _content=[
+            TextContent(text="The answer is 42."),
+            ReasoningContent(reasoning_text="Let me think step by step..."),
+        ],
+    )
+    result = convert_message_to_hf_format(message)
+    # ReasoningContent should be skipped, only text should be in the output
+    assert result == {"role": "assistant", "content": "The answer is 42."}
+
+
+def test_convert_message_to_hf_format_with_tool_call_and_reasoning():
+    """
+    Test that ReasoningContent is skipped when message contains tool calls.
+    """
+    message = ChatMessage(
+        _role=ChatRole.ASSISTANT,
+        _content=[
+            ReasoningContent(reasoning_text="I need to check the weather."),
+            ToolCall(id="123", tool_name="weather", arguments={"city": "Paris"}),
+        ],
+    )
+    result = convert_message_to_hf_format(message)
+    # ReasoningContent should be skipped
+    assert result == {
+        "role": "assistant",
+        "content": "",
+        "tool_calls": [
+            {"id": "123", "type": "function", "function": {"name": "weather", "arguments": {"city": "Paris"}}}
+        ],
+    }