#
# Copyright (c) 2024-2026, Daily
#
# SPDX-License-Identifier: BSD 2-Clause License
#

"""
Unit tests for LLM adapters' get_llm_invocation_params() method.

These tests focus specifically on the "messages" field generation for different adapters, ensuring:

For OpenAI adapter:
1. LLMStandardMessage objects are passed through unchanged
2. LLMSpecificMessage objects with llm='openai' are included and others are filtered out
3. Complex message structures (like multi-part content) are preserved
4. System instructions are preserved throughout messages at any position
5. system_instruction is prepended as a system message, with conflict warnings
6. Developer messages pass through without triggering warnings

For Gemini adapter:
1. LLMStandardMessage objects are converted to Gemini Content format
2. LLMSpecificMessage objects with llm='google' are included and others are filtered out
3. Complex message structures (image, audio, multi-text) are converted to appropriate Gemini format
4. System messages are extracted as system_instruction (without duplication)
5. Single system instruction is converted to user message when no other messages exist
6. Multiple system instructions: first extracted, later ones converted to user messages
7. system_instruction overrides context system message, with conflict warnings
8. Developer messages are converted to user

For Anthropic adapter:
1. LLMStandardMessage objects are converted to Anthropic MessageParam format
2. LLMSpecificMessage objects with llm='anthropic' are included and others are filtered out
3. Complex message structures (image, multi-text) are converted to appropriate Anthropic format
4. System messages: first extracted as system parameter, later ones converted to user messages
5. Consecutive messages with same role are merged into multi-content-block messages
6. Empty text content is converted to "(empty)"
7. system_instruction overrides context system message, with conflict warnings
8. Developer messages are converted to user

For AWS Bedrock adapter:
1. LLMStandardMessage objects are converted to AWS Bedrock format
2. LLMSpecificMessage objects with llm='aws' are included and others are filtered out
3. Complex message structures (image, multi-text) are converted to appropriate AWS Bedrock format
4. System messages: first extracted as system parameter, later ones converted to user messages
5. Consecutive messages with same role are merged into multi-content-block messages
6. Empty text content is converted to "(empty)"
7. system_instruction overrides context system message, with conflict warnings
8. Developer messages are converted to user

For OpenAI Responses adapter:
1. LLMContext messages are converted to Responses API input items
2. System and developer role messages are converted to developer role
3. Assistant tool_calls produce function_call input items
4. Tool messages produce function_call_output input items
5. Multimodal content conversion (text -> input_text, image_url -> input_image)
6. Tools schema flattening (nested function dict -> flat format)
7. system_instruction sets instructions (or becomes developer message if input is empty)
8. Developer messages pass through as developer role without triggering warnings

For BaseLLMAdapter helpers:
1. _extract_initial_system: system extraction and conversion logic
2. _resolve_system_instruction: conflict resolution between context and settings
"""

import unittest
from unittest.mock import patch

from google.genai.types import Content, Part

from pipecat.adapters.schemas.function_schema import FunctionSchema
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMAdapter
from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
from pipecat.adapters.services.open_ai_responses_adapter import OpenAIResponsesLLMAdapter
from pipecat.adapters.services.perplexity_adapter import PerplexityLLMAdapter
from pipecat.processors.aggregators.llm_context import (
    LLMContext,
    LLMStandardMessage,
)


class TestOpenAIGetLLMInvocationParams(unittest.TestCase):
    def setUp(self) -> None:
        """Sets up a common adapter instance for all tests."""
        self.adapter = OpenAILLMAdapter()

    def test_standard_messages_passed_through_unchanged(self):
        """Test that LLMStandardMessage objects are passed through unchanged to OpenAI params."""
        # Create standard messages (OpenAI format)
        standard_messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello, how are you?"},
            {"role": "assistant", "content": "I'm doing well, thank you for asking!"},
        ]

        # Create context with these messages
        context = LLMContext(messages=standard_messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Verify messages are passed through unchanged
        self.assertEqual(params["messages"], standard_messages)
        self.assertEqual(len(params["messages"]), 3)

        # Verify content matches exactly
        self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
        self.assertEqual(params["messages"][1]["content"], "Hello, how are you?")
        self.assertEqual(params["messages"][2]["content"], "I'm doing well, thank you for asking!")

    def test_llm_specific_message_filtering(self):
        """Test that OpenAI-specific messages are included and others are filtered out."""
        # Create messages with different LLM-specific ones
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            AnthropicLLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "Anthropic specific message"}
            ),
            GeminiLLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "Gemini specific message"}
            ),
            {"role": "user", "content": "Standard user message"},
            self.adapter.create_llm_specific_message(
                {"role": "assistant", "content": "OpenAI specific response"}
            ),
        ]

        # Create context with these messages
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Should only include standard messages and OpenAI-specific ones
        # (3 total: system, standard user, openai assistant)
        self.assertEqual(len(params["messages"]), 3)

        # Verify the correct messages are included
        self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
        self.assertEqual(params["messages"][1]["content"], "Standard user message")
        self.assertEqual(
            params["messages"][2], {"role": "assistant", "content": "OpenAI specific response"}
        )

    def test_complex_message_content_preserved(self):
        """Test that complex message content (like multi-part messages) is preserved."""
        # Create a message with complex content structure (text + image)
        complex_image_message = {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "data:image/jpeg;base64,/9j/4AAQSkZJRgABAQAAAQABAAD..."},
                },
            ],
        }

        # Create a message with multiple text blocks
        multi_text_message = {
            "role": "assistant",
            "content": [
                {"type": "text", "text": "Let me analyze this step by step:"},
                {"type": "text", "text": "1. First, I'll examine the visual elements"},
                {"type": "text", "text": "2. Then I'll provide my conclusions"},
            ],
        }

        messages = [
            {"role": "system", "content": "You are a helpful assistant that can analyze images."},
            complex_image_message,
            multi_text_message,
        ]

        # Create context with these messages
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Verify complex content is preserved
        self.assertEqual(len(params["messages"]), 3)
        self.assertEqual(params["messages"][1], complex_image_message)
        self.assertEqual(params["messages"][2], multi_text_message)

        # Verify the image message structure is maintained
        image_content = params["messages"][1]["content"]
        self.assertIsInstance(image_content, list)
        self.assertEqual(len(image_content), 2)
        self.assertEqual(image_content[0]["type"], "text")
        self.assertEqual(image_content[1]["type"], "image_url")

        # Verify the multi-text message structure is maintained
        text_content = params["messages"][2]["content"]
        self.assertIsInstance(text_content, list)
        self.assertEqual(len(text_content), 3)
        for i, text_block in enumerate(text_content):
            self.assertEqual(text_block["type"], "text")
        self.assertEqual(text_content[0]["text"], "Let me analyze this step by step:")
        self.assertEqual(text_content[1]["text"], "1. First, I'll examine the visual elements")
        self.assertEqual(text_content[2]["text"], "2. Then I'll provide my conclusions")

    def test_system_instructions_preserved_throughout_messages(self):
        """Test that OpenAI adapter preserves system instructions sprinkled throughout messages."""
        # Create messages with system instructions at different positions
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello!"},
            {"role": "assistant", "content": "Hi there!"},
            {"role": "system", "content": "Remember to be concise."},
            {"role": "user", "content": "Tell me about Python."},
            {"role": "system", "content": "Use simple language."},
            {"role": "assistant", "content": "Python is a programming language."},
        ]

        # Create context with these messages
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # OpenAI should preserve all messages unchanged, including multiple system messages
        self.assertEqual(len(params["messages"]), 7)

        # Verify system messages are preserved at their original positions
        self.assertEqual(params["messages"][0]["role"], "system")
        self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")

        self.assertEqual(params["messages"][3]["role"], "system")
        self.assertEqual(params["messages"][3]["content"], "Remember to be concise.")

        self.assertEqual(params["messages"][5]["role"], "system")
        self.assertEqual(params["messages"][5]["content"], "Use simple language.")

        # Verify other messages remain unchanged
        self.assertEqual(params["messages"][1]["role"], "user")
        self.assertEqual(params["messages"][2]["role"], "assistant")
        self.assertEqual(params["messages"][4]["role"], "user")
        self.assertEqual(params["messages"][6]["role"], "assistant")

    def test_system_instruction_only(self):
        """system_instruction alone is prepended as a system message."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context, system_instruction="Be helpful.")

        self.assertEqual(params["messages"][0]["role"], "system")
        self.assertEqual(params["messages"][0]["content"], "Be helpful.")
        self.assertEqual(params["messages"][1]["role"], "user")

    def test_initial_system_message_only(self):
        """Initial system message without system_instruction passes through."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 2)
        self.assertEqual(params["messages"][0]["role"], "system")
        self.assertEqual(params["messages"][0]["content"], "You are helpful.")

    def test_both_system_instruction_and_system_message_warns(self):
        """system_instruction + initial system message warns but allows both."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context, system_instruction="Be concise."
            )
            mock_logger.warning.assert_called_once()
            warning_msg = mock_logger.warning.call_args[0][0]
            self.assertIn("may be unintended", warning_msg)

        # Both are present: prepended system_instruction + original system message
        self.assertEqual(params["messages"][0]["content"], "Be concise.")
        self.assertEqual(params["messages"][1]["content"], "You are helpful.")

    def test_both_system_instruction_and_developer_message_no_warning(self):
        """system_instruction + initial developer message does NOT warn."""
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context, system_instruction="Be concise."
            )
            mock_logger.warning.assert_not_called()

        # system_instruction prepended, developer message stays in messages
        self.assertEqual(params["messages"][0]["content"], "Be concise.")
        self.assertEqual(params["messages"][1]["role"], "developer")

    def test_warning_fires_only_once(self):
        """Conflict warning fires only once per adapter instance."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            self.adapter.get_llm_invocation_params(context, system_instruction="Be concise.")
            self.adapter.get_llm_invocation_params(context, system_instruction="Be concise.")
            mock_logger.warning.assert_called_once()


class TestGeminiGetLLMInvocationParams(unittest.TestCase):
    def setUp(self) -> None:
        """Sets up a common adapter instance for all tests."""
        self.adapter = GeminiLLMAdapter()

    def test_standard_messages_converted_to_gemini_format(self):
        """Test that LLMStandardMessage objects are converted to Gemini Content format."""
        # Create standard messages (OpenAI format)
        standard_messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello, how are you?"},
            {"role": "assistant", "content": "I'm doing well, thank you for asking!"},
        ]

        # Create context with these messages
        context = LLMContext(messages=standard_messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Verify system instruction is extracted
        self.assertEqual(params["system_instruction"], "You are a helpful assistant.")

        # Verify messages are converted to Gemini format (2 messages: user + model)
        self.assertEqual(len(params["messages"]), 2)

        # Check first message (user)
        user_msg = params["messages"][0]
        self.assertIsInstance(user_msg, Content)
        self.assertEqual(user_msg.role, "user")
        self.assertEqual(len(user_msg.parts), 1)
        self.assertEqual(user_msg.parts[0].text, "Hello, how are you?")

        # Check second message (assistant -> model)
        model_msg = params["messages"][1]
        self.assertIsInstance(model_msg, Content)
        self.assertEqual(model_msg.role, "model")
        self.assertEqual(len(model_msg.parts), 1)
        self.assertEqual(model_msg.parts[0].text, "I'm doing well, thank you for asking!")

    def test_llm_specific_message_filtering(self):
        """Test that Gemini-specific messages are included and others are filtered out."""
        # Create messages with different LLM-specific ones
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            OpenAILLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "OpenAI specific message"}
            ),
            AnthropicLLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "Anthropic specific message"}
            ),
            {"role": "user", "content": "Standard user message"},
            self.adapter.create_llm_specific_message(
                Content(role="model", parts=[Part(text="Gemini specific response")]),
            ),
        ]

        # Create context with these messages
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Should only include standard messages and Gemini-specific ones
        # (2 total: converted standard user + gemini model)
        self.assertEqual(len(params["messages"]), 2)

        # Verify system instruction
        self.assertEqual(params["system_instruction"], "You are a helpful assistant.")

        # Verify the correct messages are included
        self.assertEqual(params["messages"][0].role, "user")
        self.assertEqual(params["messages"][0].parts[0].text, "Standard user message")

        self.assertEqual(params["messages"][1].role, "model")
        self.assertEqual(params["messages"][1].parts[0].text, "Gemini specific response")

    def test_complex_message_content_preserved(self):
        """Test that complex message content (like multi-part messages) is preserved and converted.

        This test covers image, audio, and multi-text content conversion to Gemini format.
        """
        # Create a message with complex content structure (text + image)
        # Using a minimal valid base64 image data
        complex_image_message = {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
                    },
                },
            ],
        }

        # Create a message with multiple text blocks
        multi_text_message = {
            "role": "assistant",
            "content": [
                {"type": "text", "text": "Let me analyze this step by step:"},
                {"type": "text", "text": "1. First, I'll examine the visual elements"},
                {"type": "text", "text": "2. Then I'll provide my conclusions"},
            ],
        }

        # Create a message with audio input (text + audio)
        # Using a minimal valid base64 audio data (16 bytes of WAV header)
        audio_message = {
            "role": "user",
            "content": [
                {"type": "text", "text": "Can you transcribe this audio?"},
                {
                    "type": "input_audio",
                    "input_audio": {
                        "data": "UklGRiQAAABXQVZFZm10IBAAAAABAAEARKwAAIhYAQACABAAZGF0YQAAAAA=",
                        "format": "wav",
                    },
                },
            ],
        }

        messages = [
            {
                "role": "system",
                "content": "You are a helpful assistant that can analyze images and audio.",
            },
            complex_image_message,
            multi_text_message,
            audio_message,
        ]

        # Create context with these messages
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Verify system instruction
        self.assertEqual(
            params["system_instruction"],
            "You are a helpful assistant that can analyze images and audio.",
        )

        # Verify complex content is converted to Gemini format
        # Note: Gemini adapter may add system instruction back as user message in some cases
        self.assertGreaterEqual(len(params["messages"]), 3)

        # Find the different message types
        user_with_image = None
        model_with_text = None
        user_with_audio = None

        for msg in params["messages"]:
            if msg.role == "user" and len(msg.parts) == 2:
                # Check if it's image or audio based on the text content
                if hasattr(msg.parts[0], "text") and "image" in msg.parts[0].text:
                    user_with_image = msg
                elif hasattr(msg.parts[0], "text") and "audio" in msg.parts[0].text:
                    user_with_audio = msg
            elif msg.role == "model" and len(msg.parts) == 3:
                model_with_text = msg

        # Verify the image message structure is converted properly
        self.assertIsNotNone(user_with_image, "Should have user message with image")
        self.assertEqual(len(user_with_image.parts), 2)

        # First part should be text
        self.assertEqual(user_with_image.parts[0].text, "What's in this image?")

        # Second part should be image data (converted to Blob)
        self.assertIsNotNone(user_with_image.parts[1].inline_data)
        self.assertEqual(user_with_image.parts[1].inline_data.mime_type, "image/jpeg")

        # Verify the audio message structure is converted properly
        self.assertIsNotNone(user_with_audio, "Should have user message with audio")
        self.assertEqual(len(user_with_audio.parts), 2)

        # First part should be text
        self.assertEqual(user_with_audio.parts[0].text, "Can you transcribe this audio?")

        # Second part should be audio data (converted to Blob)
        self.assertIsNotNone(user_with_audio.parts[1].inline_data)
        self.assertEqual(user_with_audio.parts[1].inline_data.mime_type, "audio/wav")

        # Verify the multi-text message structure is converted properly
        self.assertIsNotNone(model_with_text, "Should have model message with multi-text")
        self.assertEqual(len(model_with_text.parts), 3)

        # All parts should be text
        expected_texts = [
            "Let me analyze this step by step:",
            "1. First, I'll examine the visual elements",
            "2. Then I'll provide my conclusions",
        ]
        for i, expected_text in enumerate(expected_texts):
            self.assertEqual(model_with_text.parts[i].text, expected_text)

    def test_single_system_instruction_converted_to_user(self):
        """Test that when there's only a system instruction, it gets converted to user message."""
        # Create context with only a system message
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        # When there's only one message, it's converted to user in-place (not extracted)
        # so system_instruction is None
        self.assertIsNone(params["system_instruction"])

        # The system message should be converted to a user message
        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0].role, "user")
        self.assertEqual(params["messages"][0].parts[0].text, "You are a helpful assistant.")

    def test_multiple_system_instructions_handling(self):
        """Test that first system instruction is extracted, later ones converted to user messages."""
        # Create messages with multiple system instructions
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello!"},
            {"role": "assistant", "content": "Hi there!"},
            {"role": "system", "content": "Remember to be concise."},
            {"role": "user", "content": "Tell me about Python."},
            {"role": "system", "content": "Use simple language."},
            {"role": "assistant", "content": "Python is a programming language."},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        # First system instruction should be extracted
        self.assertEqual(params["system_instruction"], "You are a helpful assistant.")

        # Should have 6 messages (original 7 minus 1 system instruction that was extracted)
        self.assertEqual(len(params["messages"]), 6)

        # Find the converted system messages (should be user role now)
        converted_system_messages = []
        for msg in params["messages"]:
            if msg.role == "user" and (
                msg.parts[0].text == "Remember to be concise."
                or msg.parts[0].text == "Use simple language."
            ):
                converted_system_messages.append(msg.parts[0].text)

        # Should have 2 converted system messages
        self.assertEqual(len(converted_system_messages), 2)
        self.assertIn("Remember to be concise.", converted_system_messages)
        self.assertIn("Use simple language.", converted_system_messages)

        # Verify that regular user and assistant messages are preserved
        user_messages = [msg for msg in params["messages"] if msg.role == "user"]
        model_messages = [msg for msg in params["messages"] if msg.role == "model"]

        # Should have 4 user messages: 2 original + 2 converted from system
        self.assertEqual(len(user_messages), 4)
        # Should have 2 model messages (converted from assistant)
        self.assertEqual(len(model_messages), 2)

    def test_system_instruction_only(self):
        """system_instruction alone becomes the system_instruction parameter."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context, system_instruction="Be helpful.")

        self.assertEqual(params["system_instruction"], "Be helpful.")

    def test_initial_system_message_only(self):
        """Initial system message is extracted as system_instruction."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(params["system_instruction"], "You are helpful.")
        self.assertEqual(len(params["messages"]), 1)

    def test_initial_developer_message_becomes_user(self):
        """Initial developer message without system_instruction becomes user, not system_instruction."""
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertIsNone(params["system_instruction"])
        self.assertEqual(len(params["messages"]), 2)
        self.assertEqual(params["messages"][0].role, "user")
        self.assertEqual(params["messages"][0].parts[0].text, "Extra context.")

    def test_both_system_instruction_and_system_message_warns(self):
        """system_instruction + initial system message warns and uses system_instruction."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context, system_instruction="Be concise."
            )
            mock_logger.warning.assert_called_once()

        self.assertEqual(params["system_instruction"], "Be concise.")

    def test_both_system_instruction_and_developer_message_no_warning(self):
        """system_instruction + initial developer message: no warning, developer becomes user."""
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context, system_instruction="Be concise."
            )
            mock_logger.warning.assert_not_called()

        self.assertEqual(params["system_instruction"], "Be concise.")

    def test_non_initial_system_message_not_extracted(self):
        """Non-initial system message is converted to user, not extracted as system instruction."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "system", "content": "Late system message"},
            {"role": "user", "content": "How are you?"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        # No system instruction should be extracted from non-initial position
        self.assertIsNone(params["system_instruction"])
        # The system message should have been converted to user role in the Gemini Content
        # (we check that 3 messages are present, meaning no extraction happened)
        self.assertEqual(len(params["messages"]), 3)

    def test_subsequent_developer_messages_converted_to_user(self):
        """Subsequent developer messages are converted to user role."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "developer", "content": "More instructions"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 2)
        # Second message (developer) should be converted to user in Google format
        self.assertEqual(params["messages"][1].role, "user")


class TestAnthropicGetLLMInvocationParams(unittest.TestCase):
    def setUp(self) -> None:
        """Sets up a common adapter instance for all tests."""
        self.adapter = AnthropicLLMAdapter()

    def test_standard_messages_converted_to_anthropic_format(self):
        """Test that LLMStandardMessage objects are converted to Anthropic MessageParam format."""
        # Create standard messages
        standard_messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello, how are you?"},
            {"role": "assistant", "content": "I'm doing well, thank you!"},
        ]

        # Create context
        context = LLMContext(messages=standard_messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # Verify system instruction is extracted
        self.assertEqual(params["system"], "You are a helpful assistant.")

        # Verify messages are in the params (2 messages after system extraction)
        self.assertIn("messages", params)
        self.assertEqual(len(params["messages"]), 2)

        # Check first message (user)
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertEqual(user_msg["content"], "Hello, how are you?")

        # Check second message (assistant)
        assistant_msg = params["messages"][1]
        self.assertEqual(assistant_msg["role"], "assistant")
        self.assertEqual(assistant_msg["content"], "I'm doing well, thank you!")

    def test_llm_specific_message_filtering(self):
        """Test that Anthropic-specific messages are included and others are filtered out."""
        # Create anthropic-specific message content
        anthropic_message_content = {
            "role": "user",
            "content": [
                {"type": "text", "text": "Hello"},
                {
                    "type": "image",
                    "source": {"type": "base64", "media_type": "image/jpeg", "data": "fake_data"},
                },
            ],
        }

        messages = [
            {"role": "user", "content": "Standard message"},
            OpenAILLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "OpenAI specific"}
            ),
            GeminiLLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "Google specific"}
            ),
            self.adapter.create_llm_specific_message(anthropic_message_content),
            {"role": "assistant", "content": "Response"},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # Should only have 2 messages after merging consecutive user messages: merged user + standard response
        # (openai and google specific filtered out, standard + anthropic-specific merged)
        self.assertEqual(len(params["messages"]), 2)

        # First message: merged user message (standard + anthropic-specific)
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertIsInstance(user_msg["content"], list)
        # Should have 3 content blocks: standard text + anthropic text + anthropic image
        self.assertEqual(len(user_msg["content"]), 3)
        self.assertEqual(user_msg["content"][0]["type"], "text")
        self.assertEqual(user_msg["content"][0]["text"], "Standard message")
        self.assertEqual(user_msg["content"][1]["type"], "text")
        self.assertEqual(user_msg["content"][1]["text"], "Hello")
        self.assertEqual(user_msg["content"][2]["type"], "image")

        # Second message: standard response
        self.assertEqual(params["messages"][1]["content"], "Response")

    def test_consecutive_same_role_messages_merged(self):
        """Test that consecutive messages with the same role are merged into multi-content blocks."""
        messages = [
            {"role": "user", "content": "First user message"},
            {"role": "user", "content": "Second user message"},
            {"role": "user", "content": "Third user message"},
            {"role": "assistant", "content": "First assistant message"},
            {"role": "assistant", "content": "Second assistant message"},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # Should have 2 messages after merging (1 user, 1 assistant)
        self.assertEqual(len(params["messages"]), 2)

        # Check merged user message
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertIsInstance(user_msg["content"], list)
        self.assertEqual(len(user_msg["content"]), 3)
        self.assertEqual(user_msg["content"][0]["type"], "text")
        self.assertEqual(user_msg["content"][0]["text"], "First user message")
        self.assertEqual(user_msg["content"][1]["type"], "text")
        self.assertEqual(user_msg["content"][1]["text"], "Second user message")
        self.assertEqual(user_msg["content"][2]["type"], "text")
        self.assertEqual(user_msg["content"][2]["text"], "Third user message")

        # Check merged assistant message
        assistant_msg = params["messages"][1]
        self.assertEqual(assistant_msg["role"], "assistant")
        self.assertIsInstance(assistant_msg["content"], list)
        self.assertEqual(len(assistant_msg["content"]), 2)
        self.assertEqual(assistant_msg["content"][0]["type"], "text")
        self.assertEqual(assistant_msg["content"][0]["text"], "First assistant message")
        self.assertEqual(assistant_msg["content"][1]["type"], "text")
        self.assertEqual(assistant_msg["content"][1]["text"], "Second assistant message")

    def test_empty_text_converted_to_empty_placeholder(self):
        """Test that empty text content is converted to "(empty)" string."""
        messages = [
            {"role": "user", "content": ""},  # Empty string
            {
                "role": "assistant",
                "content": [
                    {"type": "text", "text": ""},  # Empty text in list content
                    {"type": "text", "text": "Valid text"},
                ],
            },
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # Check that empty string content was converted
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["content"], "(empty)")

        # Check that empty text in list content was converted
        assistant_msg = params["messages"][1]
        self.assertIsInstance(assistant_msg["content"], list)
        self.assertEqual(assistant_msg["content"][0]["text"], "(empty)")
        self.assertEqual(assistant_msg["content"][1]["text"], "Valid text")

    def test_complex_message_content_preserved(self):
        """Test that complex message structures (text + image) are properly converted to Anthropic format."""
        # Create a complex message with both text and image content
        complex_message = {
            "role": "user",
            "content": [
                {"type": "text", "text": "What do you see in this image?"},
                {
                    "type": "image_url",
                    "image_url": {"url": "data:image/jpeg;base64,fake_image_data"},
                },
                {"type": "text", "text": "Please describe it in detail."},
            ],
        }

        messages = [
            complex_message,
            {"role": "assistant", "content": "I can see the image clearly."},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # Verify complex message structure is preserved and converted
        self.assertEqual(len(params["messages"]), 2)
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertIsInstance(user_msg["content"], list)
        self.assertEqual(len(user_msg["content"]), 3)

        # Note: Anthropic adapter reorders single images to come before text, as per Anthropic docs
        # Check image part (should be moved to first position and converted from image_url to image)
        self.assertEqual(user_msg["content"][0]["type"], "image")
        self.assertIn("source", user_msg["content"][0])
        self.assertEqual(user_msg["content"][0]["source"]["type"], "base64")
        self.assertEqual(user_msg["content"][0]["source"]["media_type"], "image/jpeg")
        self.assertEqual(user_msg["content"][0]["source"]["data"], "fake_image_data")

        # Check first text part (moved to second position)
        self.assertEqual(user_msg["content"][1]["type"], "text")
        self.assertEqual(user_msg["content"][1]["text"], "What do you see in this image?")

        # Check second text part (moved to third position)
        self.assertEqual(user_msg["content"][2]["type"], "text")
        self.assertEqual(user_msg["content"][2]["text"], "Please describe it in detail.")

    def test_multiple_system_instructions_handling(self):
        """Test that first system instruction is extracted, later ones converted to user messages."""
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi there!"},
            {"role": "system", "content": "Remember to be concise."},  # Later system message
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # System instruction should be extracted from first message
        self.assertEqual(params["system"], "You are a helpful assistant.")

        # Should have 3 messages remaining (system message was removed, later system converted to user)
        self.assertEqual(len(params["messages"]), 3)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"], "Hello")
        self.assertEqual(params["messages"][1]["role"], "assistant")
        self.assertEqual(params["messages"][1]["content"], "Hi there!")

        # Later system message should be converted to user role
        self.assertEqual(params["messages"][2]["role"], "user")
        self.assertEqual(params["messages"][2]["content"], "Remember to be concise.")

    def test_single_system_message_converted_to_user(self):
        """Test that a single system message is converted to user role when no other messages exist."""
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # System should be NOT_GIVEN since we only have one message
        from anthropic import NOT_GIVEN

        self.assertEqual(params["system"], NOT_GIVEN)

        # Single system message should be converted to user role
        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")

    def test_system_instruction_only(self):
        """system_instruction alone becomes the system parameter."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(
            context, enable_prompt_caching=False, system_instruction="Be helpful."
        )

        self.assertEqual(params["system"], "Be helpful.")
        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "user")

    def test_initial_developer_message_becomes_user(self):
        """Initial developer message without system_instruction becomes user, not system."""
        from anthropic import NOT_GIVEN

        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "assistant", "content": "OK"},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        self.assertEqual(params["system"], NOT_GIVEN)
        self.assertEqual(len(params["messages"]), 3)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"], "Extra context.")

    def test_both_system_instruction_and_system_message_warns(self):
        """system_instruction + initial system message warns and uses system_instruction."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context,
                enable_prompt_caching=False,
                system_instruction="Be concise.",
            )
            mock_logger.warning.assert_called_once()
            warning_msg = mock_logger.warning.call_args[0][0]
            self.assertIn("Using system_instruction", warning_msg)

        self.assertEqual(params["system"], "Be concise.")

    def test_both_system_instruction_and_developer_message_no_warning(self):
        """system_instruction + initial developer message: no warning, developer becomes user."""
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "assistant", "content": "Hi"},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context,
                enable_prompt_caching=False,
                system_instruction="Be concise.",
            )
            mock_logger.warning.assert_not_called()

        self.assertEqual(params["system"], "Be concise.")
        # Developer message should have been converted to "user"
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"], "Extra context.")

    def test_subsequent_developer_messages_converted_to_user(self):
        """Subsequent developer messages are converted to user role."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi"},
            {"role": "developer", "content": "More instructions"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context, enable_prompt_caching=False)

        # Developer message was converted to "user"
        self.assertEqual(params["messages"][2]["role"], "user")
        self.assertEqual(params["messages"][2]["content"], "More instructions")

    def test_initial_system_discarded_when_system_instruction_provided(self):
        """Initial system message is discarded when system_instruction is provided."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "Old instruction."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger"):
            params = self.adapter.get_llm_invocation_params(
                context,
                enable_prompt_caching=False,
                system_instruction="New instruction.",
            )

        self.assertEqual(params["system"], "New instruction.")
        # Only the user message should remain
        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "user")


class TestAWSBedrockGetLLMInvocationParams(unittest.TestCase):
    def setUp(self) -> None:
        """Sets up a common adapter instance for all tests."""
        self.adapter = AWSBedrockLLMAdapter()

    def test_standard_messages_converted_to_aws_bedrock_format(self):
        """Test that LLMStandardMessage objects are converted to AWS Bedrock format."""
        # Create standard messages
        standard_messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello, how are you?"},
            {"role": "assistant", "content": "I'm doing well, thank you!"},
        ]

        # Create context
        context = LLMContext(messages=standard_messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Verify system instruction is extracted (in AWS Bedrock format)
        self.assertIsInstance(params["system"], list)
        self.assertEqual(len(params["system"]), 1)
        self.assertEqual(params["system"][0]["text"], "You are a helpful assistant.")

        # Verify messages are in the params (2 messages after system extraction)
        self.assertIn("messages", params)
        self.assertEqual(len(params["messages"]), 2)

        # Check first message (user) - should be converted to AWS Bedrock format
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertIsInstance(user_msg["content"], list)
        self.assertEqual(len(user_msg["content"]), 1)
        self.assertEqual(user_msg["content"][0]["text"], "Hello, how are you?")

        # Check second message (assistant) - should be converted to AWS Bedrock format
        assistant_msg = params["messages"][1]
        self.assertEqual(assistant_msg["role"], "assistant")
        self.assertIsInstance(assistant_msg["content"], list)
        self.assertEqual(len(assistant_msg["content"]), 1)
        self.assertEqual(assistant_msg["content"][0]["text"], "I'm doing well, thank you!")

    def test_llm_specific_message_filtering(self):
        """Test that AWS-specific messages are included and others are filtered out."""
        # Create aws-specific message content (which is what AWS Bedrock uses)
        aws_message_content = {
            "role": "user",
            "content": [
                {"text": "Hello"},
                {"image": {"format": "jpeg", "source": {"bytes": b"fake_image_data"}}},
            ],
        }

        messages = [
            {"role": "user", "content": "Standard message"},
            OpenAILLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "OpenAI specific"}
            ),
            GeminiLLMAdapter().create_llm_specific_message(
                {"role": "user", "content": "Google specific"}
            ),
            self.adapter.create_llm_specific_message(message=aws_message_content),
            {"role": "assistant", "content": "Response"},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Should only have 2 messages after merging consecutive user messages: merged user + standard response
        # (openai and google specific filtered out, standard + aws-specific merged)
        self.assertEqual(len(params["messages"]), 2)

        # First message: merged user message (standard + aws-specific)
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertIsInstance(user_msg["content"], list)
        # Should have 3 content blocks: standard text + aws text + aws image
        self.assertEqual(len(user_msg["content"]), 3)
        self.assertEqual(user_msg["content"][0]["text"], "Standard message")
        self.assertEqual(user_msg["content"][1]["text"], "Hello")
        self.assertIn("image", user_msg["content"][2])

        # Second message: standard response
        self.assertEqual(params["messages"][1]["content"][0]["text"], "Response")

    def test_consecutive_same_role_messages_merged(self):
        """Test that consecutive messages with the same role are merged into multi-content blocks."""
        messages = [
            {"role": "user", "content": "First user message"},
            {"role": "user", "content": "Second user message"},
            {"role": "user", "content": "Third user message"},
            {"role": "assistant", "content": "First assistant message"},
            {"role": "assistant", "content": "Second assistant message"},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Should have 2 messages after merging (1 user, 1 assistant)
        self.assertEqual(len(params["messages"]), 2)

        # Check merged user message
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertIsInstance(user_msg["content"], list)
        self.assertEqual(len(user_msg["content"]), 3)
        self.assertEqual(user_msg["content"][0]["text"], "First user message")
        self.assertEqual(user_msg["content"][1]["text"], "Second user message")
        self.assertEqual(user_msg["content"][2]["text"], "Third user message")

        # Check merged assistant message
        assistant_msg = params["messages"][1]
        self.assertEqual(assistant_msg["role"], "assistant")
        self.assertIsInstance(assistant_msg["content"], list)
        self.assertEqual(len(assistant_msg["content"]), 2)
        self.assertEqual(assistant_msg["content"][0]["text"], "First assistant message")
        self.assertEqual(assistant_msg["content"][1]["text"], "Second assistant message")

    def test_empty_text_converted_to_empty_placeholder(self):
        """Test that empty text content is converted to "(empty)" string."""
        messages = [
            {"role": "user", "content": ""},  # Empty string
            {
                "role": "assistant",
                "content": [
                    {"type": "text", "text": ""},  # Empty text in list content
                    {"type": "text", "text": "Valid text"},
                ],
            },
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Check that empty string content was converted
        user_msg = params["messages"][0]
        self.assertIsInstance(user_msg["content"], list)
        self.assertEqual(user_msg["content"][0]["text"], "(empty)")

        # Check that empty text in list content was converted
        assistant_msg = params["messages"][1]
        self.assertIsInstance(assistant_msg["content"], list)
        self.assertEqual(assistant_msg["content"][0]["text"], "(empty)")
        self.assertEqual(assistant_msg["content"][1]["text"], "Valid text")

    def test_complex_message_content_preserved(self):
        """Test that complex message structures (text + image) are properly converted to AWS Bedrock format."""
        # Create a complex message with both text and image content
        # Use a valid base64 string for the image
        complex_message = {
            "role": "user",
            "content": [
                {"type": "text", "text": "What do you see in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAYAAAAfFcSJAAAADUlEQVR42mNkYPhfDwAChwGA60e6kgAAAABJRU5ErkJggg=="
                    },
                },
                {"type": "text", "text": "Please describe it in detail."},
            ],
        }

        messages = [
            complex_message,
            {"role": "assistant", "content": "I can see the image clearly."},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # Verify complex message structure is preserved and converted
        self.assertEqual(len(params["messages"]), 2)
        user_msg = params["messages"][0]
        self.assertEqual(user_msg["role"], "user")
        self.assertIsInstance(user_msg["content"], list)
        self.assertEqual(len(user_msg["content"]), 3)

        # Note: AWS Bedrock adapter reorders single images to come before text, like Anthropic
        # Check image part (should be moved to first position and converted from image_url to image)
        self.assertIn("image", user_msg["content"][0])
        self.assertEqual(user_msg["content"][0]["image"]["format"], "jpeg")
        self.assertIn("source", user_msg["content"][0]["image"])
        self.assertIn("bytes", user_msg["content"][0]["image"]["source"])

        # Check first text part (moved to second position)
        self.assertEqual(user_msg["content"][1]["text"], "What do you see in this image?")

        # Check second text part (moved to third position)
        self.assertEqual(user_msg["content"][2]["text"], "Please describe it in detail.")

    def test_multiple_system_instructions_handling(self):
        """Test that first system instruction is extracted, later ones converted to user messages."""
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi there!"},
            {"role": "system", "content": "Remember to be concise."},  # Later system message
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # System instruction should be extracted from first message (in AWS Bedrock format)
        self.assertIsInstance(params["system"], list)
        self.assertEqual(len(params["system"]), 1)
        self.assertEqual(params["system"][0]["text"], "You are a helpful assistant.")

        # Should have 3 messages remaining (system message was removed, later system converted to user)
        self.assertEqual(len(params["messages"]), 3)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"][0]["text"], "Hello")
        self.assertEqual(params["messages"][1]["role"], "assistant")
        self.assertEqual(params["messages"][1]["content"][0]["text"], "Hi there!")

        # Later system message should be converted to user role
        self.assertEqual(params["messages"][2]["role"], "user")
        self.assertEqual(params["messages"][2]["content"][0]["text"], "Remember to be concise.")

    def test_single_system_message_handling(self):
        """Test that a single system message is converted to user role when no other messages exist."""
        messages = [
            {"role": "system", "content": "You are a helpful assistant."},
        ]

        # Create context
        context = LLMContext(messages=messages)

        # Get invocation params
        params = self.adapter.get_llm_invocation_params(context)

        # When there's only one message, it's converted to user in-place (not extracted)
        # so system is None
        self.assertIsNone(params["system"])

        # Single system message should be converted to user role
        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(
            params["messages"][0]["content"][0]["text"], "You are a helpful assistant."
        )

    def test_system_instruction_only(self):
        """system_instruction alone becomes the system parameter."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context, system_instruction="Be helpful.")

        self.assertEqual(params["system"], [{"text": "Be helpful."}])

    def test_initial_developer_message_becomes_user(self):
        """Initial developer message without system_instruction becomes user, not system."""
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "assistant", "content": "OK"},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertIsNone(params["system"])
        self.assertEqual(len(params["messages"]), 3)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"][0]["text"], "Extra context.")

    def test_both_system_instruction_and_system_message_warns(self):
        """system_instruction + initial system message warns and uses system_instruction."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context, system_instruction="Be concise."
            )
            mock_logger.warning.assert_called_once()

        self.assertEqual(params["system"], [{"text": "Be concise."}])

    def test_both_system_instruction_and_developer_message_no_warning(self):
        """system_instruction + initial developer message: no warning, developer becomes user."""
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = self.adapter.get_llm_invocation_params(
                context, system_instruction="Be concise."
            )
            mock_logger.warning.assert_not_called()

        self.assertEqual(params["system"], [{"text": "Be concise."}])
        self.assertEqual(params["messages"][0]["role"], "user")

    def test_subsequent_developer_messages_converted_to_user(self):
        """Subsequent developer messages are converted to user role."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi"},
            {"role": "developer", "content": "More instructions"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(params["messages"][2]["role"], "user")


class TestPerplexityGetLLMInvocationParams(unittest.TestCase):
    def setUp(self) -> None:
        """Sets up a common adapter instance for all tests."""
        self.adapter = PerplexityLLMAdapter()

    def test_standard_messages_pass_through(self):
        """Test that a valid [user, assistant, user] sequence passes through unchanged."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi there!"},
            {"role": "user", "content": "How are you?"},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 3)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"], "Hello")
        self.assertEqual(params["messages"][1]["role"], "assistant")
        self.assertEqual(params["messages"][1]["content"], "Hi there!")
        self.assertEqual(params["messages"][2]["role"], "user")
        self.assertEqual(params["messages"][2]["content"], "How are you?")

    def test_initial_system_message_preserved(self):
        """Test that a valid [system, user, assistant, user] sequence passes through unchanged."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi!"},
            {"role": "user", "content": "Bye"},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 4)
        self.assertEqual(params["messages"][0]["role"], "system")
        self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
        self.assertEqual(params["messages"][1]["role"], "user")
        self.assertEqual(params["messages"][2]["role"], "assistant")
        self.assertEqual(params["messages"][3]["role"], "user")

    def test_consecutive_same_role_messages_merged(self):
        """Test that consecutive user messages are merged into list-of-dicts content."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "First message"},
            {"role": "user", "content": "Second message"},
            {"role": "assistant", "content": "Response"},
            {"role": "user", "content": "Third message"},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 3)

        # First message should be merged users
        merged = params["messages"][0]
        self.assertEqual(merged["role"], "user")
        self.assertIsInstance(merged["content"], list)
        self.assertEqual(len(merged["content"]), 2)
        self.assertEqual(merged["content"][0]["type"], "text")
        self.assertEqual(merged["content"][0]["text"], "First message")
        self.assertEqual(merged["content"][1]["type"], "text")
        self.assertEqual(merged["content"][1]["text"], "Second message")

        self.assertEqual(params["messages"][1]["role"], "assistant")
        self.assertEqual(params["messages"][2]["role"], "user")

    def test_non_initial_system_converted_to_user(self):
        """Test that non-initial system messages are converted to user and merged with adjacent user."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi!"},
            {"role": "system", "content": "Be concise."},
            {"role": "user", "content": "Tell me about Python."},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        # system(initial), user, assistant, merged(system→user + user)
        self.assertEqual(len(params["messages"]), 4)
        self.assertEqual(params["messages"][0]["role"], "system")
        self.assertEqual(params["messages"][1]["role"], "user")
        self.assertEqual(params["messages"][2]["role"], "assistant")

        # The converted system→user and the following user should be merged
        merged = params["messages"][3]
        self.assertEqual(merged["role"], "user")
        self.assertIsInstance(merged["content"], list)
        self.assertEqual(len(merged["content"]), 2)
        self.assertEqual(merged["content"][0]["text"], "Be concise.")
        self.assertEqual(merged["content"][1]["text"], "Tell me about Python.")

    def test_multiple_system_messages_at_start_preserved(self):
        """Test that multiple consecutive system messages at start pass through unchanged."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "system", "content": "Always be polite."},
            {"role": "user", "content": "Hello"},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 3)
        self.assertEqual(params["messages"][0]["role"], "system")
        self.assertEqual(params["messages"][0]["content"], "You are a helpful assistant.")
        self.assertEqual(params["messages"][1]["role"], "system")
        self.assertEqual(params["messages"][1]["content"], "Always be polite.")
        self.assertEqual(params["messages"][2]["role"], "user")
        self.assertEqual(params["messages"][2]["content"], "Hello")

    def test_trailing_assistant_removed(self):
        """Test that a trailing assistant message is removed."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi there!"},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"], "Hello")

    def test_only_system_messages_preserved(self):
        """Test that system-only contexts are left unchanged (no system→user conversion).

        We intentionally do not convert trailing system messages to "user"
        because that would make the transformation unstable across calls —
        Perplexity has statefulness within a conversation, so a message that
        was "user" in one call but becomes "system" in the next causes errors.
        """
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are a helpful assistant."},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "system")

    def test_system_exposed_after_trailing_assistant_removed(self):
        """Test that a system message exposed by trailing assistant removal stays system.

        It's important that initial system messages are never converted to
        "user", because Perplexity has statefulness within a conversation — if
        a message was sent as "system" in one call and then becomes "user" in a
        later call (after more messages are appended), the API rejects it.
        """
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "assistant", "content": "Sure thing."},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        # Trailing assistant removed → [system], system stays as-is
        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "system")
        self.assertEqual(params["messages"][0]["content"], "You are helpful.")

    def test_consecutive_assistants_merged_then_trailing_removed(self):
        """Test that consecutive assistant messages are merged, then trailing assistant is removed."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "First response"},
            {"role": "assistant", "content": "Second response"},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        # After merging assistants we get [user, assistant(merged)], then trailing
        # assistant is removed, leaving just [user]
        self.assertEqual(len(params["messages"]), 1)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][0]["content"], "Hello")

    def test_tool_messages_preserved(self):
        """Test that tool messages pass through without modification."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "What's the weather?"},
            {
                "role": "assistant",
                "content": "Let me check.",
                "tool_calls": [{"id": "1", "function": {"name": "get_weather", "arguments": "{}"}}],
            },
            {"role": "tool", "content": "Sunny, 72F", "tool_call_id": "1"},
            {"role": "user", "content": "Thanks!"},
        ]

        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["messages"]), 4)
        self.assertEqual(params["messages"][0]["role"], "user")
        self.assertEqual(params["messages"][1]["role"], "assistant")
        self.assertEqual(params["messages"][2]["role"], "tool")
        self.assertEqual(params["messages"][2]["content"], "Sunny, 72F")
        self.assertEqual(params["messages"][3]["role"], "user")

    def test_empty_messages(self):
        """Test that empty messages list returns empty."""
        context = LLMContext(messages=[])
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(params["messages"], [])


class TestOpenAIResponsesGetLLMInvocationParams(unittest.TestCase):
    def setUp(self) -> None:
        """Sets up a common adapter instance for all tests."""
        self.adapter = OpenAIResponsesLLMAdapter()

    def test_simple_user_assistant_messages(self):
        """Simple user/assistant text messages are converted correctly."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "assistant", "content": "Hi there!"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["input"]), 2)
        self.assertEqual(params["input"][0], {"role": "user", "content": "Hello"})
        self.assertEqual(params["input"][1], {"role": "assistant", "content": "Hi there!"})

    def test_system_role_converted_to_developer(self):
        """System role messages are converted to developer role."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(params["input"][0]["role"], "developer")
        self.assertEqual(params["input"][0]["content"], "You are helpful.")

    def test_developer_role_kept_as_developer(self):
        """Developer role messages are kept as developer role."""
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(params["input"][0]["role"], "developer")
        self.assertEqual(params["input"][0]["content"], "Extra context.")

    def test_system_message_without_system_instruction_no_warning(self):
        """System message without system_instruction does not trigger a warning."""
        adapter = OpenAIResponsesLLMAdapter()
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            adapter.get_llm_invocation_params(context)
            mock_logger.warning.assert_not_called()

    def test_system_message_with_system_instruction_triggers_warning(self):
        """System message + system_instruction triggers a conflict warning."""
        adapter = OpenAIResponsesLLMAdapter()
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            adapter.get_llm_invocation_params(context, system_instruction="Be concise.")
            mock_logger.warning.assert_called_once()
            warning_msg = mock_logger.warning.call_args[0][0]
            self.assertIn("system_instruction", warning_msg)

    def test_developer_message_with_system_instruction_no_warning(self):
        """Developer message + system_instruction does NOT trigger a warning."""
        adapter = OpenAIResponsesLLMAdapter()
        messages: list[LLMStandardMessage] = [
            {"role": "developer", "content": "Extra context."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = adapter.get_llm_invocation_params(context, system_instruction="Be concise.")
            mock_logger.warning.assert_not_called()

        # Developer message stays as developer, system_instruction becomes instructions
        self.assertEqual(params["input"][0]["role"], "developer")
        self.assertEqual(params["instructions"], "Be concise.")

    def test_non_initial_system_message_no_warning(self):
        """Non-initial system messages are converted without a warning."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
            {"role": "system", "content": "New instruction"},
        ]
        context = LLMContext(messages=messages)

        adapter = OpenAIResponsesLLMAdapter()
        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            params = adapter.get_llm_invocation_params(context, system_instruction="Be helpful.")
            mock_logger.warning.assert_not_called()

        self.assertEqual(params["input"][1]["role"], "developer")
        self.assertEqual(params["input"][1]["content"], "New instruction")

    def test_conflict_warning_fires_only_once(self):
        """The conflict warning fires only once per adapter instance."""
        messages: list[LLMStandardMessage] = [
            {"role": "system", "content": "You are helpful."},
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)

        adapter = OpenAIResponsesLLMAdapter()
        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            adapter.get_llm_invocation_params(context, system_instruction="Be concise.")
            adapter.get_llm_invocation_params(context, system_instruction="Be concise.")
            mock_logger.warning.assert_called_once()

    def test_assistant_tool_calls_to_function_call(self):
        """Assistant messages with tool_calls produce function_call input items."""
        messages = [
            {
                "role": "assistant",
                "tool_calls": [
                    {
                        "id": "call_123",
                        "function": {
                            "name": "get_weather",
                            "arguments": '{"location": "SF"}',
                        },
                        "type": "function",
                    }
                ],
            }
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["input"]), 1)
        fc = params["input"][0]
        self.assertEqual(fc["type"], "function_call")
        self.assertEqual(fc["call_id"], "call_123")
        self.assertEqual(fc["name"], "get_weather")
        self.assertEqual(fc["arguments"], '{"location": "SF"}')

    def test_multiple_tool_calls(self):
        """Multiple tool calls in one assistant message produce multiple function_call items."""
        messages = [
            {
                "role": "assistant",
                "tool_calls": [
                    {
                        "id": "call_1",
                        "function": {"name": "get_weather", "arguments": '{"location": "SF"}'},
                        "type": "function",
                    },
                    {
                        "id": "call_2",
                        "function": {
                            "name": "get_restaurant",
                            "arguments": '{"location": "SF"}',
                        },
                        "type": "function",
                    },
                ],
            }
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["input"]), 2)
        self.assertEqual(params["input"][0]["name"], "get_weather")
        self.assertEqual(params["input"][1]["name"], "get_restaurant")

    def test_tool_message_to_function_call_output(self):
        """Tool role messages produce function_call_output input items."""
        messages = [
            {
                "role": "tool",
                "content": '{"temperature": "72"}',
                "tool_call_id": "call_123",
            }
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["input"]), 1)
        fco = params["input"][0]
        self.assertEqual(fco["type"], "function_call_output")
        self.assertEqual(fco["call_id"], "call_123")
        self.assertEqual(fco["output"], '{"temperature": "72"}')

    def test_mixed_conversation(self):
        """Mixed conversation with text + function calls converts correctly."""
        messages = [
            {"role": "user", "content": "What's the weather in SF?"},
            {
                "role": "assistant",
                "tool_calls": [
                    {
                        "id": "call_abc",
                        "function": {"name": "get_weather", "arguments": '{"location": "SF"}'},
                        "type": "function",
                    }
                ],
            },
            {
                "role": "tool",
                "content": '{"temp": "72"}',
                "tool_call_id": "call_abc",
            },
            {"role": "assistant", "content": "It's 72 degrees in SF."},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["input"]), 4)
        self.assertEqual(params["input"][0]["role"], "user")
        self.assertEqual(params["input"][1]["type"], "function_call")
        self.assertEqual(params["input"][2]["type"], "function_call_output")
        self.assertEqual(params["input"][3]["role"], "assistant")

    def test_multimodal_text_conversion(self):
        """Multimodal text content parts are converted to input_text."""
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "What's in this image?"},
                ],
            }
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        content = params["input"][0]["content"]
        self.assertEqual(len(content), 1)
        self.assertEqual(content[0]["type"], "input_text")
        self.assertEqual(content[0]["text"], "What's in this image?")

    def test_multimodal_image_conversion(self):
        """Multimodal image_url content parts are converted to input_image."""
        messages = [
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": "Describe this:"},
                    {
                        "type": "image_url",
                        "image_url": {"url": "data:image/jpeg;base64,abc123"},
                    },
                ],
            }
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        content = params["input"][0]["content"]
        self.assertEqual(len(content), 2)
        self.assertEqual(content[0]["type"], "input_text")
        self.assertEqual(content[1]["type"], "input_image")
        self.assertEqual(content[1]["image_url"], "data:image/jpeg;base64,abc123")
        self.assertEqual(content[1]["detail"], "auto")

    def test_multimodal_image_with_detail(self):
        """Image content parts preserve the detail setting when provided."""
        messages = [
            {
                "role": "user",
                "content": [
                    {
                        "type": "image_url",
                        "image_url": {"url": "https://example.com/img.png", "detail": "high"},
                    },
                ],
            }
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        content = params["input"][0]["content"]
        self.assertEqual(content[0]["detail"], "high")

    def test_tools_schema_flattening(self):
        """Tools schema with nested function dict is flattened to Responses API format."""
        weather_fn = FunctionSchema(
            name="get_weather",
            description="Get the current weather",
            properties={
                "location": {"type": "string", "description": "The city"},
            },
            required=["location"],
        )
        tools = ToolsSchema(standard_tools=[weather_fn])
        context = LLMContext(tools=tools)
        params = self.adapter.get_llm_invocation_params(context)

        tool_list = params["tools"]
        self.assertEqual(len(tool_list), 1)
        tool = tool_list[0]
        self.assertEqual(tool["type"], "function")
        self.assertEqual(tool["name"], "get_weather")
        self.assertEqual(tool["description"], "Get the current weather")
        self.assertIn("properties", tool["parameters"])

    def test_empty_messages(self):
        """Empty messages list produces empty input list."""
        context = LLMContext(messages=[])
        params = self.adapter.get_llm_invocation_params(context)
        self.assertEqual(params["input"], [])

    def test_llm_specific_message_passthrough(self):
        """LLMSpecificMessage with llm='openai_responses' passes through."""
        specific_msg = self.adapter.create_llm_specific_message(
            {"type": "function_call", "call_id": "x", "name": "foo", "arguments": "{}"}
        )
        messages = [
            {"role": "user", "content": "Hello"},
            specific_msg,
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context)

        self.assertEqual(len(params["input"]), 2)
        self.assertEqual(params["input"][0]["role"], "user")
        self.assertEqual(params["input"][1]["type"], "function_call")

    def test_id_for_llm_specific_messages(self):
        """Adapter identifier is 'openai_responses'."""
        self.assertEqual(self.adapter.id_for_llm_specific_messages, "openai_responses")

    def test_system_instruction_with_messages_sets_instructions(self):
        """When system_instruction is provided and input is non-empty, sets instructions."""
        messages: list[LLMStandardMessage] = [
            {"role": "user", "content": "Hello"},
        ]
        context = LLMContext(messages=messages)
        params = self.adapter.get_llm_invocation_params(context, system_instruction="Be helpful.")

        self.assertEqual(params["instructions"], "Be helpful.")
        self.assertEqual(len(params["input"]), 1)
        self.assertEqual(params["input"][0]["role"], "user")

    def test_system_instruction_with_empty_input_becomes_developer_message(self):
        """When system_instruction is provided but input is empty, it becomes a developer message."""
        context = LLMContext(messages=[])
        params = self.adapter.get_llm_invocation_params(context, system_instruction="Be helpful.")

        self.assertNotIn("instructions", params)
        self.assertEqual(len(params["input"]), 1)
        self.assertEqual(params["input"][0]["role"], "developer")
        self.assertEqual(params["input"][0]["content"], "Be helpful.")

    def test_no_system_instruction_omits_instructions(self):
        """When no system_instruction is provided, instructions key is absent."""
        context = LLMContext(messages=[{"role": "user", "content": "Hi"}])
        params = self.adapter.get_llm_invocation_params(context)

        self.assertNotIn("instructions", params)


class TestBaseLLMAdapterHelpers(unittest.TestCase):
    """Tests for the shared helper methods on BaseLLMAdapter."""

    def setUp(self):
        # Use OpenAILLMAdapter as a concrete implementation for testing the base helpers
        self.adapter = OpenAILLMAdapter()

    def test_extract_system_message(self):
        """System message is extracted from messages[0]."""
        messages = [
            {"role": "system", "content": "Be helpful."},
            {"role": "user", "content": "Hello"},
        ]
        content = self.adapter._extract_initial_system(messages, system_instruction=None)

        self.assertEqual(content, "Be helpful.")
        self.assertEqual(len(messages), 1)  # popped

    def test_extract_developer_not_extracted(self):
        """Developer message is not extracted by _extract_initial_system."""
        messages = [
            {"role": "developer", "content": "Context."},
            {"role": "user", "content": "Hello"},
        ]
        content = self.adapter._extract_initial_system(messages, system_instruction=None)

        self.assertIsNone(content)
        self.assertEqual(len(messages), 2)  # not popped
        self.assertEqual(messages[0]["role"], "developer")  # unchanged

    def test_developer_with_system_instruction_not_extracted(self):
        """Developer message with system_instruction is not handled by _extract_initial_system."""
        messages = [
            {"role": "developer", "content": "Context."},
            {"role": "user", "content": "Hello"},
        ]
        content = self.adapter._extract_initial_system(messages, system_instruction="Be helpful.")

        self.assertIsNone(content)
        self.assertEqual(len(messages), 2)  # not popped
        self.assertEqual(messages[0]["role"], "developer")  # unchanged by helper

    def test_single_system_message_becomes_user(self):
        """Single system message is converted to user instead of extracting (empty prevention)."""
        messages = [
            {"role": "system", "content": "Be helpful."},
        ]
        content = self.adapter._extract_initial_system(messages, system_instruction=None)

        self.assertIsNone(content)
        self.assertEqual(len(messages), 1)  # not popped
        self.assertEqual(messages[0]["role"], "user")

    def test_single_system_message_with_system_instruction_warns(self):
        """Single system message + system_instruction still warns even though content isn't extracted."""
        messages = [
            {"role": "system", "content": "Be helpful."},
        ]

        adapter = OpenAILLMAdapter()
        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            content = adapter._extract_initial_system(messages, system_instruction="Be concise.")
            mock_logger.warning.assert_called_once()

        self.assertIsNone(content)
        self.assertEqual(messages[0]["role"], "user")

    def test_non_system_message_ignored(self):
        """Non-system/developer first message is ignored."""
        messages = [
            {"role": "user", "content": "Hello"},
        ]
        content = self.adapter._extract_initial_system(messages, system_instruction=None)

        self.assertIsNone(content)
        self.assertEqual(len(messages), 1)

    def test_empty_messages(self):
        """Empty messages list returns None."""
        messages = []
        content = self.adapter._extract_initial_system(messages, system_instruction=None)

        self.assertIsNone(content)

    def test_resolve_both_system_discard(self):
        """Resolve with discard=True: system_instruction wins, warns."""
        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            result = self.adapter._resolve_system_instruction(
                "from context", "from settings", discard_context_system=True
            )
            mock_logger.warning.assert_called_once()

        self.assertEqual(result, "from settings")

    def test_resolve_both_system_keep(self):
        """Resolve with discard=False: warns but returns system_instruction."""
        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            result = self.adapter._resolve_system_instruction(
                "from context", "from settings", discard_context_system=False
            )
            mock_logger.warning.assert_called_once()

        self.assertEqual(result, "from settings")

    def test_resolve_only_system_instruction(self):
        """Only system_instruction: returns it, no warning."""
        with patch("pipecat.adapters.base_llm_adapter.logger") as mock_logger:
            result = self.adapter._resolve_system_instruction(
                None, "from settings", discard_context_system=True
            )
            mock_logger.warning.assert_not_called()

        self.assertEqual(result, "from settings")

    def test_resolve_only_context_system_discard(self):
        """Only context system (discard=True): returns it."""
        result = self.adapter._resolve_system_instruction(
            "from context", None, discard_context_system=True
        )

        self.assertEqual(result, "from context")

    def test_resolve_only_context_system_keep(self):
        """Only context system (discard=False): returns None (already in messages)."""
        result = self.adapter._resolve_system_instruction(
            "from context", None, discard_context_system=False
        )

        self.assertIsNone(result)


if __name__ == "__main__":
    unittest.main()
