import os
import sys


import pytest

# sys.path.insert(
#     0, os.path.abspath("../..")
# ) # noqa
# )  # Adds the parent directory to the system path

import litellm
from base_llm_unit_tests import BaseLLMChatTest
from litellm.llms.groq.chat.transformation import (
    GroqChatConfig,
    GroqChatCompletionStreamingHandler,
)


class TestGroq(BaseLLMChatTest):
    def get_base_completion_call_args(self) -> dict:
        return {
            "model": "groq/openai/gpt-oss-120b",
        }

    def test_tool_call_no_arguments(self, tool_call_no_arguments):
        """Test that tool calls with no arguments is translated correctly. Relevant issue: https://github.com/BerriAI/litellm/issues/6833"""
        pass

    def test_tool_call_with_empty_enum_property(self):
        pass

    @pytest.mark.parametrize(
        "model",
        ["groq/qwen/qwen3-32b", "groq/openai/gpt-oss-20b", "groq/openai/gpt-oss-120b"],
    )
    def test_reasoning_effort_in_supported_params(self, model):
        """Test that reasoning_effort is in the list of supported parameters for Groq"""
        supported_params = GroqChatConfig().get_supported_openai_params(model=model)
        assert "reasoning_effort" in supported_params


class TestGroqStructuredOutputs:
    """
    Tests for Groq structured outputs handling.
    Related issues:
    - https://github.com/BerriAI/litellm/issues/11001
    - https://github.com/openai/openai-agents-python/issues/2140
    """

    def test_structured_output_with_tools_raises_error_for_non_native_models(self):
        """
        Test that using structured outputs + tools with models that don't support
        native json_schema raises a clear error message.

        Groq does not support structured outputs + tools together.
        See: https://console.groq.com/docs/structured-outputs
        "Streaming and tool use are not currently supported with Structured Outputs"
        """
        config = GroqChatConfig()

        # Model that doesn't support native json_schema
        model = "llama-3.3-70b-versatile"

        non_default_params = {
            "response_format": {
                "type": "json_schema",
                "json_schema": {
                    "name": "test",
                    "schema": {
                        "type": "object",
                        "properties": {"name": {"type": "string"}},
                        "required": ["name"],
                    },
                },
            },
            "tools": [
                {
                    "type": "function",
                    "function": {
                        "name": "get_weather",
                        "parameters": {"type": "object", "properties": {}},
                    },
                }
            ],
        }

        with pytest.raises(litellm.BadRequestError) as exc_info:
            config.map_openai_params(
                non_default_params=non_default_params,
                optional_params={},
                model=model,
                drop_params=False,
            )

        assert "does not support native structured outputs" in str(exc_info.value)
        assert "incompatible with user-provided tools" in str(exc_info.value)

    def test_structured_output_without_tools_uses_workaround_for_non_native_models(
        self,
    ):
        """
        Test that structured outputs without tools works using the json_tool_call workaround
        for models that don't support native json_schema.
        """
        config = GroqChatConfig()

        model = "llama-3.3-70b-versatile"

        non_default_params = {
            "response_format": {
                "type": "json_schema",
                "json_schema": {
                    "name": "test",
                    "schema": {
                        "type": "object",
                        "properties": {"name": {"type": "string"}},
                        "required": ["name"],
                    },
                },
            }
        }

        result = config.map_openai_params(
            non_default_params=non_default_params,
            optional_params={},
            model=model,
            drop_params=False,
        )

        # Should use the workaround (json_tool_call)
        assert "tools" in result
        assert len(result["tools"]) == 1
        assert result["tools"][0]["function"]["name"] == "json_tool_call"
        assert result["tool_choice"]["function"]["name"] == "json_tool_call"
        assert result.get("json_mode") is True

    def test_structured_output_passes_through_for_native_models(self):
        """
        Test that structured outputs pass through directly for models that
        support native json_schema (e.g., gpt-oss-120b).
        """
        config = GroqChatConfig()

        # Model that supports native json_schema
        model = "openai/gpt-oss-120b"

        non_default_params = {
            "response_format": {
                "type": "json_schema",
                "json_schema": {
                    "name": "test",
                    "schema": {
                        "type": "object",
                        "properties": {"name": {"type": "string"}},
                        "required": ["name"],
                    },
                },
            }
        }

        result = config.map_openai_params(
            non_default_params=non_default_params,
            optional_params={},
            model=model,
            drop_params=False,
        )

        # Should NOT use the workaround - response_format should pass through
        # The workaround sets json_mode=True, so if it's not set, we know it passed through
        assert result.get("json_mode") is not True
        # Should not have the json_tool_call tool
        if "tools" in result:
            tool_names = [t.get("function", {}).get("name") for t in result["tools"]]
            assert "json_tool_call" not in tool_names


class TestGroqReasoning:
    """
    Tests for Groq reasoning field mapping.

    Groq returns 'reasoning' field in delta, but LiteLLM expects 'reasoning_content'.
    """

    def test_reasoning_field_mapping_in_streaming_chunks(self):
        """
        Test that Groq's 'reasoning' field in streaming chunks is properly mapped
        to LiteLLM's 'reasoning_content' field.
        """
        handler = GroqChatCompletionStreamingHandler(
            streaming_response=None, sync_stream=True
        )

        # Simulate a chunk with reasoning field as returned by Groq
        groq_chunk = {
            "id": "chatcmpl-test",
            "object": "chat.completion.chunk",
            "created": 1769511767,
            "model": "qwen/qwen3-32b",
            "choices": [
                {
                    "delta": {
                        "reasoning": "This is reasoning content",
                        "role": None,
                    },
                    "finish_reason": None,
                    "index": 0,
                }
            ],
        }

        # Parse the chunk
        parsed_chunk = handler.chunk_parser(groq_chunk)

        # Verify that reasoning was mapped to reasoning_content
        assert (
            parsed_chunk.choices[0].delta.reasoning_content
            == "This is reasoning content"
        )
        # Verify that the original 'reasoning' field was removed
        assert not hasattr(parsed_chunk.choices[0].delta, "reasoning")

    def test_reasoning_field_not_present(self):
        """
        Test that chunks without reasoning field still work correctly.
        """
        handler = GroqChatCompletionStreamingHandler(
            streaming_response=None, sync_stream=True
        )

        # Simulate a chunk without reasoning field
        groq_chunk = {
            "id": "chatcmpl-test",
            "object": "chat.completion.chunk",
            "created": 1769511767,
            "model": "qwen/qwen3-32b",
            "choices": [
                {
                    "delta": {
                        "content": "Regular content",
                        "role": "assistant",
                    },
                    "finish_reason": None,
                    "index": 0,
                }
            ],
        }

        # Parse the chunk
        parsed_chunk = handler.chunk_parser(groq_chunk)

        # Verify that content is present
        assert parsed_chunk.choices[0].delta.content == "Regular content"
        assert parsed_chunk.choices[0].delta.role == "assistant"
        # Verify that reasoning_content is not set (it should be deleted by Delta.__init__)
        assert not hasattr(parsed_chunk.choices[0].delta, "reasoning_content")

    def test_reasoning_with_tool_calls(self):
        """
        Test that reasoning field is properly mapped even when tool_calls are present.
        """
        handler = GroqChatCompletionStreamingHandler(
            streaming_response=None, sync_stream=True
        )

        # Simulate a chunk with both reasoning and tool_calls
        groq_chunk = {
            "id": "chatcmpl-test",
            "object": "chat.completion.chunk",
            "created": 1769511767,
            "model": "qwen/qwen3-32b",
            "choices": [
                {
                    "delta": {
                        "reasoning": "Reasoning before tool call",
                        "tool_calls": [
                            {
                                "index": 0,
                                "id": "call_123",
                                "function": {
                                    "name": "test_function",
                                    "arguments": "{}",
                                },
                                "type": "function",
                            }
                        ],
                    },
                    "finish_reason": None,
                    "index": 0,
                }
            ],
        }

        # Parse the chunk
        parsed_chunk = handler.chunk_parser(groq_chunk)

        # Verify that reasoning was mapped to reasoning_content
        assert (
            parsed_chunk.choices[0].delta.reasoning_content
            == "Reasoning before tool call"
        )
        # Verify tool_calls are still present
        assert parsed_chunk.choices[0].delta.tool_calls is not None
        assert len(parsed_chunk.choices[0].delta.tool_calls) == 1
        assert (
            parsed_chunk.choices[0].delta.tool_calls[0]["function"]["name"]
            == "test_function"
        )