import os
from typing import List

import pytest
from openai import OpenAIError

from haystack.components.generators import GPTGenerator
from haystack.components.generators.utils import default_streaming_callback
from haystack.dataclasses import StreamingChunk, ChatMessage


class TestGPTGenerator:
    def test_init_default(self):
        component = GPTGenerator(api_key="test-api-key")
        assert component.client.api_key == "test-api-key"
        assert component.model_name == "gpt-3.5-turbo"
        assert component.streaming_callback is None
        assert not component.generation_kwargs

    def test_init_fail_wo_api_key(self, monkeypatch):
        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
        with pytest.raises(OpenAIError):
            GPTGenerator()

    def test_init_with_parameters(self):
        component = GPTGenerator(
            api_key="test-api-key",
            model_name="gpt-4",
            streaming_callback=default_streaming_callback,
            api_base_url="test-base-url",
            generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
        )
        assert component.client.api_key == "test-api-key"
        assert component.model_name == "gpt-4"
        assert component.streaming_callback is default_streaming_callback
        assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}

    def test_to_dict_default(self):
        component = GPTGenerator(api_key="test-api-key")
        data = component.to_dict()
        assert data == {
            "type": "haystack.components.generators.openai.GPTGenerator",
            "init_parameters": {
                "model_name": "gpt-3.5-turbo",
                "streaming_callback": None,
                "system_prompt": None,
                "api_base_url": None,
                "generation_kwargs": {},
            },
        }

    def test_to_dict_with_parameters(self):
        component = GPTGenerator(
            api_key="test-api-key",
            model_name="gpt-4",
            streaming_callback=default_streaming_callback,
            api_base_url="test-base-url",
            generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
        )
        data = component.to_dict()
        assert data == {
            "type": "haystack.components.generators.openai.GPTGenerator",
            "init_parameters": {
                "model_name": "gpt-4",
                "system_prompt": None,
                "api_base_url": "test-base-url",
                "streaming_callback": "haystack.components.generators.utils.default_streaming_callback",
                "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
            },
        }

    def test_to_dict_with_lambda_streaming_callback(self):
        component = GPTGenerator(
            api_key="test-api-key",
            model_name="gpt-4",
            streaming_callback=lambda x: x,
            api_base_url="test-base-url",
            generation_kwargs={"max_tokens": 10, "some_test_param": "test-params"},
        )
        data = component.to_dict()
        assert data == {
            "type": "haystack.components.generators.openai.GPTGenerator",
            "init_parameters": {
                "model_name": "gpt-4",
                "system_prompt": None,
                "api_base_url": "test-base-url",
                "streaming_callback": "test_openai.<lambda>",
                "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
            },
        }

    def test_from_dict(self, monkeypatch):
        monkeypatch.setenv("OPENAI_API_KEY", "fake-api-key")
        data = {
            "type": "haystack.components.generators.openai.GPTGenerator",
            "init_parameters": {
                "model_name": "gpt-4",
                "system_prompt": None,
                "api_base_url": "test-base-url",
                "streaming_callback": "haystack.components.generators.utils.default_streaming_callback",
                "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
            },
        }
        component = GPTGenerator.from_dict(data)
        assert component.model_name == "gpt-4"
        assert component.streaming_callback is default_streaming_callback
        assert component.api_base_url == "test-base-url"
        assert component.generation_kwargs == {"max_tokens": 10, "some_test_param": "test-params"}

    def test_from_dict_fail_wo_env_var(self, monkeypatch):
        monkeypatch.delenv("OPENAI_API_KEY", raising=False)
        data = {
            "type": "haystack.components.generators.openai.GPTGenerator",
            "init_parameters": {
                "model_name": "gpt-4",
                "api_base_url": "test-base-url",
                "streaming_callback": "haystack.components.generators.utils.default_streaming_callback",
                "generation_kwargs": {"max_tokens": 10, "some_test_param": "test-params"},
            },
        }
        with pytest.raises(OpenAIError):
            GPTGenerator.from_dict(data)

    def test_run(self, mock_chat_completion):
        component = GPTGenerator(api_key="test-api-key")
        response = component.run("What's Natural Language Processing?")

        # check that the component returns the correct ChatMessage response
        assert isinstance(response, dict)
        assert "replies" in response
        assert isinstance(response["replies"], list)
        assert len(response["replies"]) == 1
        assert [isinstance(reply, str) for reply in response["replies"]]

    def test_run_with_params_streaming(self, mock_chat_completion_chunk):
        streaming_callback_called = False

        def streaming_callback(chunk: StreamingChunk) -> None:
            nonlocal streaming_callback_called
            streaming_callback_called = True

        component = GPTGenerator(streaming_callback=streaming_callback)
        response = component.run("Come on, stream!")

        # check we called the streaming callback
        assert streaming_callback_called

        # check that the component still returns the correct response
        assert isinstance(response, dict)
        assert "replies" in response
        assert isinstance(response["replies"], list)
        assert len(response["replies"]) == 1
        assert "Hello" in response["replies"][0]  # see mock_chat_completion_chunk

    def test_run_with_params(self, mock_chat_completion):
        component = GPTGenerator(api_key="test-api-key", generation_kwargs={"max_tokens": 10, "temperature": 0.5})
        response = component.run("What's Natural Language Processing?")

        # check that the component calls the OpenAI API with the correct parameters
        _, kwargs = mock_chat_completion.call_args
        assert kwargs["max_tokens"] == 10
        assert kwargs["temperature"] == 0.5

        # check that the component returns the correct response
        assert isinstance(response, dict)
        assert "replies" in response
        assert isinstance(response["replies"], list)
        assert len(response["replies"]) == 1
        assert [isinstance(reply, str) for reply in response["replies"]]

    def test_check_abnormal_completions(self, caplog):
        component = GPTGenerator(api_key="test-api-key")

        # underlying implementation uses ChatMessage objects so we have to use them here
        messages: List[ChatMessage] = []
        for i, _ in enumerate(range(4)):
            message = ChatMessage.from_assistant("Hello")
            metadata = {"finish_reason": "content_filter" if i % 2 == 0 else "length", "index": i}
            message.meta.update(metadata)
            messages.append(message)

        for m in messages:
            component._check_finish_reason(m)

        # check truncation warning
        message_template = (
            "The completion for index {index} has been truncated before reaching a natural stopping point. "
            "Increase the max_tokens parameter to allow for longer completions."
        )

        for index in [1, 3]:
            assert caplog.records[index].message == message_template.format(index=index)

        # check content filter warning
        message_template = "The completion for index {index} has been truncated due to the content filter."
        for index in [0, 2]:
            assert caplog.records[index].message == message_template.format(index=index)

    @pytest.mark.skipif(
        not os.environ.get("OPENAI_API_KEY", None),
        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
    )
    @pytest.mark.integration
    def test_live_run(self):
        component = GPTGenerator(api_key=os.environ.get("OPENAI_API_KEY"))
        results = component.run("What's the capital of France?")
        assert len(results["replies"]) == 1
        assert len(results["meta"]) == 1
        response: str = results["replies"][0]
        assert "Paris" in response

        metadata = results["meta"][0]
        assert "gpt-3.5" in metadata["model"]
        assert metadata["finish_reason"] == "stop"

        assert "usage" in metadata
        assert "prompt_tokens" in metadata["usage"] and metadata["usage"]["prompt_tokens"] > 0
        assert "completion_tokens" in metadata["usage"] and metadata["usage"]["completion_tokens"] > 0
        assert "total_tokens" in metadata["usage"] and metadata["usage"]["total_tokens"] > 0

    @pytest.mark.skipif(
        not os.environ.get("OPENAI_API_KEY", None),
        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
    )
    @pytest.mark.integration
    def test_live_run_wrong_model(self):
        component = GPTGenerator(model_name="something-obviously-wrong", api_key=os.environ.get("OPENAI_API_KEY"))
        with pytest.raises(OpenAIError):
            component.run("Whatever")

    @pytest.mark.skipif(
        not os.environ.get("OPENAI_API_KEY", None),
        reason="Export an env var called OPENAI_API_KEY containing the OpenAI API key to run this test.",
    )
    @pytest.mark.integration
    def test_live_run_streaming(self):
        class Callback:
            def __init__(self):
                self.responses = ""
                self.counter = 0

            def __call__(self, chunk: StreamingChunk) -> None:
                self.counter += 1
                self.responses += chunk.content if chunk.content else ""

        callback = Callback()
        component = GPTGenerator(os.environ.get("OPENAI_API_KEY"), streaming_callback=callback)
        results = component.run("What's the capital of France?")

        assert len(results["replies"]) == 1
        assert len(results["meta"]) == 1
        response: str = results["replies"][0]
        assert "Paris" in response

        metadata = results["meta"][0]

        assert "gpt-3.5" in metadata["model"]
        assert metadata["finish_reason"] == "stop"

        # unfortunately, the usage is not available for streaming calls
        # we keep the key in the metadata for compatibility
        assert "usage" in metadata and len(metadata["usage"]) == 0

        assert callback.counter > 1
        assert "Paris" in callback.responses