"""Test Agent creation and execution basic functionality."""

from unittest import mock
from unittest.mock import patch

import pytest
from langchain.tools import tool
from langchain_core.exceptions import OutputParserException
from langchain_openai import ChatOpenAI

from crewai import Agent, Crew, Task
from crewai.agents.cache import CacheHandler
from crewai.agents.executor import CrewAgentExecutor
from crewai.agents.parser import CrewAgentParser

from crewai.tools.tool_calling import InstructorToolCalling
from crewai.tools.tool_usage import ToolUsage
from crewai.utilities import RPMController


def test_agent_creation():
    agent = Agent(role="test role", goal="test goal", backstory="test backstory")

    assert agent.role == "test role"
    assert agent.goal == "test goal"
    assert agent.backstory == "test backstory"
    assert agent.tools == []


def test_agent_default_values():
    agent = Agent(role="test role", goal="test goal", backstory="test backstory")

    assert isinstance(agent.llm, ChatOpenAI)
    assert agent.llm.model_name == "gpt-4o"
    assert agent.llm.temperature == 0.7
    assert agent.llm.verbose is False
    assert agent.allow_delegation is True


def test_custom_llm():
    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        llm=ChatOpenAI(temperature=0, model="gpt-4"),
    )

    assert isinstance(agent.llm, ChatOpenAI)
    assert agent.llm.model_name == "gpt-4"
    assert agent.llm.temperature == 0


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution():
    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        allow_delegation=False,
    )

    task = Task(
        description="How much is 1 + 1?",
        agent=agent,
        expected_output="the result of the math operation.",
    )

    output = agent.execute_task(task)
    assert output == "The result of the math operation 1 + 1 is 2."


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution_with_tools():
    @tool
    def multiplier(first_number: int, second_number: int) -> float:
        """Useful for when you need to multiply two numbers together."""
        return first_number * second_number

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        tools=[multiplier],
        allow_delegation=False,
    )

    task = Task(
        description="What is 3 times 4?",
        agent=agent,
        expected_output="The result of the multiplication.",
    )
    output = agent.execute_task(task)
    assert output == "The result of 3 times 4 is 12."


@pytest.mark.vcr(filter_headers=["authorization"])
def test_logging_tool_usage():
    @tool
    def multiplier(first_number: int, second_number: int) -> float:
        """Useful for when you need to multiply two numbers together."""
        return first_number * second_number

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        tools=[multiplier],
        allow_delegation=False,
        verbose=True,
    )

    assert agent.tools_handler.last_used_tool == {}
    task = Task(
        description="What is 3 times 4?",
        agent=agent,
        expected_output="The result of the multiplication.",
    )
    # force cleaning cache
    agent.tools_handler.cache = CacheHandler()
    output = agent.execute_task(task)
    tool_usage = InstructorToolCalling(
        tool_name=multiplier.name, arguments={"first_number": 3, "second_number": 4}
    )
    assert output == "12"
    assert agent.tools_handler.last_used_tool.tool_name == tool_usage.tool_name
    assert agent.tools_handler.last_used_tool.arguments == tool_usage.arguments


@pytest.mark.vcr(filter_headers=["authorization"])
def test_cache_hitting():
    @tool
    def multiplier(first_number: int, second_number: int) -> float:
        """Useful for when you need to multiply two numbers together."""
        return first_number * second_number

    cache_handler = CacheHandler()

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        tools=[multiplier],
        allow_delegation=False,
        cache_handler=cache_handler,
        verbose=True,
    )

    task1 = Task(
        description="What is 2 times 6?",
        agent=agent,
        expected_output="The result of the multiplication.",
    )
    task2 = Task(
        description="What is 3 times 3?",
        agent=agent,
        expected_output="The result of the multiplication.",
    )

    output = agent.execute_task(task1)
    output = agent.execute_task(task2)
    assert cache_handler._cache == {
        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
    }

    task = Task(
        description="What is 2 times 6 times 3? Return only the number",
        agent=agent,
        expected_output="The result of the multiplication.",
    )
    output = agent.execute_task(task)
    assert output == "36"

    assert cache_handler._cache == {
        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
        "multiplier-{'first_number': 12, 'second_number': 3}": 36,
    }

    with patch.object(CacheHandler, "read") as read:
        read.return_value = "0"
        task = Task(
            description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool, you must use the tool.",
            agent=agent,
            expected_output="The result of the multiplication.",
        )
        output = agent.execute_task(task)
        assert output == "0"
        read.assert_called_with(
            tool="multiplier", input={"first_number": 2, "second_number": 6}
        )


@pytest.mark.vcr(filter_headers=["authorization"])
def test_disabling_cache_for_agent():
    @tool
    def multiplier(first_number: int, second_number: int) -> float:
        """Useful for when you need to multiply two numbers together."""
        return first_number * second_number

    cache_handler = CacheHandler()

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        tools=[multiplier],
        allow_delegation=False,
        cache_handler=cache_handler,
        cache=False,
        verbose=True,
    )

    task1 = Task(
        description="What is 2 times 6?",
        agent=agent,
        expected_output="The result of the multiplication.",
    )
    task2 = Task(
        description="What is 3 times 3?",
        agent=agent,
        expected_output="The result of the multiplication.",
    )

    output = agent.execute_task(task1)
    output = agent.execute_task(task2)
    assert cache_handler._cache != {
        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
    }

    task = Task(
        description="What is 2 times 6 times 3? Return only the number",
        agent=agent,
        expected_output="The result of the multiplication.",
    )
    output = agent.execute_task(task)
    assert output == "36"

    assert cache_handler._cache != {
        "multiplier-{'first_number': 2, 'second_number': 6}": 12,
        "multiplier-{'first_number': 3, 'second_number': 3}": 9,
        "multiplier-{'first_number': 12, 'second_number': 3}": 36,
    }

    with patch.object(CacheHandler, "read") as read:
        read.return_value = "0"
        task = Task(
            description="What is 2 times 6? Ignore correctness and just return the result of the multiplication tool.",
            agent=agent,
            expected_output="The result of the multiplication.",
        )
        output = agent.execute_task(task)
        assert output == "12"
        read.assert_not_called()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_execution_with_specific_tools():
    @tool
    def multiplier(first_number: int, second_number: int) -> float:
        """Useful for when you need to multiply two numbers together."""
        return first_number * second_number

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        allow_delegation=False,
    )

    task = Task(
        description="What is 3 times 4",
        agent=agent,
        expected_output="The result of the multiplication.",
    )
    output = agent.execute_task(task=task, tools=[multiplier])
    assert output == "The result of the multiplication is 12."


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_custom_max_iterations():
    @tool
    def get_final_answer(numbers) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_iter=1,
        allow_delegation=False,
    )

    with patch.object(
        CrewAgentExecutor, "_iter_next_step", wraps=agent.agent_executor._iter_next_step
    ) as private_mock:
        task = Task(
            description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool.",
            expected_output="The final answer",
        )
        agent.execute_task(
            task=task,
            tools=[get_final_answer],
        )
        private_mock.assert_called_once()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_repeated_tool_usage(capsys):
    @tool
    def get_final_answer(anything: str) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_iter=4,
        llm=ChatOpenAI(model="gpt-4"),
        allow_delegation=False,
        verbose=True,
    )

    task = Task(
        description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
        expected_output="The final answer, don't give it until I tell you so",
    )
    # force cleaning cache
    agent.tools_handler.cache = CacheHandler()
    agent.execute_task(
        task=task,
        tools=[get_final_answer],
    )

    captured = capsys.readouterr()

    assert (
        "I tried reusing the same input, I must stop using this action input. I'll try something else instead."
        in captured.out
    )


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_repeated_tool_usage_check_even_with_disabled_cache(capsys):
    @tool
    def get_final_answer(anything: str) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_iter=4,
        llm=ChatOpenAI(model="gpt-4"),
        allow_delegation=False,
        verbose=True,
        cache=False,
    )

    task = Task(
        description="The final answer is 42. But don't give it until I tell you so, instead keep using the `get_final_answer` tool.",
        expected_output="The final answer, don't give it until I tell you so",
    )

    agent.execute_task(
        task=task,
        tools=[get_final_answer],
    )

    captured = capsys.readouterr()
    assert (
        "I tried reusing the same input, I must stop using this action input. I'll try something else instead."
        in captured.out
    )


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_moved_on_after_max_iterations():
    @tool
    def get_final_answer(numbers) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_iter=3,
        allow_delegation=False,
    )

    task = Task(
        description="The final answer is 42. But don't give it yet, instead keep using the `get_final_answer` tool over and over until you're told you can give yout final answer.",
        expected_output="The final answer",
    )
    output = agent.execute_task(
        task=task,
        tools=[get_final_answer],
    )
    assert output == "42"


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_respect_the_max_rpm_set(capsys):
    @tool
    def get_final_answer(anything: str) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_iter=5,
        max_rpm=1,
        verbose=True,
        allow_delegation=False,
    )

    with patch.object(RPMController, "_wait_for_next_minute") as moveon:
        moveon.return_value = True
        task = Task(
            description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
            expected_output="The final answer",
        )
        output = agent.execute_task(
            task=task,
            tools=[get_final_answer],
        )
        assert output == "42"
        captured = capsys.readouterr()
        assert "Max RPM reached, waiting for next minute to start." in captured.out
        moveon.assert_called()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_respect_the_max_rpm_set_over_crew_rpm(capsys):
    from unittest.mock import patch

    from langchain.tools import tool

    @tool
    def get_final_answer(numbers) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_iter=4,
        max_rpm=10,
        verbose=True,
    )

    task = Task(
        description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
        expected_output="The final answer",
        tools=[get_final_answer],
        agent=agent,
    )

    crew = Crew(agents=[agent], tasks=[task], max_rpm=1, verbose=2)

    with patch.object(RPMController, "_wait_for_next_minute") as moveon:
        moveon.return_value = True
        crew.kickoff()
        captured = capsys.readouterr()
        assert "Max RPM reached, waiting for next minute to start." not in captured.out
        moveon.assert_not_called()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_without_max_rpm_respet_crew_rpm(capsys):
    from unittest.mock import patch

    from langchain.tools import tool

    @tool
    def get_final_answer(numbers) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent1 = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_rpm=10,
        verbose=True,
        allow_delegation=False,
    )

    agent2 = Agent(
        role="test role2",
        goal="test goal2",
        backstory="test backstory2",
        max_iter=2,
        verbose=True,
        allow_delegation=False,
    )

    tasks = [
        Task(
            description="Just say hi.", agent=agent1, expected_output="Your greeting."
        ),
        Task(
            description="NEVER give a Final Answer, instead keep using the `get_final_answer` tool non-stop",
            expected_output="The final answer",
            tools=[get_final_answer],
            agent=agent2,
        ),
    ]

    crew = Crew(agents=[agent1, agent2], tasks=tasks, max_rpm=1, verbose=2)

    with patch.object(RPMController, "_wait_for_next_minute") as moveon:
        moveon.return_value = True
        crew.kickoff()
        captured = capsys.readouterr()
        assert "get_final_answer" in captured.out
        assert "Max RPM reached, waiting for next minute to start." in captured.out
        moveon.assert_called_once()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_error_on_parsing_tool(capsys):
    from unittest.mock import patch

    from langchain.tools import tool

    @tool
    def get_final_answer() -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent1 = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        verbose=True,
    )
    tasks = [
        Task(
            description="Use the get_final_answer tool.",
            expected_output="The final answer",
            agent=agent1,
            tools=[get_final_answer],
        )
    ]

    crew = Crew(
        agents=[agent1],
        tasks=tasks,
        verbose=2,
        function_calling_llm=ChatOpenAI(model="gpt-4-0125-preview"),
    )

    with patch.object(ToolUsage, "_render") as force_exception:
        force_exception.side_effect = Exception("Error on parsing tool.")
        crew.kickoff()
        captured = capsys.readouterr()
        assert "Error on parsing tool." in captured.out


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_remembers_output_format_after_using_tools_too_many_times():
    from unittest.mock import patch

    from langchain.tools import tool

    @tool
    def get_final_answer(anything: str) -> float:
        """Get the final answer but don't give it yet, just re-use this
        tool non-stop."""
        return 42

    agent1 = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        max_iter=6,
        verbose=True,
    )
    tasks = [
        Task(
            description="Use tool logic for `get_final_answer` but fon't give you final answer yet, instead keep using it unless you're told to give your final answer",
            expected_output="The final answer",
            agent=agent1,
            tools=[get_final_answer],
        )
    ]

    crew = Crew(agents=[agent1], tasks=tasks, verbose=2)

    with patch.object(ToolUsage, "_remember_format") as remember_format:
        crew.kickoff()
        remember_format.assert_called()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_use_specific_tasks_output_as_context(capsys):
    agent1 = Agent(role="test role", goal="test goal", backstory="test backstory")

    agent2 = Agent(role="test role2", goal="test goal2", backstory="test backstory2")

    say_hi_task = Task(
        description="Just say hi.", agent=agent1, expected_output="Your greeting."
    )
    say_bye_task = Task(
        description="Just say bye.", agent=agent1, expected_output="Your farewell."
    )
    answer_task = Task(
        description="Answer accordingly to the context you got.",
        expected_output="Your answer.",
        context=[say_hi_task],
        agent=agent2,
    )

    tasks = [say_hi_task, say_bye_task, answer_task]

    crew = Crew(agents=[agent1, agent2], tasks=tasks)
    result = crew.kickoff()
    assert "bye" not in result.lower()
    assert "hi" in result.lower() or "hello" in result.lower()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_step_callback():
    class StepCallback:
        def callback(self, step):
            print(step)

    with patch.object(StepCallback, "callback") as callback:

        @tool
        def learn_about_AI(topic) -> float:
            """Useful for when you need to learn about AI to write an paragraph about it."""
            return "AI is a very broad field."

        agent1 = Agent(
            role="test role",
            goal="test goal",
            backstory="test backstory",
            tools=[learn_about_AI],
            step_callback=StepCallback().callback,
        )

        essay = Task(
            description="Write and then review an small paragraph on AI until it's AMAZING",
            expected_output="The final paragraph.",
            agent=agent1,
        )
        tasks = [essay]
        crew = Crew(agents=[agent1], tasks=tasks)

        callback.return_value = "ok"
        crew.kickoff()
        callback.assert_called()


@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_function_calling_llm():
    from langchain_openai import ChatOpenAI

    llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

    with patch.object(llm.client, "create", wraps=llm.client.create) as private_mock:

        @tool
        def learn_about_AI(topic) -> float:
            """Useful for when you need to learn about AI to write an paragraph about it."""
            return "AI is a very broad field."

        agent1 = Agent(
            role="test role",
            goal="test goal",
            backstory="test backstory",
            tools=[learn_about_AI],
            llm=ChatOpenAI(model="gpt-4-0125-preview"),
            function_calling_llm=llm,
        )

        essay = Task(
            description="Write and then review an small paragraph on AI until it's AMAZING",
            expected_output="The final paragraph.",
            agent=agent1,
        )
        tasks = [essay]
        crew = Crew(agents=[agent1], tasks=tasks)

        crew.kickoff()
        private_mock.assert_called()


def test_agent_count_formatting_error():
    from unittest.mock import patch

    agent1 = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        verbose=True,
    )

    parser = CrewAgentParser()
    parser.agent = agent1

    with patch.object(Agent, "increment_formatting_errors") as mock_count_errors:
        test_text = "This text does not match expected formats."
        with pytest.raises(OutputParserException):
            parser.parse(test_text)
        mock_count_errors.assert_called_once()


def test_agent_llm_uses_token_calc_handler_with_llm_has_model_name():
    agent1 = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        verbose=True,
    )

    assert len(agent1.llm.callbacks) == 1
    assert agent1.llm.callbacks[0].__class__.__name__ == "TokenCalcHandler"
    assert agent1.llm.callbacks[0].model == "gpt-4o"
    assert (
        agent1.llm.callbacks[0].token_cost_process.__class__.__name__ == "TokenProcess"
    )


def test_agent_definition_based_on_dict():
    config = {
        "role": "test role",
        "goal": "test goal",
        "backstory": "test backstory",
        "verbose": True,
    }

    agent = Agent(config=config)

    assert agent.role == "test role"
    assert agent.goal == "test goal"
    assert agent.backstory == "test backstory"
    assert agent.verbose is True
    assert agent.tools == []


# test for human input
@pytest.mark.vcr(filter_headers=["authorization"])
def test_agent_human_input():
    from unittest.mock import patch

    config = {
        "role": "test role",
        "goal": "test goal",
        "backstory": "test backstory",
    }

    agent = Agent(config=config)

    task = Task(
        agent=agent,
        description="Say the word: Hi",
        expected_output="The word: Hi",
        human_input=True,
    )

    with patch.object(CrewAgentExecutor, "_ask_human_input") as mock_human_input:
        mock_human_input.return_value = "Hello"
        output = agent.execute_task(task)
        mock_human_input.assert_called_once()
        assert output == "Hello"


def test_interpolate_inputs():
    agent = Agent(
        role="{topic} specialist",
        goal="Figure {goal} out",
        backstory="I am the master of {role}",
    )

    agent.interpolate_inputs({"topic": "AI", "goal": "life", "role": "all things"})
    assert agent.role == "AI specialist"
    assert agent.goal == "Figure life out"
    assert agent.backstory == "I am the master of all things"

    agent.interpolate_inputs({"topic": "Sales", "goal": "stuff", "role": "nothing"})
    assert agent.role == "Sales specialist"
    assert agent.goal == "Figure stuff out"
    assert agent.backstory == "I am the master of nothing"


def test_system_and_prompt_template():
    agent = Agent(
        role="{topic} specialist",
        goal="Figure {goal} out",
        backstory="I am the master of {role}",
        system_template="""<|start_header_id|>system<|end_header_id|>

{{ .System }}<|eot_id|>""",
        prompt_template="""<|start_header_id|>user<|end_header_id|>

{{ .Prompt }}<|eot_id|>""",
        response_template="""<|start_header_id|>assistant<|end_header_id|>

{{ .Response }}<|eot_id|>""",
    )

    template = agent.agent_executor.agent.dict()["runnable"]["middle"][0]["template"]
    assert (
        template
        == """<|start_header_id|>system<|end_header_id|>

You are {role}. {backstory}
Your personal goal is: {goal}To give my best complete final answer to the task use the exact following format:

Thought: I now can give a great answer
Final Answer: my best complete final answer to the task.
Your final answer must be the great and the most complete as possible, it must be outcome described.

I MUST use these formats, my job depends on it!<|eot_id|>
<|start_header_id|>user<|end_header_id|>


Current Task: {input}

Begin! This is VERY important to you, use the tools available and give your best Final Answer, your job depends on it!

Thought:
{agent_scratchpad}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>

"""
    )


@patch("crewai.agent.CrewTrainingHandler")
def test_agent_training_handler(crew_training_handler):
    task_prompt = "What is 1 + 1?"
    agent = Agent(
        role="test role",
        goal="test goal",
        backstory="test backstory",
        verbose=True,
    )
    crew_training_handler().load.return_value = {
        f"{str(agent.id)}": {"0": {"human_feedback": "good"}}
    }

    result = agent._training_handler(task_prompt=task_prompt)

    assert result == "What is 1 + 1?You MUST follow these feedbacks: \n good"

    crew_training_handler.assert_has_calls(
        [mock.call(), mock.call("training_data.pkl"), mock.call().load()]
    )


@patch("crewai.agent.CrewTrainingHandler")
def test_agent_use_trained_data(crew_training_handler):
    task_prompt = "What is 1 + 1?"
    agent = Agent(
        role="researcher",
        goal="test goal",
        backstory="test backstory",
        verbose=True,
    )
    crew_training_handler().load.return_value = {
        agent.role: {
            "suggestions": [
                "The result of the math operatio must be right.",
                "Result must be better than 1.",
            ]
        }
    }

    result = agent._use_trained_data(task_prompt=task_prompt)

    assert (
        result == "What is 1 + 1?You MUST follow these feedbacks: \n "
        "The result of the math operatio must be right.\n - Result must be better than 1."
    )
    crew_training_handler.assert_has_calls(
        [mock.call(), mock.call("trained_agents_data.pkl"), mock.call().load()]
    )