import json
from unittest.mock import patch

import pytest

from workflows.errors import CyclicDependencyError, WorkflowError
from workflows.executors import (
    create_processed_inputs,
    execute_model_step,
    execute_workflow,
)
from workflows.structs import CallType, InputField, ModelStep, OutputField, Workflow

# Tests for utility functions
lower = str.lower
upper = str.upper


# Tests for create_processed_inputs


def assert_model_step_result(result: dict, expected_result: dict):
    # Verify the results
    assert isinstance(result, dict)
    assert "outputs" in result
    assert "content" in result
    assert "logprob" in result
    assert result["outputs"] == expected_result["outputs"]
    assert result["content"] == expected_result["content"]
    assert result["logprob"] == expected_result["logprob"]


def assert_workflow_output(output: dict, expected_output: dict):
    assert isinstance(output, dict)
    for key in ["final_outputs", "intermediate_outputs", "step_contents", "logprob"]:
        assert key in output
        assert output[key] == expected_output[key]


def test_create_processed_inputs_basic():
    """Test basic input processing without transformations."""
    step = ModelStep(
        id="test_step",
        name="Test Step",
        model="gpt-4",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Test prompt",
        input_fields=[InputField(name="text", description="Input text", variable="input_text")],
        output_fields=[],
    )
    available_vars = {"input_text": "Hello World"}

    result = create_processed_inputs(step, available_vars)
    assert result == {"text": "Hello World"}


def test_create_processed_inputs_with_transformation():
    """Test input processing with transformation functions."""
    step = ModelStep(
        id="test_step",
        name="Test Step",
        model="gpt-4",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Test prompt",
        input_fields=[
            InputField(name="upper_text", description="Uppercase text", variable="input_text", func="upper"),
            InputField(name="lower_text", description="Lowercase text", variable="input_caps", func="lower"),
        ],
        output_fields=[],
    )
    available_vars = {"input_text": "hello", "input_caps": "WORLD"}

    result = create_processed_inputs(step, available_vars)
    assert result == {"upper_text": "HELLO", "lower_text": "world"}


def test_create_processed_inputs_missing_var():
    """Test that appropriate error is raised when a variable is missing."""
    step = ModelStep(
        id="test_step",
        name="Test Step",
        model="gpt-4",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Test prompt",
        input_fields=[InputField(name="text", description="Input text", variable="missing_var")],
        output_fields=[],
    )
    available_vars = {"input_text": "Hello World"}

    with pytest.raises(KeyError):
        create_processed_inputs(step, available_vars)


def test_create_processed_inputs_unknown_func():
    """Test that appropriate error is raised when an unknown function is specified."""
    step = ModelStep(
        id="test_step",
        name="Test Step",
        model="gpt-4",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Test prompt",
        input_fields=[InputField(name="text", description="Input text", variable="input_text", func="unknown_func")],
        output_fields=[],
    )
    available_vars = {"input_text": "Hello World"}

    # This should raise an error when the function isn't found
    with pytest.raises(Exception):
        create_processed_inputs(step, available_vars)


# Tests for execute_model_step


@patch("workflows.executors.completion")
def test_execute_model_step_success(mock_completion):
    """Test successful execution of a model step with mocked litellm response."""
    # Mock the litellm response
    mock_response = {
        "content": json.dumps({"summary": "This is a summary"}),
        "output": {"summary": "This is a summary"},
    }
    mock_completion.return_value = mock_response

    # Create a test step
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    # Execute the step
    result = execute_model_step(step, {"input_text": "Long text to be summarized..."})

    # Verify the results
    assert isinstance(result, dict)
    assert "outputs" in result
    assert "content" in result
    assert "logprob" in result
    assert result["outputs"] == {"summary": "This is a summary"}
    assert result["content"] is None
    assert result["logprob"] is None

    # Verify the litellm call was made correctly
    mock_completion.assert_called_once()
    args, kwargs = mock_completion.call_args
    assert kwargs["model"] == "OpenAI/gpt-3.5-turbo"
    assert "Summarize the text" in kwargs["system"]


@patch("workflows.executors.completion")
def test_execute_model_step_with_full_content(mock_completion):
    """Test execution of a model step with full content returned."""
    # Mock the litellm response
    mock_response = {
        "content": "Full model response content",
        "output": {"summary": "This is a summary"},
    }
    mock_completion.return_value = mock_response

    # Create a test step
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    # Execute the step with return_full_content=True
    result = execute_model_step(step, {"input_text": "Long text to be summarized..."}, return_full_content=True)

    # Verify the results
    assert isinstance(result, dict)
    assert "outputs" in result
    assert "content" in result
    assert "logprob" in result
    assert result["outputs"] == {"summary": "This is a summary"}
    assert result["content"] == "Full model response content"
    assert result["logprob"] is None


@patch("workflows.executors.completion")
def test_execute_model_step_with_logprobs(mock_completion):
    """Test execution of a model step with log probabilities."""
    # Mock the litellm response with log probability
    mock_response = {
        "content": json.dumps({"summary": "This is a summary"}),
        "output": {"summary": "This is a summary"},
        "log_prob": -2.5,
    }
    mock_completion.return_value = mock_response

    # Create a test step
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    # Execute the step with logprobs=True
    result = execute_model_step(step, {"input_text": "Long text to be summarized..."}, logprobs=True)

    # Verify the results
    assert isinstance(result, dict)
    assert "outputs" in result
    assert "content" in result
    assert "logprob" in result
    assert result["outputs"] == {"summary": "This is a summary"}
    assert result["content"] is None
    assert result["logprob"] == -2.5


@patch("workflows.executors.completion")
def test_execute_model_step_error(mock_completion):
    """Test handling of errors in model step execution."""
    # Make litellm raise an exception
    mock_completion.side_effect = Exception("API Error")

    # Create a test step
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    # Execute the step - should raise an exception
    with pytest.raises(Exception):
        execute_model_step(step, {"input_text": "Long text to be summarized..."})


# Tests for execute_workflow


@patch("workflows.executors.execute_model_step")
def test_execute_workflow_simple(mock_execute_step):
    """Test execution of a simple workflow with a single step."""
    # Configure mock to return expected outputs
    mock_result = {"outputs": {"summary": "This is a summary"}, "content": None, "logprob": None}
    mock_execute_step.return_value = mock_result

    # Create a simple workflow
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    workflow = Workflow(steps={"summarize": step}, inputs=["input_text"], outputs={"summary": "summarize.summary"})

    # Execute the workflow
    result = execute_workflow(workflow, {"input_text": "Long text to be summarized..."})

    # Verify the results
    assert_workflow_output(
        result,
        {
            "final_outputs": {"summary": "This is a summary"},
            "intermediate_outputs": {
                "input_text": "Long text to be summarized...",
                "summarize.summary": "This is a summary",
            },
            "step_contents": {},
            "logprob": None,
        },
    )
    # Verify execute_model_step was called correctly
    mock_execute_step.assert_called_once()


@patch("workflows.executors.execute_model_step")
def test_execute_workflow_multi_step(mock_execute_step):
    """Test execution of a multi-step workflow with dependencies."""

    # Configure mock to return different values based on the step
    def side_effect(step, available_vars, return_full_content=False, logprobs=False):
        if step.id == "extract":
            return {"outputs": {"entities": ["Apple", "product"]}, "content": None, "logprob": None}
        elif step.id == "analyze":
            return {"outputs": {"sentiment": "positive"}, "content": None, "logprob": None}
        return {"outputs": {}, "content": None, "logprob": None}

    mock_execute_step.side_effect = side_effect

    # Create extract step
    extract_step = ModelStep(
        id="extract",
        name="Extract Entities",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Extract entities",
        input_fields=[InputField(name="text", description="Text to analyze", variable="input_text")],
        output_fields=[OutputField(name="entities", description="Extracted entities", type="list[str]")],
    )

    # Create analyze step that depends on extract step
    analyze_step = ModelStep(
        id="analyze",
        name="Analyze Sentiment",
        model="gpt-4",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Analyze sentiment",
        input_fields=[InputField(name="entities", description="Entities to analyze", variable="extract.entities")],
        output_fields=[OutputField(name="sentiment", description="Sentiment analysis", type="str")],
    )

    workflow = Workflow(
        steps={"extract": extract_step, "analyze": analyze_step},
        inputs=["input_text"],
        outputs={"entities": "extract.entities", "sentiment": "analyze.sentiment"},
    )

    # Execute the workflow
    result = execute_workflow(workflow, {"input_text": "Apple is launching a new product tomorrow."})

    assert_workflow_output(
        result,
        {
            "final_outputs": {"entities": ["Apple", "product"], "sentiment": "positive"},
            "intermediate_outputs": {
                "input_text": "Apple is launching a new product tomorrow.",
                "extract.entities": ["Apple", "product"],
                "analyze.sentiment": "positive",
            },
            "step_contents": {},
            "logprob": None,
        },
    )

    # Verify execute_model_step was called twice (once for each step)
    assert mock_execute_step.call_count == 2


def test_execute_workflow_missing_input():
    """Test that an error is raised when a required input is missing."""
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    workflow = Workflow(steps={"summarize": step}, inputs=["input_text"], outputs={"summary": "summarize.summary"})

    # Execute with missing input
    with pytest.raises(WorkflowError, match="Missing required workflow input"):
        execute_workflow(workflow, {})


def test_execute_workflow_cyclic_dependency():
    """Test that a cyclic dependency in the workflow raises an appropriate error."""
    # Make create_dependency_graph raise a CyclicDependencyError

    step1 = ModelStep(
        id="t1",
        name="Test Step 1",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Test",
        input_fields=[InputField(name="v1", description="", variable="t2.var")],
        output_fields=[OutputField(name="out", description="")],
    )
    step2 = ModelStep(
        id="t2",
        name="Test Step 2",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Test",
        input_fields=[InputField(name="v2", description="", variable="t1.out")],
        output_fields=[OutputField(name="var", description="")],
    )

    workflow = Workflow(steps=[step1, step2], inputs=[], outputs={})

    # This should propagate the CyclicDependencyError
    with pytest.raises(CyclicDependencyError):
        execute_workflow(workflow, {})


@patch("workflows.executors.execute_model_step")
def test_execute_workflow_with_full_content(mock_execute_step):
    """Test execution of a workflow with return_full_content=True."""
    # Configure mock to return expected outputs and content
    mock_result = {
        "outputs": {"summary": "This is a summary"},
        "content": "Full model response content",
        "logprob": None,
    }
    mock_execute_step.return_value = mock_result

    # Create a simple workflow
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    workflow = Workflow(steps=[step], inputs=["input_text"], outputs={"summary": "summarize.summary"})

    # Execute the workflow with return_full_content=True
    inputs = {"input_text": "Long text to be summarized..."}
    result = execute_workflow(workflow, inputs, return_full_content=True)

    assert_workflow_output(
        result,
        {
            "final_outputs": {"summary": "This is a summary"},
            "intermediate_outputs": {
                "input_text": "Long text to be summarized...",
                "summarize.summary": "This is a summary",
            },
            "step_contents": {"summarize": "Full model response content"},
            "logprob": None,
        },
    )

    # Verify execute_model_step was called correctly with return_full_content=True
    mock_execute_step.assert_called_once_with(step, inputs, return_full_content=True, logprobs=False)


@patch("workflows.executors.execute_model_step")
def test_execute_workflow_with_logprob(mock_execute_step):
    """Test execution of a workflow with logprob_step specified."""
    # Configure mock to return expected outputs with logprob
    mock_result = {"outputs": {"summary": "This is a summary"}, "content": None, "logprob": -2.5}
    mock_execute_step.return_value = mock_result

    # Create a simple workflow
    step = ModelStep(
        id="summarize",
        name="Summarize Text",
        model="gpt-3.5-turbo",
        provider="OpenAI",
        call_type=CallType.LLM,
        system_prompt="Summarize the text",
        input_fields=[InputField(name="text", description="Text to summarize", variable="input_text")],
        output_fields=[OutputField(name="summary", description="Summary of the text", type="str")],
    )

    workflow = Workflow(steps={"summarize": step}, inputs=["input_text"], outputs={"summary": "summarize.summary"})

    # Execute the workflow with logprob_step specified
    result = execute_workflow(workflow, {"input_text": "Long text to be summarized..."}, logprob_step="summarize")

    # Verify the results
    assert_workflow_output(
        result,
        {
            "final_outputs": {"summary": "This is a summary"},
            "logprob": -2.5,
            "intermediate_outputs": {
                "input_text": "Long text to be summarized...",
                "summarize.summary": "This is a summary",
            },
            "step_contents": {},
        },
    )
    # Verify execute_model_step was called with logprobs=True
    mock_execute_step.assert_called_once()
    args, kwargs = mock_execute_step.call_args
    assert kwargs["logprobs"] is True