"""Unit tests for ReAct."""

from typing import Any, List, Mapping, Optional, Union

import pytest

from langchain.chains.llm import LLMChain
from langchain.chains.react.base import ReActChain, predict_until_observation
from langchain.docstore.base import Docstore
from langchain.docstore.document import Document
from langchain.llms.base import LLM
from langchain.prompts.prompt import PromptTemplate

_PAGE_CONTENT = """This is a page about LangChain.

It is a really cool framework.

What isn't there to love about langchain?

Made in 2022."""

_FAKE_PROMPT = PromptTemplate(input_variables=["input"], template="{input}")


class FakeListLLM(LLM):
    """Fake LLM for testing that outputs elements of a list."""

    def __init__(self, responses: List[str]):
        """Initialize with list of responses."""
        self.responses = responses
        self.i = -1

    def __call__(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        """Increment counter, and then return response in that index."""
        self.i += 1
        return self.responses[self.i]

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        return {}


class FakeDocstore(Docstore):
    """Fake docstore for testing purposes."""

    def search(self, search: str) -> Union[str, Document]:
        """Return the fake document."""
        document = Document(page_content=_PAGE_CONTENT)
        return document


def test_predict_until_observation_normal() -> None:
    """Test predict_until_observation when observation is made normally."""
    outputs = ["foo\nAction 1: search[foo]"]
    fake_llm = FakeListLLM(outputs)
    fake_llm_chain = LLMChain(llm=fake_llm, prompt=_FAKE_PROMPT)
    ret_text, action, directive = predict_until_observation(fake_llm_chain, "", 1)
    assert ret_text == outputs[0]
    assert action == "search"
    assert directive == "foo"


def test_predict_until_observation_repeat() -> None:
    """Test when no action is generated initially."""
    outputs = ["foo", " search[foo]"]
    fake_llm = FakeListLLM(outputs)
    fake_llm_chain = LLMChain(llm=fake_llm, prompt=_FAKE_PROMPT)
    ret_text, action, directive = predict_until_observation(fake_llm_chain, "", 1)
    assert ret_text == "foo\nAction 1: search[foo]"
    assert action == "search"
    assert directive == "foo"


def test_predict_until_observation_error() -> None:
    """Test handling of generation of text that cannot be parsed."""
    outputs = ["foo\nAction 1: foo"]
    fake_llm = FakeListLLM(outputs)
    fake_llm_chain = LLMChain(llm=fake_llm, prompt=_FAKE_PROMPT)
    with pytest.raises(ValueError):
        predict_until_observation(fake_llm_chain, "", 1)


def test_react_chain() -> None:
    """Test react chain."""
    responses = [
        "I should probably search\nAction 1: Search[langchain]",
        "I should probably lookup\nAction 2: Lookup[made]",
        "Ah okay now I know the answer\nAction 3: Finish[2022]",
    ]
    fake_llm = FakeListLLM(responses)
    react_chain = ReActChain(llm=fake_llm, docstore=FakeDocstore())
    inputs = {"question": "when was langchain made"}
    output = react_chain(inputs)
    assert output["answer"] == "2022"


def test_react_chain_bad_action() -> None:
    """Test react chain when bad action given."""
    responses = [
        "I should probably search\nAction 1: BadAction[langchain]",
    ]
    fake_llm = FakeListLLM(responses)
    react_chain = ReActChain(llm=fake_llm, docstore=FakeDocstore())
    with pytest.raises(ValueError):
        react_chain.run("when was langchain made")