import os
from pathlib import Path

import numpy as np
import pandas as pd

from buster.busterbot import Buster, BusterConfig, Response
from buster.completers.base import Completer, Completion
from buster.retriever import Retriever
from buster.utils import get_retriever_from_extension

TEST_DATA_DIR = Path(__file__).resolve().parent / "data"
DOCUMENTS_FILE = os.path.join(str(TEST_DATA_DIR), "document_embeddings_huggingface_subset.tar.gz")


def get_fake_embedding(length=1536):
    rng = np.random.default_rng()
    return list(rng.random(length, dtype=np.float32))


class MockCompleter(Completer):
    def __init__(self, expected_answer):
        self.expected_answer = expected_answer

    def complete(self):
        return

    def generate_response(self, user_input, system_prompt) -> Completion:
        return Completion(self.expected_answer)


class MockRetriever(Retriever):
    def __init__(self, filepath):
        self.filepath = filepath

        n_samples = 100
        self.documents = pd.DataFrame.from_dict(
            {
                "title": ["test"] * n_samples,
                "url": ["http://url.com"] * n_samples,
                "content": ["cool text"] * n_samples,
                "embedding": [get_fake_embedding()] * n_samples,
                "n_tokens": [10] * n_samples,
                "source": ["fake source"] * n_samples,
            }
        )

    def get_documents(self, source):
        return self.documents

    def get_source_display_name(self, source):
        return source


import logging

logging.basicConfig(level=logging.INFO)


def test_chatbot_mock_data(tmp_path, monkeypatch):
    gpt_expected_answer = "this is GPT answer"
    monkeypatch.setattr(Buster, "get_embedding", lambda self, prompt, engine: get_fake_embedding())
    monkeypatch.setattr(
        "buster.busterbot.completer_factory", lambda x: MockCompleter(expected_answer=gpt_expected_answer)
    )

    hf_transformers_cfg = BusterConfig(
        unknown_prompt="This doesn't seem to be related to the huggingface library. I am not sure how to answer.",
        embedding_model="text-embedding-ada-002",
        retriever_cfg={
            "top_k": 3,
            "thresh": 0.7,
        },
        document_source="fake source",
        completion_cfg={
            "name": "ChatGPT",
            "completion_kwargs": {
                "engine": "gpt-3.5-turbo",
                "max_tokens": 200,
                "temperature": None,
                "top_p": None,
                "frequency_penalty": 1,
                "presence_penalty": 1,
            },
        },
        prompt_cfg={
            "max_words": 2000,
            "text_before_documents": "",
            "text_before_prompt": (
                """You are a slack chatbot assistant answering technical questions about huggingface transformers, a library to train transformers in python.\n"""
                """Make sure to format your answers in Markdown format, including code block and snippets.\n"""
                """Do not include any links to urls or hyperlinks in your answers.\n\n"""
                """Now answer the following question:\n"""
            ),
        },
    )
    filepath = tmp_path / "not_a_real_file.tar.gz"
    retriever = MockRetriever(filepath)
    buster = Buster(cfg=hf_transformers_cfg, retriever=retriever)
    response = buster.process_input("What is a transformer?")
    assert isinstance(response.completion.text, str)
    assert response.completion.text.startswith(gpt_expected_answer)


def test_chatbot_real_data__chatGPT():
    hf_transformers_cfg = BusterConfig(
        unknown_prompt="I'm sorry, but I am an AI language model trained to assist with questions related to the huggingface transformers library. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?",
        embedding_model="text-embedding-ada-002",
        completion_cfg={
            "name": "ChatGPT",
            "completion_kwargs": {
                "model": "gpt-3.5-turbo",
            },
        },
        prompt_cfg={
            "max_words": 2000,
            "text_before_documents": "",
            "text_before_prompt": (
                """You are a slack chatbot assistant answering technical questions about huggingface transformers, a library to train transformers in python.\n"""
                """Make sure to format your answers in Markdown format, including code block and snippets.\n"""
                """Do not include any links to urls or hyperlinks in your answers.\n\n"""
                """Now answer the following question:\n"""
            ),
        },
    )
    retriever = get_retriever_from_extension(DOCUMENTS_FILE)(DOCUMENTS_FILE)
    buster = Buster(cfg=hf_transformers_cfg, retriever=retriever)
    response = buster.process_input("What is a transformer?")
    assert isinstance(response.completion.text, str)


def test_chatbot_real_data__chatGPT_OOD():
    buster_cfg = BusterConfig(
        unknown_prompt="I'm sorry, but I am an AI language model trained to assist with questions related to the huggingface transformers library. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?",
        embedding_model="text-embedding-ada-002",
        completion_cfg={
            "name": "ChatGPT",
            "completion_kwargs": {
                "model": "gpt-3.5-turbo",
            },
        },
        retriever_cfg={
            "top_k": 3,
            "thresh": 0.7,
        },
        prompt_cfg={
            "max_words": 3000,
            "text_before_prompt": (
                """You are a chatbot assistant answering technical questions about huggingface transformers, a library to train transformers in python. """
                """Make sure to format your answers in Markdown format, including code block and snippets. """
                """Do not include any links to urls or hyperlinks in your answers. """
                """If you do not know the answer to a question, or if it is completely irrelevant to the library usage, let the user know you cannot answer. """
                """Use this response: """
                """'I'm sorry, but I am an AI language model trained to assist with questions related to the huggingface transformers library. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?'\n"""
                """For example:\n"""
                """What is the meaning of life for huggingface?\n"""
                """I'm sorry, but I am an AI language model trained to assist with questions related to the huggingface transformers library. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?"""
                """Now answer the following question:\n"""
            ),
            "text_before_documents": "Only use these documents as reference:\n",
        },
    )
    retriever = get_retriever_from_extension(DOCUMENTS_FILE)(DOCUMENTS_FILE)
    buster = Buster(cfg=buster_cfg, retriever=retriever)
    response = buster.process_input("What is a good recipe for brocolli soup?")
    assert isinstance(response.completion.text, str)
    assert response.is_relevant == False


def test_chatbot_real_data__GPT():
    buster_cfg = BusterConfig(
        unknown_prompt="I'm sorry, but I am an AI language model trained to assist with questions related to the huggingface transformers library. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?",
        embedding_model="text-embedding-ada-002",
        completion_cfg={
            "name": "ChatGPT",
            "completion_kwargs": {
                "model": "gpt-3.5-turbo",
            },
        },
        retriever_cfg={
            "top_k": 3,
            "thresh": 0.7,
        },
        prompt_cfg={
            "max_words": 3000,
            "text_before_prompt": (
                """You are a chatbot assistant answering technical questions about huggingface transformers, a library to train transformers in python. """
                """Make sure to format your answers in Markdown format, including code block and snippets. """
                """Do not include any links to urls or hyperlinks in your answers. """
                """If you do not know the answer to a question, or if it is completely irrelevant to the library usage, let the user know you cannot answer. """
                """Use this response: """
                """'I'm sorry, but I am an AI language model trained to assist with questions related to the huggingface transformers library. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?'\n"""
                """For example:\n"""
                """What is the meaning of life for huggingface?\n"""
                """I'm sorry, but I am an AI language model trained to assist with questions related to the huggingface transformers library. I cannot answer that question as it is not relevant to the library or its usage. Is there anything else I can assist you with?"""
                """Now answer the following question:\n"""
            ),
            "text_before_documents": "Only use these documents as reference:\n",
        },
    )
    retriever = get_retriever_from_extension(DOCUMENTS_FILE)(DOCUMENTS_FILE)
    buster = Buster(cfg=buster_cfg, retriever=retriever)
    response = buster.process_input("What is a transformer?")
    assert isinstance(response.completion.text, str)
    assert response.is_relevant == True