"""
Module which builds embeddings for issues and pull requests

The module is designed to be run from the command line and takes the following arguments:

        --input_filename: The name of the file containing the issues and pull requests
        --model_id: The name of the sentence transformer model to use
        --issue_type: The type of issue to embed (either "issue" or "pull")
        --n_issues: The number of issues to embed
        --update: Whether to update the existing embeddings

The module saves the embeddings to a file called <issue_type>_embeddings.npy and the index to a file called
embedding_index_to_<issue_type>.json

The index provides a mapping from the index of the embedding to the issue or pull request number.

"""

import argparse
import json
import logging
import os

import numpy as np
from sentence_transformers import SentenceTransformer

logging.basicConfig(level=logging.INFO)

logger = logging.getLogger(__name__)


def load_model(model_id: str):
    return SentenceTransformer(model_id)


class EmbeddingWriter:
    def __init__(self, output_embedding_filename, output_index_filename, update, embedding_to_issue_index) -> None:
        self.output_embedding_filename = output_embedding_filename
        self.output_index_filename = output_index_filename
        self.embeddings = []
        self.embedding_to_issue_index = embedding_to_issue_index
        self.update = update

    def __enter__(self):
        return self.embeddings

    def __exit__(self, exc_type, exc_val, exc_tb):
        if len(self.embeddings) == 0:
            return

        embeddings = np.array(self.embeddings)

        if self.update and os.path.exists(self.output_embedding_filename):
            embeddings = np.concatenate([np.load(self.output_embedding_filename), embeddings])

        logger.info(f"Saving embeddings to {self.output_embedding_filename}")
        np.save(self.output_embedding_filename, embeddings)

        logger.info(f"Saving embedding index to {self.output_index_filename}")
        with open(self.output_index_filename, "w") as f:
            json.dump(self.embedding_to_issue_index, f, indent=4)


def embed_issues(
    input_filename: str,
    model_id: str,
    issue_type: str,
    n_issues: int = -1,
    update: bool = False
):
    model = load_model(model_id)

    output_embedding_filename = f"{issue_type}_embeddings.npy"
    output_index_filename = f"embedding_index_to_{issue_type}.json"

    with open(input_filename, "r") as f:
        issues = json.load(f)

    if update and os.path.exists(output_index_filename):
        with open(output_index_filename, "r") as f:
            embedding_to_issue_index = json.load(f)
        embedding_index = len(embedding_to_issue_index)
    else:
        embedding_to_issue_index = {}
        embedding_index = 0

    max_issues = n_issues if n_issues > 0 else len(issues)
    n_issues = 0

    with EmbeddingWriter(
        output_embedding_filename=output_embedding_filename,
        output_index_filename=output_index_filename,
        update=update,
        embedding_to_issue_index=embedding_to_issue_index
    ) as embeddings: #, embedding_to_issue_index:
        for issue_id, issue in issues.items():
            if n_issues >= max_issues:
                break

            if issue_id in embedding_to_issue_index.values() and update:
                logger.info(f"Skipping issue {issue_id} as it is already embedded")
                continue

            if "body" not in issue:
                logger.info(f"Skipping issue {issue_id} as it has no body")
                continue

            if issue_type == "pull" and "pull_request" not in issue:
                logger.info(f"Skipping issue {issue_id} as it is not a pull request")
                continue

            elif issue_type == "issue" and "pull_request" in issue:
                logger.info(f"Skipping issue {issue_id} as it is a pull request")
                continue

            title = issue["title"] if issue["title"] is not None else ""
            body = issue["body"] if issue["body"] is not None else ""

            logger.info(f"Embedding issue {issue_id}")
            embedding = model.encode(title + "\n" + body)
            embedding_to_issue_index[embedding_index] = issue_id
            embeddings.append(embedding)
            embedding_index += 1
            n_issues += 1


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('issue_type', choices=['issue', 'pull'], default='issue')
    parser.add_argument("--input_filename", type=str, default="issues_dict.json")
    parser.add_argument("--model_id", type=str, default="all-mpnet-base-v2")
    parser.add_argument("--n_issues", type=int, default=-1)
    parser.add_argument("--update", action="store_true")
    args = parser.parse_args()
    embed_issues(**vars(args))