"""
    Source Code: https://github.com/mit-han-lab/streaming-llm/blob/main/streaming_llm/utils.py
"""

import torch
import argparse
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
)
import os.path as osp
import ssl
import urllib.request
import os
import json


def load(model_name_or_path):
    print(f"Loading model from {model_name_or_path} ...")
    # however, tensor parallel for running falcon will occur bugs
    tokenizer = AutoTokenizer.from_pretrained(
        model_name_or_path,
        trust_remote_code=True,
    )
    model = AutoModelForCausalLM.from_pretrained(
        model_name_or_path,
        device_map="auto",
        torch_dtype=torch.float16,
        trust_remote_code=True,
    )
    if tokenizer.pad_token_id is None:
        if tokenizer.eos_token_id is not None:
            tokenizer.pad_token_id = tokenizer.eos_token_id
        else:
            tokenizer.pad_token_id = 0

    model.eval()

    return model, tokenizer


def download_url(url: str, folder="folder"):
    """
    Downloads the content of an url to a folder. Modified from \
    https://github.com/pyg-team/pytorch_geometric/tree/master/torch_geometric

    Args:
        url (string): The url of target file.
        folder (string): The target folder.

    Returns:
        string: File path of downloaded files.
    """

    file = url.rpartition("/")[2]
    file = file if file[0] == "?" else file.split("?")[0]
    path = osp.join(folder, file)
    if osp.exists(path):
        print(f"File {file} exists, use existing file.")
        return path

    print(f"Downloading {url}")
    os.makedirs(folder, exist_ok=True)
    ctx = ssl._create_unverified_context()
    data = urllib.request.urlopen(url, context=ctx)
    with open(path, "wb") as f:
        f.write(data.read())

    return path


def load_jsonl(
    file_path,
):
    list_data_dict = []
    with open(file_path, "r") as f:
        for line in f:
            list_data_dict.append(json.loads(line))
    return list_data_dict



@torch.no_grad()
def greedy_generate(model, tokenizer, input_ids, past_key_values, max_gen_len):
    outputs = model(
        input_ids=input_ids,
        past_key_values=past_key_values,
        use_cache=True,
    )
    past_key_values = outputs.past_key_values
    pred_token_idx = outputs.logits[:, -1, :].argmax(dim=-1).unsqueeze(1)
    generated_ids = [pred_token_idx.item()]
    pos = 0
    for _ in range(max_gen_len - 1):
        outputs = model(
            input_ids=pred_token_idx,
            past_key_values=past_key_values,
            use_cache=True,
        )
        past_key_values = outputs.past_key_values
        pred_token_idx = outputs.logits[:, -1, :].argmax(dim=-1).unsqueeze(1)
        generated_ids.append(pred_token_idx.item())
        generated_text = (
            tokenizer.decode(
                generated_ids,
                skip_special_tokens=True,
                clean_up_tokenization_spaces=True,
                spaces_between_special_tokens=False,
            )
            .strip()
            .split(" ")
        )

        now = len(generated_text) - 1
        if now > pos:
            print(" ".join(generated_text[pos:now]), end=" ", flush=True)
            pos = now

        if pred_token_idx == tokenizer.eos_token_id:
            break
    print(" ".join(generated_text[pos:]), flush=True)
    return past_key_values