#!/usr/bin/env python
# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Optional

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from utils import (
    Seq2SeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


logger = getLogger(__name__)


def eval_data_dir(
    data_dir,
    save_dir: str,
    model_name: str,
    bs: int = 8,
    max_source_length: int = 1024,
    type_path="val",
    n_obs=None,
    fp16=False,
    task="summarization",
    local_rank=None,
    num_return_sequences=1,
    dataset_kwargs: Optional[dict] = None,
    prefix="",
    **generate_kwargs,
) -> dict:
    """Run evaluation on part of the data for one gpu and save to {save_dir}/rank_{rank}_output.json"""
    model_name = str(model_name)
    assert local_rank is not None
    torch.distributed.init_process_group(backend="nccl", rank=local_rank)

    save_dir = Path(save_dir)
    save_path = save_dir.joinpath(f"rank_{local_rank}_output.json")
    torch.cuda.set_device(local_rank)
    model = AutoModelForSeq2SeqLM.from_pretrained(model_name).cuda()
    if fp16:
        model = model.half()
    # determine if we need to increase num_beams
    use_task_specific_params(model, task)  # update config with task specific params
    num_beams = generate_kwargs.pop("num_beams", model.config.num_beams)  # AttributeError risk?
    if num_return_sequences > num_beams:
        num_beams = num_return_sequences

    tokenizer = AutoTokenizer.from_pretrained(model_name)
    logger.info(f"Inferred tokenizer type: {tokenizer.__class__}")  # if this is wrong, check config.model_type.

    if max_source_length is None:
        max_source_length = tokenizer.model_max_length
    if prefix is None:
        prefix = prefix or getattr(model.config, "prefix", "") or ""
    ds = Seq2SeqDataset(
        tokenizer,
        data_dir,
        max_source_length,
        max_target_length=1024,
        type_path=type_path,
        n_obs=n_obs,
        prefix=prefix,
        **dataset_kwargs,
    )
    # I set shuffle=True for a more accurate progress bar.
    # If all the longest samples are first, the prog bar estimate is too high at the beginning.
    sampler = ds.make_sortish_sampler(bs, distributed=True, add_extra_examples=False, shuffle=True)
    data_loader = DataLoader(ds, sampler=sampler, batch_size=bs, collate_fn=ds.collate_fn)
    results = []
    for batch in tqdm(data_loader):
        summaries = model.generate(
            input_ids=batch["input_ids"].to(model.device),
            attention_mask=batch["attention_mask"].to(model.device),
            num_return_sequences=num_return_sequences,
            num_beams=num_beams,
            **generate_kwargs,
        )
        preds = tokenizer.batch_decode(summaries, skip_special_tokens=True, clean_up_tokenization_spaces=False)
        ids = batch["ids"]
        if num_return_sequences > 1:
            preds = chunks(preds, num_return_sequences)  # batch size chunks, each of size num_return_seq
        for i, pred in enumerate(preds):
            results.append({"pred": pred, "id": ids[i].item()})
    save_json(results, save_path)
    return results, sampler.num_replicas


def run_generate():
    parser = argparse.ArgumentParser(
        epilog="Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"
    )
    parser.add_argument("--data_dir", type=str, help="like cnn_dm/test.source")
    parser.add_argument(
        "--model_name",
        type=str,
        help="like facebook/bart-large-cnn,google-t5/t5-base, etc.",
        default="sshleifer/distilbart-xsum-12-3",
    )
    parser.add_argument("--save_dir", type=str, help="where to save", default="tmp_gen")
    parser.add_argument("--max_source_length", type=int, default=None)
    parser.add_argument(
        "--type_path", type=str, default="test", help="which subset to evaluate typically train/val/test"
    )
    parser.add_argument("--task", type=str, default="summarization", help="used for task_specific_params + metrics")
    parser.add_argument("--bs", type=int, default=8, required=False, help="batch size")
    parser.add_argument(
        "--local_rank", type=int, default=-1, required=False, help="should be passed by distributed.launch"
    )

    parser.add_argument(
        "--n_obs", type=int, default=None, required=False, help="How many observations. Defaults to all."
    )
    parser.add_argument(
        "--num_return_sequences", type=int, default=1, required=False, help="How many sequences to return"
    )
    parser.add_argument(
        "--sync_timeout",
        type=int,
        default=600,
        required=False,
        help="How long should master process wait for other processes to finish.",
    )
    parser.add_argument("--src_lang", type=str, default=None, required=False)
    parser.add_argument("--tgt_lang", type=str, default=None, required=False)
    parser.add_argument(
        "--prefix", type=str, required=False, default=None, help="will be added to the beginning of src examples"
    )
    parser.add_argument("--fp16", action="store_true")
    parser.add_argument("--debug", action="store_true")
    start_time = time.time()
    args, rest = parser.parse_known_args()
    generate_kwargs = parse_numeric_n_bool_cl_kwargs(rest)
    if generate_kwargs and args.local_rank <= 0:
        print(f"parsed the following generate kwargs: {generate_kwargs}")
    json_save_dir = Path(args.save_dir + "_tmp")
    Path(json_save_dir).mkdir(exist_ok=True)  # this handles locking.
    intermediate_files = list(json_save_dir.glob("rank_*.json"))
    if intermediate_files:
        raise ValueError(f"Found files at {json_save_dir} please move or remove them.")
        # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
    dataset_kwargs = {}
    if args.src_lang is not None:
        dataset_kwargs["src_lang"] = args.src_lang
    if args.tgt_lang is not None:
        dataset_kwargs["tgt_lang"] = args.tgt_lang

    Path(args.save_dir).mkdir(exist_ok=True)
    results, num_replicas = eval_data_dir(
        args.data_dir,
        json_save_dir,
        args.model_name,
        type_path=args.type_path,
        bs=args.bs,
        fp16=args.fp16,
        task=args.task,
        local_rank=args.local_rank,
        n_obs=args.n_obs,
        max_source_length=args.max_source_length,
        num_return_sequences=args.num_return_sequences,
        prefix=args.prefix,
        dataset_kwargs=dataset_kwargs,
        **generate_kwargs,
    )

    if args.local_rank <= 0:
        save_dir = Path(args.save_dir)
        save_dir.mkdir(exist_ok=True)
        partial_results = gather_results_from_each_node(num_replicas, json_save_dir, args.sync_timeout)
        preds = combine_partial_results(partial_results)
        if args.num_return_sequences > 1:
            save_path = save_dir.joinpath("pseudolabel_results.json")
            print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/")
            save_json(preds, save_path)
            return
        tgt_file = Path(args.data_dir).joinpath(args.type_path + ".target")
        with open(tgt_file) as f:
            labels = [x.rstrip() for x in f.readlines()][: len(preds)]

        # Calculate metrics, save metrics,  and save _generations.txt
        calc_bleu = "translation" in args.task
        score_fn = calculate_bleu if calc_bleu else calculate_rouge
        metric_name = "bleu" if calc_bleu else "rouge"
        metrics: dict = score_fn(preds, labels)
        metrics["n_obs"] = len(preds)
        runtime = time.time() - start_time
        metrics["seconds_per_sample"] = round(runtime / metrics["n_obs"], 4)
        metrics["n_gpus"] = num_replicas
        # TODO(@stas00): add whatever metadata to metrics
        metrics_save_path = save_dir.joinpath(f"{args.type_path}_{metric_name}.json")
        save_json(metrics, metrics_save_path, indent=None)
        print(metrics)
        write_txt_file(preds, save_dir.joinpath(f"{args.type_path}_generations.txt"))
        if args.debug:
            write_txt_file(labels, save_dir.joinpath(f"{args.type_path}.target"))
        else:
            shutil.rmtree(json_save_dir)


def combine_partial_results(partial_results) -> list:
    """Concatenate partial results into one file, then sort it by id."""
    records = []
    for partial_result in partial_results:
        records.extend(partial_result)
    records = sorted(records, key=lambda x: x["id"])
    preds = [x["pred"] for x in records]
    return preds


def gather_results_from_each_node(num_replicas, save_dir, timeout) -> list[dict[str, list]]:
    # WAIT FOR lots of .json files
    start_wait = time.time()
    logger.info("waiting for all nodes to finish")
    json_data = None
    while (time.time() - start_wait) < timeout:
        json_files = list(save_dir.glob("rank_*.json"))
        if len(json_files) < num_replicas:
            continue
        try:
            # make sure all json files are fully saved
            json_data = lmap(load_json, json_files)
            return json_data
        except JSONDecodeError:
            continue
    else:
        raise TimeoutError("Rank 0 gave up on waiting for other processes")
    # Unreachable


if __name__ == "__main__":
    # Usage for MT:
    run_generate()
