# Copyright (c) 2022 PaddlePaddle Authors. 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 os
import sys
from functools import partial

import paddle
import paddle.nn as nn
from paddle.io import BatchSampler, DataLoader, DistributedBatchSampler

from paddlenlp.data import DataCollatorWithPadding
from paddlenlp.datasets import load_dataset
from paddlenlp.transformers import ErnieForSequenceClassification, ErnieTokenizer
from paddlenlp.utils.log import logger

from .model_base import BenchmarkBase

sys.path.insert(
    0,
    os.path.abspath(
        os.path.join(
            os.path.dirname(__file__), os.pardir, os.pardir, os.pardir, os.pardir, "slm", "model_zoo", "ernie-3.0"
        )
    ),
)


from utils import seq_convert_example  # noqa: E402


class ErnieTinyBenchmark(BenchmarkBase):
    def __init__(self):
        self.label_list = None
        super().__init__()

    @staticmethod
    def add_args(args, parser):
        parser.add_argument(
            "--model_name_or_path", type=str, default="ernie-tiny", help="Model name. Defaults to ernie-tiny. "
        )
        parser.add_argument(
            "--task_name",
            default="tnews",
            type=str,
            help="The name of the task to train selected in the list: afqmc, tnews, iflytek, ocnli, cmnli, cluewsc2020, csl",
        )
        parser.add_argument("--max_seq_length", type=int, default=args.max_seq_len, help="Maximum sequence length. ")

    def create_data_loader(self, args, **kwargs):
        args.task_name = args.task_name.lower()

        tokenizer = ErnieTokenizer.from_pretrained(args.model_name_or_path)
        train_ds, dev_ds = load_dataset("clue", args.task_name, splits=("train", "dev"))
        trans_func = partial(
            seq_convert_example, label_list=train_ds.label_list, tokenizer=tokenizer, max_seq_len=args.max_seq_length
        )

        train_ds = train_ds.map(trans_func, lazy=True)
        train_batch_sampler = DistributedBatchSampler(train_ds, batch_size=args.batch_size, shuffle=True)

        dev_ds = dev_ds.map(trans_func, lazy=True)
        dev_batch_sampler = BatchSampler(dev_ds, batch_size=args.batch_size, shuffle=False)

        batchify_fn = DataCollatorWithPadding(tokenizer)

        train_loader = DataLoader(
            dataset=train_ds,
            batch_sampler=train_batch_sampler,
            collate_fn=batchify_fn,
            num_workers=0,
            return_list=True,
        )
        dev_loader = DataLoader(
            dataset=dev_ds, batch_sampler=dev_batch_sampler, collate_fn=batchify_fn, num_workers=0, return_list=True
        )
        self.num_batch = len(train_loader)
        self.label_list = train_ds.label_list

        return train_loader, dev_loader

    def build_model(self, args, **kwargs):
        num_classes = 1 if self.label_list is None else len(self.label_list)
        model = ErnieForSequenceClassification.from_pretrained(args.model_name_or_path, num_classes=num_classes)

        self.loss_fct = nn.CrossEntropyLoss() if self.label_list else nn.MSELoss()

        return model

    def forward(self, model, args, input_data=None, **kwargs):
        labels = input_data.pop("labels")
        logits = model(**input_data)
        loss = self.loss_fct(logits, labels)

        return loss, args.batch_size

    def logger(
        self,
        args,
        step_id=None,
        pass_id=None,
        batch_id=None,
        loss=None,
        batch_cost=None,
        reader_cost=None,
        num_samples=None,
        ips=None,
        **kwargs
    ):
        max_mem_reserved_msg = ""
        max_mem_allocated_msg = ""
        if paddle.device.is_compiled_with_cuda():
            max_mem_reserved_msg = f"max_mem_reserved: {paddle.device.cuda.max_memory_reserved() // (1024 ** 2)} MB,"
            max_mem_allocated_msg = f"max_mem_allocated: {paddle.device.cuda.max_memory_allocated() // (1024 ** 2)} MB"
        logger.info(
            "global step %d / %d, loss: %f, avg_reader_cost: %.5f sec, avg_batch_cost: %.5f sec, "
            "avg_samples: %.5f, ips: %.5f sequences/sec, %s %s"
            % (
                step_id,
                args.epoch * self.num_batch,
                loss,
                reader_cost,
                batch_cost,
                num_samples,
                ips,
                max_mem_reserved_msg,
                max_mem_allocated_msg,
            )
        )