# 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 argparse
import os
from pprint import pprint

import paddle

from paddlenlp.ops import FasterMBART
from paddlenlp.transformers import MBartForConditionalGeneration, MBartTokenizer
from paddlenlp.utils.log import logger


def parse_args():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--model_name_or_path",
        default="mbart-large-50-many-to-many-mmt",
        type=str,
        help="The model name to specify the bart to use. ",
    )
    parser.add_argument(
        "--inference_model_dir", default="./infer_model/", type=str, help="Path to save inference model of bart. "
    )
    parser.add_argument("--topk", default=4, type=int, help="The number of candidate to procedure top_k sampling. ")
    parser.add_argument(
        "--topp", default=1.0, type=float, help="The probability threshold to procedure top_p sampling. "
    )
    parser.add_argument("--max_out_len", default=64, type=int, help="Maximum output length. ")
    parser.add_argument("--temperature", default=1.0, type=float, help="The temperature to set. ")
    parser.add_argument("--num_return_sequences", default=1, type=int, help="The number of returned sequences. ")
    parser.add_argument("--use_fp16_decoding", action="store_true", help="Whether to use fp16 decoding to predict. ")
    parser.add_argument(
        "--decoding_strategy",
        default="beam_search",
        choices=["sampling", "beam_search"],
        type=str,
        help="The main strategy to decode. ",
    )
    parser.add_argument("--num_beams", default=5, type=int, help="The number of candidate to procedure beam search. ")
    parser.add_argument(
        "--diversity_rate", default=0.0, type=float, help="The diversity rate to procedure beam search. "
    )
    parser.add_argument("--repetition_penalty", default=1.0, type=float, help="The repetition_penalty to set. ")
    parser.add_argument("--length_penalty", default=0.0, type=float, help="The length penalty to decode. ")
    parser.add_argument("--early_stopping", action="store_true", help="Whether to do early stopping. ")

    args = parser.parse_args()
    return args


def do_predict(args):
    place = "gpu"
    place = paddle.set_device(place)

    model = MBartForConditionalGeneration.from_pretrained(args.model_name_or_path)
    tokenizer = MBartTokenizer.from_pretrained(args.model_name_or_path, src_lang="en_XX")

    # For opening faster_encoder
    model.eval()

    fast_mbart = FasterMBART(model=model, use_fp16_decoding=args.use_fp16_decoding)
    # Set evaluate mode
    fast_mbart.eval()

    # Convert dygraph model to static graph model
    fast_mbart = paddle.jit.to_static(
        fast_mbart,
        input_spec=[
            # input_ids
            paddle.static.InputSpec(shape=[None, None], dtype="int32"),
            # encoder_output
            None,
            # seq_len
            None,
            paddle.static.InputSpec(
                shape=[None, 1], dtype="int32"
            ),  # forced_bos_token_id can be a Tensor or int (bos_id)
            args.num_beams,  # num_beams.
            args.topk,  # top_k
            args.topp,  # top_p
            args.decoding_strategy,  # decode_strategy
            tokenizer.bos_token_id,  # bos_token_id
            tokenizer.eos_token_id,  # eos_token_id
            tokenizer.pad_token_id,  # pad_token_id
            model.mbart.config["decoder_start_token_id"],  # decoder_start_token_id
            args.max_out_len,  # max_length
            args.diversity_rate,  # diversity_rate
            args.length_penalty,  # length_penalty
            args.temperature,  # temperature
            args.num_return_sequences,  # num_return_sequences
            args.early_stopping,  # early_stopping
            tokenizer.eos_token_id,  # forced_eos_token_id
        ],
    )

    # Save converted static graph model
    paddle.jit.save(fast_mbart, os.path.join(args.inference_model_dir, "mbart"))
    logger.info("MBART has been saved to {}.".format(args.inference_model_dir))


if __name__ == "__main__":
    args = parse_args()
    pprint(args)

    do_predict(args)
