# coding=utf-8
# Copyright 2018 The HuggingFace Inc. team.
#
# 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.
"""Convert OpenAI GPT checkpoint."""


import argparse
import json
import logging

import numpy
import torch

from transformers import CONFIG_NAME, WEIGHTS_NAME
from transformers.tokenization_xlm import VOCAB_FILES_NAMES


logging.basicConfig(level=logging.INFO)


def convert_xlm_checkpoint_to_pytorch(xlm_checkpoint_path, pytorch_dump_folder_path):
    # Load checkpoint
    chkpt = torch.load(xlm_checkpoint_path, map_location="cpu")

    state_dict = chkpt["model"]

    # We have the base model one level deeper than the original XLM repository
    two_levels_state_dict = {}
    for k, v in state_dict.items():
        if "pred_layer" in k:
            two_levels_state_dict[k] = v
        else:
            two_levels_state_dict["transformer." + k] = v

    config = chkpt["params"]
    config = dict((n, v) for n, v in config.items() if not isinstance(v, (torch.FloatTensor, numpy.ndarray)))

    vocab = chkpt["dico_word2id"]
    vocab = dict((s + "</w>" if s.find("@@") == -1 and i > 13 else s.replace("@@", ""), i) for s, i in vocab.items())

    # Save pytorch-model
    pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
    pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
    pytorch_vocab_dump_path = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

    print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
    torch.save(two_levels_state_dict, pytorch_weights_dump_path)

    print("Save configuration file to {}".format(pytorch_config_dump_path))
    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
        f.write(json.dumps(config, indent=2) + "\n")

    print("Save vocab file to {}".format(pytorch_config_dump_path))
    with open(pytorch_vocab_dump_path, "w", encoding="utf-8") as f:
        f.write(json.dumps(vocab, indent=2) + "\n")


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    args = parser.parse_args()
    convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)
