# 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 BERT checkpoint."""



from __future__ import absolute_import

from __future__ import division

from __future__ import print_function



import os

import argparse

import torch



from transformers import (CONFIG_NAME, WEIGHTS_NAME,

                                                    XLNetConfig,

                                                    XLNetLMHeadModel, XLNetForQuestionAnswering,

                                                    XLNetForSequenceClassification,

                                                    load_tf_weights_in_xlnet)



GLUE_TASKS_NUM_LABELS = {

    "cola": 2,

    "mnli": 3,

    "mrpc": 2,

    "sst-2": 2,

    "sts-b": 1,

    "qqp": 2,

    "qnli": 2,

    "rte": 2,

    "wnli": 2,

}



import logging

logging.basicConfig(level=logging.INFO)



def convert_xlnet_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_folder_path, finetuning_task=None):

    # Initialise PyTorch model

    config = XLNetConfig.from_json_file(bert_config_file)



    finetuning_task = finetuning_task.lower() if finetuning_task is not None else ""

    if finetuning_task in GLUE_TASKS_NUM_LABELS:

        print("Building PyTorch XLNetForSequenceClassification model from configuration: {}".format(str(config)))

        config.finetuning_task = finetuning_task

        config.num_labels = GLUE_TASKS_NUM_LABELS[finetuning_task]

        model = XLNetForSequenceClassification(config)

    elif 'squad' in finetuning_task:

        config.finetuning_task = finetuning_task

        model = XLNetForQuestionAnswering(config)

    else:

        model = XLNetLMHeadModel(config)



    # Load weights from tf checkpoint

    load_tf_weights_in_xlnet(model, config, tf_checkpoint_path)



    # Save pytorch-model

    pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)

    pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME)

    print("Save PyTorch model to {}".format(os.path.abspath(pytorch_weights_dump_path)))

    torch.save(model.state_dict(), pytorch_weights_dump_path)

    print("Save configuration file to {}".format(os.path.abspath(pytorch_config_dump_path)))

    with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:

        f.write(config.to_json_string())





if __name__ == "__main__":

    parser = argparse.ArgumentParser()

    ## Required parameters

    parser.add_argument("--tf_checkpoint_path",

                        default = None,

                        type = str,

                        required = True,

                        help = "Path to the TensorFlow checkpoint path.")

    parser.add_argument("--xlnet_config_file",

                        default = None,

                        type = str,

                        required = True,

                        help = "The config json file corresponding to the pre-trained XLNet model. \n"

                               "This specifies the model architecture.")

    parser.add_argument("--pytorch_dump_folder_path",

                        default = None,

                        type = str,

                        required = True,

                        help = "Path to the folder to store the PyTorch model or dataset/vocab.")

    parser.add_argument("--finetuning_task",

                        default = None,

                        type = str,

                        help = "Name of a task on which the XLNet TensorFloaw model was fine-tuned")

    args = parser.parse_args()

    print(args)



    convert_xlnet_checkpoint_to_pytorch(args.tf_checkpoint_path,

                                        args.xlnet_config_file,

                                        args.pytorch_dump_folder_path,

                                        args.finetuning_task)

