# 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."""



from __future__ import absolute_import, division, print_function



import argparse

from io import open



import torch



from transformers import (CONFIG_NAME, WEIGHTS_NAME,

                                                     OpenAIGPTConfig,

                                                     OpenAIGPTModel,

                                                     load_tf_weights_in_openai_gpt)



import logging

logging.basicConfig(level=logging.INFO)





def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):

    # Construct model

    if openai_config_file == "":

        config = OpenAIGPTConfig()

    else:

        config = OpenAIGPTConfig.from_json_file(openai_config_file)

    model = OpenAIGPTModel(config)



    # Load weights from numpy

    load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path)



    # Save pytorch-model

    pytorch_weights_dump_path = pytorch_dump_folder_path + '/' + WEIGHTS_NAME

    pytorch_config_dump_path = pytorch_dump_folder_path + '/' + CONFIG_NAME

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

    torch.save(model.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(config.to_json_string())





if __name__ == "__main__":

    parser = argparse.ArgumentParser()

    ## Required parameters

    parser.add_argument("--openai_checkpoint_folder_path",

                        default = None,

                        type = str,

                        required = True,

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

    parser.add_argument("--pytorch_dump_folder_path",

                        default = None,

                        type = str,

                        required = True,

                        help = "Path to the output PyTorch model.")

    parser.add_argument("--openai_config_file",

                        default = "",

                        type = str,

                        help = "An optional config json file corresponding to the pre-trained OpenAI model. \n"

                            "This specifies the model architecture.")

    args = parser.parse_args()

    convert_openai_checkpoint_to_pytorch(args.openai_checkpoint_folder_path,

                                         args.openai_config_file,

                                         args.pytorch_dump_folder_path)

