# coding=utf-8
# Copyright 2020 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 Seq2Seq TF Hub checkpoint."""

import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()


def convert_tf_checkpoint_to_pytorch(tf_hub_path, pytorch_dump_path, is_encoder_named_decoder, vocab_size, is_encoder):
    # Initialise PyTorch model
    bert_config = BertConfig.from_pretrained(
        "google-bert/bert-large-cased",
        vocab_size=vocab_size,
        max_position_embeddings=512,
        is_decoder=True,
        add_cross_attention=True,
    )
    bert_config_dict = bert_config.to_dict()
    del bert_config_dict["type_vocab_size"]
    config = BertGenerationConfig(**bert_config_dict)
    if is_encoder:
        model = BertGenerationEncoder(config)
    else:
        model = BertGenerationDecoder(config)
    print(f"Building PyTorch model from configuration: {config}")

    # Load weights from tf checkpoint
    load_tf_weights_in_bert_generation(
        model,
        tf_hub_path,
        model_class="bert",
        is_encoder_named_decoder=is_encoder_named_decoder,
        is_encoder=is_encoder,
    )

    # Save pytorch-model
    print(f"Save PyTorch model and config to {pytorch_dump_path}")
    model.save_pretrained(pytorch_dump_path)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    parser.add_argument(
        "--is_encoder_named_decoder",
        action="store_true",
        help="If decoder has to be renamed to encoder in PyTorch model.",
    )
    parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
    parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
    args = parser.parse_args()
    convert_tf_checkpoint_to_pytorch(
        args.tf_hub_path,
        args.pytorch_dump_path,
        args.is_encoder_named_decoder,
        args.vocab_size,
        is_encoder=args.is_encoder,
    )
