# Copyright 2022 Quantapix 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

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()


def load_tf_weights_in_convbert(model, config, tf_checkpoint_path):
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    init_vars = tf.train.list_variables(tf_path)
    tf_data = {}
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        tf_data[name] = array
    param_mapping = {
        "embeddings.word_embeddings.weight": "electra/embeddings/word_embeddings",
        "embeddings.position_embeddings.weight": "electra/embeddings/position_embeddings",
        "embeddings.token_type_embeddings.weight": "electra/embeddings/token_type_embeddings",
        "embeddings.LayerNorm.weight": "electra/embeddings/LayerNorm/gamma",
        "embeddings.LayerNorm.bias": "electra/embeddings/LayerNorm/beta",
        "embeddings_project.weight": "electra/embeddings_project/kernel",
        "embeddings_project.bias": "electra/embeddings_project/bias",
    }
    if config.n_groups > 1:
        group_dense_name = "g_dense"
    else:
        group_dense_name = "dense"
    for j in range(config.n_lays):
        param_mapping[
            f"encoder.layer.{j}.attention.self.query.weight"
        ] = f"electra/encoder/layer_{j}/attention/self/query/kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.self.query.bias"
        ] = f"electra/encoder/layer_{j}/attention/self/query/bias"
        param_mapping[
            f"encoder.layer.{j}.attention.self.key.weight"
        ] = f"electra/encoder/layer_{j}/attention/self/key/kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.self.key.bias"
        ] = f"electra/encoder/layer_{j}/attention/self/key/bias"
        param_mapping[
            f"encoder.layer.{j}.attention.self.value.weight"
        ] = f"electra/encoder/layer_{j}/attention/self/value/kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.self.value.bias"
        ] = f"electra/encoder/layer_{j}/attention/self/value/bias"
        param_mapping[
            f"encoder.layer.{j}.attention.self.key_conv_attn_layer.depthwise.weight"
        ] = f"electra/encoder/layer_{j}/attention/self/conv_attn_key/depthwise_kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.self.key_conv_attn_layer.pointwise.weight"
        ] = f"electra/encoder/layer_{j}/attention/self/conv_attn_key/pointwise_kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.self.key_conv_attn_layer.bias"
        ] = f"electra/encoder/layer_{j}/attention/self/conv_attn_key/bias"
        param_mapping[
            f"encoder.layer.{j}.attention.self.conv_kernel_layer.weight"
        ] = f"electra/encoder/layer_{j}/attention/self/conv_attn_kernel/kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.self.conv_kernel_layer.bias"
        ] = f"electra/encoder/layer_{j}/attention/self/conv_attn_kernel/bias"
        param_mapping[
            f"encoder.layer.{j}.attention.self.conv_out_layer.weight"
        ] = f"electra/encoder/layer_{j}/attention/self/conv_attn_point/kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.self.conv_out_layer.bias"
        ] = f"electra/encoder/layer_{j}/attention/self/conv_attn_point/bias"
        param_mapping[
            f"encoder.layer.{j}.attention.output.dense.weight"
        ] = f"electra/encoder/layer_{j}/attention/output/dense/kernel"
        param_mapping[
            f"encoder.layer.{j}.attention.output.LayerNorm.weight"
        ] = f"electra/encoder/layer_{j}/attention/output/LayerNorm/gamma"
        param_mapping[
            f"encoder.layer.{j}.attention.output.dense.bias"
        ] = f"electra/encoder/layer_{j}/attention/output/dense/bias"
        param_mapping[
            f"encoder.layer.{j}.attention.output.LayerNorm.bias"
        ] = f"electra/encoder/layer_{j}/attention/output/LayerNorm/beta"
        param_mapping[
            f"encoder.layer.{j}.intermediate.dense.weight"
        ] = f"electra/encoder/layer_{j}/intermediate/{group_dense_name}/kernel"
        param_mapping[
            f"encoder.layer.{j}.intermediate.dense.bias"
        ] = f"electra/encoder/layer_{j}/intermediate/{group_dense_name}/bias"
        param_mapping[
            f"encoder.layer.{j}.output.dense.weight"
        ] = f"electra/encoder/layer_{j}/output/{group_dense_name}/kernel"
        param_mapping[
            f"encoder.layer.{j}.output.dense.bias"
        ] = f"electra/encoder/layer_{j}/output/{group_dense_name}/bias"
        param_mapping[
            f"encoder.layer.{j}.output.LayerNorm.weight"
        ] = f"electra/encoder/layer_{j}/output/LayerNorm/gamma"
        param_mapping[
            f"encoder.layer.{j}.output.LayerNorm.bias"
        ] = f"electra/encoder/layer_{j}/output/LayerNorm/beta"
    for param in model.named_parameters():
        param_name = param[0]
        retriever = attrgetter(param_name)
        result = retriever(model)
        tf_name = param_mapping[param_name]
        value = torch.from_numpy(tf_data[tf_name])
        logger.info(f"TF: {tf_name}, PT: {param_name} ")
        if tf_name.endswith("/kernel"):
            if not tf_name.endswith("/intermediate/g_dense/kernel"):
                if not tf_name.endswith("/output/g_dense/kernel"):
                    value = value.T
        if tf_name.endswith("/depthwise_kernel"):
            value = value.permute(1, 2, 0)  # 2, 0, 1
        if tf_name.endswith("/pointwise_kernel"):
            value = value.permute(2, 1, 0)  # 2, 1, 0
        if tf_name.endswith("/conv_attn_key/bias"):
            value = value.unsqueeze(-1)
        result.data = value
    return model


def convert_orig_tf1_checkpoint_to_pytorch(
    tf_checkpoint_path, convbert_config_file, pytorch_dump_path
):
    conf = ConvBertConfig.from_json_file(convbert_config_file)
    model = ConvBertModel(conf)
    model = load_tf_weights_in_convbert(model, conf, tf_checkpoint_path)
    model.save_pretrained(pytorch_dump_path)
    tf_model = TFConvBertModel.from_pretrained(pytorch_dump_path, from_pt=True)
    tf_model.save_pretrained(pytorch_dump_path)


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(
        "--convbert_config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the pre-trained ConvBERT model. \n"
        "This specifies the model architecture.",
    )
    parser.add_argument(
        "--pytorch_dump_path",
        default=None,
        type=str,
        required=True,
        help="Path to the output PyTorch model.",
    )
    args = parser.parse_args()
    convert_orig_tf1_checkpoint_to_pytorch(
        args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path
    )
