"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
from . import (BertConfig, BertGenerationConfig, BertGenerationDecoder, BertGenerationEncoder, load_tf_weights_in_bert_generation, logging)
import argparse
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(tf_hub_path, pytorch_dump_path, is_encoder_named_decoder, vocab_size, is_encoder):
    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_tf_weights_in_bert_generation(model, tf_hub_path, model_class="bert", is_encoder_named_decoder=is_encoder_named_decoder, is_encoder=is_encoder)
    print(f"Save PyTorch model and config to {pytorch_dump_path}")
    model.save_pretrained(pytorch_dump_path)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    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)
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
