"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
import argparse
from sapiens_transformers import (TapasConfig, TapasForMaskedLM, TapasForQuestionAnswering, TapasForSequenceClassification, TapasModel, TapasTokenizer, load_tf_weights_in_tapas)
from sapiens_transformers.utils import logging
logging.set_verbosity_info()
def convert_tf_checkpoint_to_pytorch(task, reset_position_index_per_cell, tf_checkpoint_path, tapas_config_file, pytorch_dump_path):
    config = TapasConfig.from_json_file(tapas_config_file)
    config.reset_position_index_per_cell = reset_position_index_per_cell
    if task == "SQA": model = TapasForQuestionAnswering(config=config)
    elif task == "WTQ":
        config.num_aggregation_labels = 4
        config.use_answer_as_supervision = True
        config.answer_loss_cutoff = 0.664694
        config.cell_selection_preference = 0.207951
        config.huber_loss_delta = 0.121194
        config.init_cell_selection_weights_to_zero = True
        config.select_one_column = True
        config.allow_empty_column_selection = False
        config.temperature = 0.0352513
        model = TapasForQuestionAnswering(config=config)
    elif task == "WIKISQL_SUPERVISED":
        config.num_aggregation_labels = 4
        config.use_answer_as_supervision = False
        config.answer_loss_cutoff = 36.4519
        config.cell_selection_preference = 0.903421
        config.huber_loss_delta = 222.088
        config.init_cell_selection_weights_to_zero = True
        config.select_one_column = True
        config.allow_empty_column_selection = True
        config.temperature = 0.763141
        model = TapasForQuestionAnswering(config=config)
    elif task == "TABFACT": model = TapasForSequenceClassification(config=config)
    elif task == "MLM": model = TapasForMaskedLM(config=config)
    elif task == "INTERMEDIATE_PRETRAINING": model = TapasModel(config=config)
    else: raise ValueError(f"Task {task} not supported.")
    print(f"Building PyTorch model from configuration: {config}")
    load_tf_weights_in_tapas(model, config, tf_checkpoint_path)
    print(f"Save PyTorch model to {pytorch_dump_path}")
    model.save_pretrained(pytorch_dump_path)
    print(f"Save tokenizer files to {pytorch_dump_path}")
    tokenizer = TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt", model_max_length=512)
    tokenizer.save_pretrained(pytorch_dump_path)
    print("Used relative position embeddings:", model.config.reset_position_index_per_cell)
if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA.")
    parser.add_argument("--reset_position_index_per_cell", default=False, action="store_true", help="Whether to use relative position embeddings or not. Defaults to True.")
    parser.add_argument("--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path.")
    parser.add_argument("--tapas_config_file", default=None, type=str, required=True, help=("The config json file corresponding to the pre-trained TAPAS model. \nThis 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_tf_checkpoint_to_pytorch(args.task, args.reset_position_index_per_cell, args.tf_checkpoint_path, args.tapas_config_file, args.pytorch_dump_path)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
