"""
    #################################################################################################################################################################################################
    # 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 os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available(): raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")
USE_XLA = False
USE_AMP = False
def train_command_factory(args: Namespace): return TrainCommand(args)
class TrainCommand(BaseTransformersCLICommand):
    @staticmethod
    def register_subcommand(parser: ArgumentParser):
        train_parser = parser.add_parser("train", help="CLI tool to train a model on a task.")
        train_parser.add_argument("--train_data", type=str, required=True, help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.")
        train_parser.add_argument("--column_label", type=int, default=0, help="Column of the dataset csv file with example labels.")
        train_parser.add_argument("--column_text", type=int, default=1, help="Column of the dataset csv file with example texts.")
        train_parser.add_argument("--column_id", type=int, default=2, help="Column of the dataset csv file with example ids.")
        train_parser.add_argument("--skip_first_row", action="store_true", help="Skip the first row of the csv file (headers).")
        train_parser.add_argument("--validation_data", type=str, default="", help="path to validation dataset.")
        train_parser.add_argument("--validation_split", type=float, default=0.1, help="if validation dataset is not provided, fraction of train dataset to use as validation dataset.")
        train_parser.add_argument("--output", type=str, default="./", help="path to saved the trained model.")
        train_parser.add_argument("--task", type=str, default="text_classification", help="Task to train the model on.")
        train_parser.add_argument("--model", type=str, default="google-bert/bert-base-uncased", help="Model's name or path to stored model.")
        train_parser.add_argument("--train_batch_size", type=int, default=32, help="Batch size for training.")
        train_parser.add_argument("--valid_batch_size", type=int, default=64, help="Batch size for validation.")
        train_parser.add_argument("--learning_rate", type=float, default=3e-5, help="Learning rate.")
        train_parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon for Adam optimizer.")
        train_parser.set_defaults(func=train_command_factory)
    def __init__(self, args: Namespace):
        self.logger = logging.get_logger("sapiens_transformers-cli/training")
        self.framework = "tf" if is_tf_available() else "torch"
        os.makedirs(args.output, exist_ok=True)
        self.output = args.output
        self.column_label = args.column_label
        self.column_text = args.column_text
        self.column_id = args.column_id
        self.logger.info(f"Loading {args.task} pipeline for {args.model}")
        if args.task == "text_classification": self.pipeline = TextClassificationPipeline.from_pretrained(args.model)
        elif args.task == "token_classification": raise NotImplementedError
        elif args.task == "question_answering": raise NotImplementedError
        self.logger.info(f"Loading dataset from {args.train_data}")
        self.train_dataset = Processor.create_from_csv(args.train_data, column_label=args.column_label, column_text=args.column_text, column_id=args.column_id, skip_first_row=args.skip_first_row)
        self.valid_dataset = None
        if args.validation_data:
            self.logger.info(f"Loading validation dataset from {args.validation_data}")
            self.valid_dataset = Processor.create_from_csv(args.validation_data, column_label=args.column_label, column_text=args.column_text, column_id=args.column_id, skip_first_row=args.skip_first_row)
        self.validation_split = args.validation_split
        self.train_batch_size = args.train_batch_size
        self.valid_batch_size = args.valid_batch_size
        self.learning_rate = args.learning_rate
        self.adam_epsilon = args.adam_epsilon
    def run(self):
        if self.framework == "tf": return self.run_tf()
        return self.run_torch()
    def run_torch(self): raise NotImplementedError
    def run_tf(self):
        self.pipeline.fit(self.train_dataset, validation_data=self.valid_dataset, validation_split=self.validation_split, learning_rate=self.learning_rate, adam_epsilon=self.adam_epsilon, train_batch_size=self.train_batch_size, valid_batch_size=self.valid_batch_size)
        self.pipeline.save_pretrained(self.output)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
