



import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("SDP_ENABLED") or is_sagemaker_dp_enabled():
    UpperCAmelCase_          =				True
    UpperCAmelCase_          =				"p3dn.24xlarge"
    import smdistributed.dataparallel.tensorflow as sdp
else:
    UpperCAmelCase_          =				False
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						int  ,					_SCREAMING_SNAKE_CASE :						Dict  ,					_SCREAMING_SNAKE_CASE :						Union[str, Any]  ,					_SCREAMING_SNAKE_CASE :						str  ,					_SCREAMING_SNAKE_CASE :						str  ,					_SCREAMING_SNAKE_CASE :						List[Any]  ,					_SCREAMING_SNAKE_CASE :						int=None					)->List[str]:
      _lowerCAmelCase			      =	tqdm(_SCREAMING_SNAKE_CASE					)
      for i, batch in enumerate(_SCREAMING_SNAKE_CASE					):
            with tf.GradientTape() as tape:
                  _lowerCAmelCase      , _lowerCAmelCase			      =	batch
                  _lowerCAmelCase			      =	model(_SCREAMING_SNAKE_CASE					)
                  _lowerCAmelCase			      =	loss(_SCREAMING_SNAKE_CASE  ,					outputs.logits					)

            if SDP_ENABLED:
                  _lowerCAmelCase			      =	sdp.DistributedGradientTape(_SCREAMING_SNAKE_CASE  ,					sparse_as_dense=_SCREAMING_SNAKE_CASE					)

            _lowerCAmelCase			      =	tape.gradient(_SCREAMING_SNAKE_CASE  ,					model.trainable_variables					)
            opt.apply_gradients(zip(_SCREAMING_SNAKE_CASE  ,					model.trainable_variables					)					)

            pbar.set_description(f'''Loss: {loss_value:.4f}'''					)

            if SDP_ENABLED and i == 0:
                  sdp.broadcast_variables(model.variables  ,					root_rank=0					)
                  sdp.broadcast_variables(opt.variables()  ,					root_rank=0					)

            if max_steps and i >= max_steps:
                  break

      _lowerCAmelCase			      =	{'''loss''': loss_value.numpy()}
      return train_results
def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						Optional[Any]  ,					_SCREAMING_SNAKE_CASE :						Tuple  ,					_SCREAMING_SNAKE_CASE :						Any					)->Optional[Any]:
      # Load dataset
      _lowerCAmelCase      , _lowerCAmelCase			      =	load_dataset('''imdb'''  ,					split=['''train''', '''test''']					)

      # Preprocess train dataset
      _lowerCAmelCase			      =	train_dataset.map(
          lambda _SCREAMING_SNAKE_CASE					: tokenizer(e['''text''']  ,					truncation=_SCREAMING_SNAKE_CASE  ,					padding='''max_length'''					)  ,					batched=_SCREAMING_SNAKE_CASE					)
      train_dataset.set_format(type='''tensorflow'''  ,					columns=['''input_ids''', '''attention_mask''', '''label''']					)

      _lowerCAmelCase			      =	{
          x: train_dataset[x].to_tensor(default_value=0  ,					shape=[None, tokenizer.model_max_length]					)
          for x in ['''input_ids''', '''attention_mask''']
      }
      _lowerCAmelCase			      =	tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])					)

      # Preprocess test dataset
      _lowerCAmelCase			      =	test_dataset.map(
          lambda _SCREAMING_SNAKE_CASE					: tokenizer(e['''text''']  ,					truncation=_SCREAMING_SNAKE_CASE  ,					padding='''max_length'''					)  ,					batched=_SCREAMING_SNAKE_CASE					)
      test_dataset.set_format(type='''tensorflow'''  ,					columns=['''input_ids''', '''attention_mask''', '''label''']					)

      _lowerCAmelCase			      =	{
          x: test_dataset[x].to_tensor(default_value=0  ,					shape=[None, tokenizer.model_max_length]					)
          for x in ['''input_ids''', '''attention_mask''']
      }
      _lowerCAmelCase			      =	tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])					)

      if SDP_ENABLED:
            _lowerCAmelCase			      =	tf_train_dataset.shard(sdp.size()  ,					sdp.rank()					)
            _lowerCAmelCase			      =	tf_test_dataset.shard(sdp.size()  ,					sdp.rank()					)
      _lowerCAmelCase			      =	tf_train_dataset.batch(_SCREAMING_SNAKE_CASE  ,					drop_remainder=_SCREAMING_SNAKE_CASE					)
      _lowerCAmelCase			      =	tf_test_dataset.batch(_SCREAMING_SNAKE_CASE  ,					drop_remainder=_SCREAMING_SNAKE_CASE					)

      return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
    UpperCAmelCase_          =				argparse.ArgumentParser()

    # Hyperparameters sent by the client are passed as command-line arguments to the script.
    parser.add_argument("--epochs", type=int, default=3)
    parser.add_argument("--per_device_train_batch_size", type=int, default=1_6)
    parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
    parser.add_argument("--model_name_or_path", type=str)
    parser.add_argument("--learning_rate", type=str, default=5E-5)
    parser.add_argument("--do_train", type=bool, default=True)
    parser.add_argument("--do_eval", type=bool, default=True)
    parser.add_argument("--output_dir", type=str)
    parser.add_argument("--max_steps", type=int, default=None)

    # Data, model, and output directories
    parser.add_argument("--output_data_dir", type=str, default=os.environ["SM_OUTPUT_DATA_DIR"])
    parser.add_argument("--model_dir", type=str, default=os.environ["SM_MODEL_DIR"])
    parser.add_argument("--n_gpus", type=str, default=os.environ["SM_NUM_GPUS"])

    UpperCAmelCase_      ,						UpperCAmelCase_          =				parser.parse_known_args()

    # Set up logging
    UpperCAmelCase_          =				logging.getLogger(__name__)

    logging.basicConfig(
        level=logging.getLevelName("INFO"),
        handlers=[logging.StreamHandler(sys.stdout)],
        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    )

    if SDP_ENABLED:
        sdp.init()

        UpperCAmelCase_          =				tf.config.experimental.list_physical_devices("GPU")
        for gpu in gpus:
            tf.config.experimental.set_memory_growth(gpu, True)
        if gpus:
            tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], "GPU")

    # Load model and tokenizer
    UpperCAmelCase_          =				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
    UpperCAmelCase_          =				AutoTokenizer.from_pretrained(args.model_name_or_path)

    # get datasets
    UpperCAmelCase_      ,						UpperCAmelCase_          =				get_datasets(
        tokenizer=tokenizer,
        train_batch_size=args.per_device_train_batch_size,
        eval_batch_size=args.per_device_eval_batch_size,
    )

    # fine optimizer and loss
    UpperCAmelCase_          =				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
    UpperCAmelCase_          =				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
    UpperCAmelCase_          =				[tf.keras.metrics.SparseCategoricalAccuracy()]
    model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

    # Training
    if args.do_train:
        # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
        UpperCAmelCase_          =				time.time()
        UpperCAmelCase_          =				fit(
            model,
            loss,
            optimizer,
            tf_train_dataset,
            args.epochs,
            args.per_device_train_batch_size,
            max_steps=args.max_steps,
        )
        UpperCAmelCase_          =				time.time() - start_train_time
        logger.info("*** Train ***")
        logger.info(F"""train_runtime = {end_train_time}""")

        UpperCAmelCase_          =				os.path.join(args.output_dir, "train_results.txt")

        if not SDP_ENABLED or sdp.rank() == 0:
            with open(output_eval_file, "w") as writer:
                logger.info("***** Train results *****")
                logger.info(train_results)
                for key, value in train_results.items():
                    logger.info(F"""  {key} = {value}""")
                    writer.write(F"""{key} = {value}\n""")

    # Evaluation
    if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
        UpperCAmelCase_          =				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
        logger.info("*** Evaluate ***")

        UpperCAmelCase_          =				os.path.join(args.output_dir, "eval_results.txt")

        with open(output_eval_file, "w") as writer:
            logger.info("***** Eval results *****")
            logger.info(result)
            for key, value in result.items():
                logger.info(F"""  {key} = {value}""")
                writer.write(F"""{key} = {value}\n""")

    # Save result
    if SDP_ENABLED:
        if sdp.rank() == 0:
            model.save_pretrained(args.output_dir)
            tokenizer.save_pretrained(args.output_dir)
    else:
        model.save_pretrained(args.output_dir)
        tokenizer.save_pretrained(args.output_dir)




def 				UpperCAmelCase__		(					_SCREAMING_SNAKE_CASE :						str					)->str:
      return " ".join(sorted(set(sentence.split()					)					)					)


if __name__ == "__main__":
    import doctest

    doctest.testmod()