






"""simple docstring"""





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():
  A__       :			Optional[int]=						True
  A__       :			Dict=						"""p3dn.24xlarge"""
  import smdistributed.dataparallel.tensorflow as sdp
else:
  A__       :			List[str]=						False







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE=None       )       ->						int:
    """simple docstring"""

    UpperCamelCase__								=     tqdm(SCREAMING_SNAKE_CASE       )
    for i, batch in enumerate(SCREAMING_SNAKE_CASE       ):
        with tf.GradientTape() as tape:
            UpperCamelCase__							,    UpperCamelCase__								=     batch
            UpperCamelCase__								=     model(SCREAMING_SNAKE_CASE       )
            UpperCamelCase__								=     loss(SCREAMING_SNAKE_CASE			,						outputs.logits       )

        if SDP_ENABLED:
            UpperCamelCase__								=     sdp.DistributedGradientTape(SCREAMING_SNAKE_CASE			,						sparse_as_dense=SCREAMING_SNAKE_CASE       )

        UpperCamelCase__								=     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

    UpperCamelCase__								=     {'loss': loss_value.numpy()}
    return train_results







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Tuple:
    """simple docstring"""

    UpperCamelCase__							,    UpperCamelCase__								=     load_dataset('imdb'			,						split=['train', 'test']       )

    # Preprocess train dataset
    UpperCamelCase__								=     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']       )

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

    # Preprocess test dataset
    UpperCamelCase__								=     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']       )

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

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

    return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  A__       :			Optional[Any]=						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=16)
  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"""])

  A__,  A__       :			List[Any]=						parser.parse_known_args()

  # Set up logging
  A__       :			Any=						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()

    A__       :			str=						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
  A__       :			str=						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  A__       :			int=						AutoTokenizer.from_pretrained(args.model_name_or_path)

  # get datasets
  A__,  A__       :			Optional[int]=						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
  A__       :			int=						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  A__       :			List[Any]=						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  A__       :			List[str]=						[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)
    A__       :			Any=						time.time()
    A__       :			Any=						fit(
        model,
        loss,
        optimizer,
        tf_train_dataset,
        args.epochs,
        args.per_device_train_batch_size,
        max_steps=args.max_steps,
    )
    A__       :			Optional[Any]=						time.time() - start_train_time
    logger.info("""*** Train ***""")
    logger.info(F"""train_runtime = {end_train_time}""")

    A__       :			Optional[Any]=						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):
    A__       :			Optional[Any]=						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info("""*** Evaluate ***""")

    A__       :			Tuple=						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)












"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    if not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        raise ValueError('double_factorial() only accepts integral values'       )
    if num < 0:
        raise ValueError('double_factorial() not defined for negative values'       )
    UpperCamelCase__								=     1
    for i in range(SCREAMING_SNAKE_CASE			,						0			,						-2       ):
        value *= i
    return value


if __name__ == "__main__":
  import doctest

  doctest.testmod()




