

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__			:  int					     =     True
  UpperCAmelCase__			:  Dict					     =     """p3dn.24xlarge"""
  import smdistributed.dataparallel.tensorflow as sdp
else:
  UpperCAmelCase__			:  Any					     =     False






def        __lowercase   (  _A    ,     _A    ,     _A    ,     _A    ,     _A    ,     _A    ,     _A=None      )  ->    Dict:
      SCREAMING_SNAKE_CASE			:   Dict	  = tqdm(_A      )
      for i, batch in enumerate(_A      ):
            with tf.GradientTape() as tape:
                  SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Optional[int]	  = batch
                  SCREAMING_SNAKE_CASE			:   int	  = model(_A      )
                  SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = loss(_A    ,     outputs.logits      )

            if SDP_ENABLED:
                  SCREAMING_SNAKE_CASE			:   List[str]	  = sdp.DistributedGradientTape(_A    ,     sparse_as_dense=_A      )

            SCREAMING_SNAKE_CASE			:   Dict	  = tape.gradient(_A    ,     model.trainable_variables      )
            opt.apply_gradients(zip(_A    ,     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

      SCREAMING_SNAKE_CASE			:   List[Any]	  = {"""loss""": loss_value.numpy()}
      return train_results






def        __lowercase   (  _A    ,     _A    ,     _A      )  ->    Optional[Any]:
      # Load dataset
      SCREAMING_SNAKE_CASE  ,						SCREAMING_SNAKE_CASE			:   Dict	  = load_dataset("""imdb"""    ,     split=["""train""", """test"""]      )

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

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

      # Preprocess test dataset
      SCREAMING_SNAKE_CASE			:   Optional[Any]	  = test_dataset.map(
          lambda _A      : tokenizer(e["""text"""]    ,     truncation=_A    ,     padding="""max_length"""      )    ,     batched=_A      )
      test_dataset.set_format(type="""tensorflow"""    ,     columns=["""input_ids""", """attention_mask""", """label"""]      )

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

      if SDP_ENABLED:
            SCREAMING_SNAKE_CASE			:   Dict	  = tf_train_dataset.shard(sdp.size()    ,     sdp.rank()      )
            SCREAMING_SNAKE_CASE			:   Dict	  = tf_test_dataset.shard(sdp.size()    ,     sdp.rank()      )
      SCREAMING_SNAKE_CASE			:   Optional[int]	  = tf_train_dataset.batch(_A    ,     drop_remainder=_A      )
      SCREAMING_SNAKE_CASE			:   Union[str, Any]	  = tf_test_dataset.batch(_A    ,     drop_remainder=_A      )

      return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  UpperCAmelCase__			:  Tuple					     =     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"""])

  UpperCAmelCase__     , UpperCAmelCase__			:  Tuple					     =     parser.parse_known_args()

  # Set up logging
  UpperCAmelCase__			:  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()

    UpperCAmelCase__			:  Optional[int]					     =     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__			:  Optional[int]					     =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  UpperCAmelCase__			:  Dict					     =     AutoTokenizer.from_pretrained(args.model_name_or_path)

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

    UpperCAmelCase__			:  List[str]					     =     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__			:  List[Any]					     =     model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info("""*** Evaluate ***""")

    UpperCAmelCase__			:  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)







import safs







class 							a__							( safs.SaFileSystem							):







  """simple docstring"""




