




'''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():
 lowerCamelCase_		       =						True
 lowerCamelCase_		       =						'p3dn.24xlarge'
 import smdistributed.dataparallel.tensorflow as sdp
else:
 lowerCamelCase_		       =						False
def  SCREAMING_SNAKE_CASE_ (							__A				: Tuple       ,     __A				: List[str]       ,     __A				: str       ,     __A				: int       ,     __A				: Any       ,     __A				: Union[str, Any]       ,     __A				: Optional[Any]=None  ) -> List[str]:
   _SCREAMING_SNAKE_CASE       =      tqdm(lowercase_  )
   for i, batch in enumerate(lowercase_  ):
      with tf.GradientTape() as tape:
         _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE       =      batch
         _SCREAMING_SNAKE_CASE       =      model(lowercase_  )
         _SCREAMING_SNAKE_CASE       =      loss(lowercase_       ,     outputs.logits  )

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

      _SCREAMING_SNAKE_CASE       =      tape.gradient(lowercase_       ,     model.trainable_variables  )
      opt.apply_gradients(zip(lowercase_       ,     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       =      {"loss": loss_value.numpy()}
   return train_results
def  SCREAMING_SNAKE_CASE_ (							__A				: List[str]       ,     __A				: str       ,     __A				: str  ) -> Optional[int]:
   _SCREAMING_SNAKE_CASE,       _SCREAMING_SNAKE_CASE       =      load_dataset("imdb"       ,     split=["train", "test"]  )

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

   _SCREAMING_SNAKE_CASE       =      {
       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       =      tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])  )

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

   _SCREAMING_SNAKE_CASE       =      {
       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       =      tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])  )

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

   return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
 lowerCamelCase_		       =						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'])

 lowerCamelCase_		       =						parser.parse_known_args()

 # Set up logging
 lowerCamelCase_		       =						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()

  lowerCamelCase_		       =						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
 lowerCamelCase_		       =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
 lowerCamelCase_		       =						AutoTokenizer.from_pretrained(args.model_name_or_path)

 # get datasets
 lowerCamelCase_		       =						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
 lowerCamelCase_		       =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
 lowerCamelCase_		       =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
 lowerCamelCase_		       =						[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)
  lowerCamelCase_		       =						time.time()
  lowerCamelCase_		       =						fit(
      model,
      loss,
      optimizer,
      tf_train_dataset,
      args.epochs,
      args.per_device_train_batch_size,
      max_steps=args.max_steps,
  )
  lowerCamelCase_		       =						time.time() - start_train_time
  logger.info('*** Train ***')
  logger.info(f'''train_runtime = {end_train_time}''')

  lowerCamelCase_		       =						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):
  lowerCamelCase_		       =						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  logger.info('*** Evaluate ***')

  lowerCamelCase_		       =						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 		_lowercase		(	lowercase_  ):



		'''simple docstring'''







		return "".join(chr(ord(lowercase_  ) + 32  ) if "A" <= char <= "Z" else char for char in word  )


if __name__ == "__main__":
						from doctest import testmod

						testmod()





