






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




def      snake_case    (A_ :int      ,     A_ :List[str]      ,     A_ :Optional[int]      ,     A_ :Any      ,     A_ :int      ,     A_ :Optional[Any]      ,     A_ :List[Any]=None ):







  '''simple docstring'''

  a	:     Optional[int]							=  tqdm(A_ )
  for i, batch in enumerate(A_ ):
    with tf.GradientTape() as tape:
      a,	a	:     Optional[Any]							=  batch
      a	:     Optional[Any]							=  model(A_ )
      a	:     int							=  loss(A_      ,     outputs.logits )

    if SDP_ENABLED:
      a	:     Dict							=  sdp.DistributedGradientTape(A_      ,     sparse_as_dense=A_ )

    a	:     Optional[int]							=  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

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




def      snake_case    (A_ :Any      ,     A_ :List[str]      ,     A_ :int ):







  '''simple docstring'''

  a,	a	:     Optional[Any]							=  load_dataset('imdb'      ,     split=['train', 'test'] )

  # Preprocess train dataset
  a	:     str							=  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'] )

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

  # Preprocess test dataset
  a	:     Tuple							=  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'] )

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

  if SDP_ENABLED:
    a	:     Optional[Any]							=  tf_train_dataset.shard(sdp.size()      ,     sdp.rank() )
    a	:     Tuple							=  tf_test_dataset.shard(sdp.size()      ,     sdp.rank() )
  a	:     List[str]							=  tf_train_dataset.batch(A_      ,     drop_remainder=A_ )
  a	:     Optional[Any]							=  tf_test_dataset.batch(A_      ,     drop_remainder=A_ )

  return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
   _UpperCamelCase							:       str       					=		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							:       Optional[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							:       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							:       int       					=		TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   _UpperCamelCase							:       List[Any]       					=		AutoTokenizer.from_pretrained(args.model_name_or_path)

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

      _UpperCamelCase							:       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):
      _UpperCamelCase							:       Tuple       					=		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)












"""simple docstring"""


_UpperCamelCase							:       str       					=		10**8




def      snake_case    (A_ :int ):







  '''simple docstring'''

  if n % 1_0 == 0:
    return False
  a	:     Optional[int]							=  str(A_ )
  return s == s[::-1]




def      snake_case    ():







  '''simple docstring'''

  a	:     Tuple							=  set()
  a	:     Optional[Any]							=  1
  a	:     List[str]							=  5
  while sum_squares < LIMIT:
    a	:     int							=  first_square + 1
    while sum_squares < LIMIT:
      if is_palindrome(A_ ):
        answer.add(A_ )
      last_square += 1
      sum_squares += last_square**2
    first_square += 1
    a	:     List[str]							=  first_square**2 + (first_square + 1) ** 2

  return sum(A_ )


if __name__ == "__main__":
   print(solution())




