



'''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():
  lowercase											=      True
  lowercase											=      '''p3dn.24xlarge'''
  import smdistributed.dataparallel.tensorflow as sdp
else:
  lowercase											=      False






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							,						lowercase__							,						lowercase__=None							):


     '''simple docstring'''







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

          if SDP_ENABLED:
               a_									=sdp.DistributedGradientTape(lowercase__							,						sparse_as_dense=lowercase__							)

          a_									=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

     a_									={"loss": loss_value.numpy()}
     return train_results






def 			UpperCAmelCase_							(		lowercase__							,						lowercase__							,						lowercase__							):


     '''simple docstring'''







     a_		,    a_									=load_dataset("imdb"							,						split=["train", "test"]							)

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

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

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

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

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

     return tf_train_dataset, tf_test_dataset


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

  lowercase							,   lowercase											=      parser.parse_known_args()

  # Set up logging
  lowercase											=      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()

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

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

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

    lowercase											=      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'''


from math import floor, sqrt






def 			UpperCAmelCase_							(		lowercase__							):


     '''simple docstring'''







     a_									=0.0
     a_									=1.0
     a_									=int(sqrt(lowercase__							)							)
     a_									=root
     a_									=0
     while integer_part != 2 * root:
          a_									=denominator * integer_part - numerator
          a_									=(n - numerator**2) / denominator
          a_									=int((root + numerator) / denominator							)
          period += 1
     return period






def 			UpperCAmelCase_							(		lowercase__ = 1_0_0_0_0							):


     '''simple docstring'''







     a_									=0
     for i in range(2							,						n + 1							):
          a_									=sqrt(lowercase__							)
          if sr - floor(lowercase__							) != 0 and continuous_fraction_period(lowercase__							) % 2 == 1:
               count_odd_periods += 1
     return count_odd_periods


if __name__ == "__main__":
  print(F"""{solution(int(input().strip()))}""")


