code: 
  
  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():
   __snake_case							      =						True
   __snake_case							      =						"""p3dn.24xlarge"""
   import smdistributed.dataparallel.tensorflow as sdp
  else:
   __snake_case							      =						False
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[Any]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			List[str]  ,						SCREAMING_SNAKE_CASE__						:			str  ,						SCREAMING_SNAKE_CASE__						:			Any  ,						SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			Optional[int]=None ):
      UpperCamelCase :Optional[int]										=     tqdm(SCREAMING_SNAKE_CASE__ )
      for i, batch in enumerate(SCREAMING_SNAKE_CASE__ ):
          with tf.GradientTape() as tape:
              UpperCamelCase							,     UpperCamelCase :Optional[Any]										=     batch
              UpperCamelCase :Any										=     model(SCREAMING_SNAKE_CASE__ )
              UpperCamelCase :int										=     loss(SCREAMING_SNAKE_CASE__  ,						outputs.logits )
  
          if SDP_ENABLED:
              UpperCamelCase :List[Any]										=     sdp.DistributedGradientTape(SCREAMING_SNAKE_CASE__  ,						sparse_as_dense=SCREAMING_SNAKE_CASE__ )
  
          UpperCamelCase :str										=     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 :Union[str, Any]										=     {'''loss''': loss_value.numpy()}
      return train_results
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			Optional[int]  ,						SCREAMING_SNAKE_CASE__						:			int  ,						SCREAMING_SNAKE_CASE__						:			Any ):
      UpperCamelCase							,     UpperCamelCase :Union[str, Any]										=     load_dataset('''imdb'''  ,						split=['''train''', '''test'''] )
  
      # Preprocess train dataset
      UpperCamelCase :List[str]										=     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 :Optional[Any]										=     {
          x: train_dataset[x].to_tensor(default_value=0  ,						shape=[None, tokenizer.model_max_length] )
          for x in ['''input_ids''', '''attention_mask''']
      }
      UpperCamelCase :List[Any]										=     tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label''']) )
  
      # Preprocess test dataset
      UpperCamelCase :str										=     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 :int										=     {
          x: test_dataset[x].to_tensor(default_value=0  ,						shape=[None, tokenizer.model_max_length] )
          for x in ['''input_ids''', '''attention_mask''']
      }
      UpperCamelCase :Dict										=     tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label''']) )
  
      if SDP_ENABLED:
          UpperCamelCase :Tuple										=     tf_train_dataset.shard(sdp.size()  ,						sdp.rank() )
          UpperCamelCase :str										=     tf_test_dataset.shard(sdp.size()  ,						sdp.rank() )
      UpperCamelCase :List[str]										=     tf_train_dataset.batch(SCREAMING_SNAKE_CASE__  ,						drop_remainder=SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :int										=     tf_test_dataset.batch(SCREAMING_SNAKE_CASE__  ,						drop_remainder=SCREAMING_SNAKE_CASE__ )
  
      return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
   __snake_case							      =						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"""])
  
   __snake_case							      =						parser.parse_known_args()
  
   # Set up logging
   __snake_case							      =						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()
  
    __snake_case							      =						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
   __snake_case							      =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   __snake_case							      =						AutoTokenizer.from_pretrained(args.model_name_or_path)
  
   # get datasets
   __snake_case							      =						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
   __snake_case							      =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   __snake_case							      =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   __snake_case							      =						[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)
    __snake_case							      =						time.time()
    __snake_case							      =						fit(
        model,
        loss,
        optimizer,
        tf_train_dataset,
        args.epochs,
        args.per_device_train_batch_size,
        max_steps=args.max_steps,
    )
    __snake_case							      =						time.time() - start_train_time
    logger.info("""*** Train ***""")
    logger.info(f'''train_runtime = {end_train_time}''')
  
    __snake_case							      =						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):
    __snake_case							      =						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info("""*** Evaluate ***""")
  
    __snake_case							      =						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)
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int ):
      UpperCamelCase :int										=     {1}
      UpperCamelCase :Optional[int]										=     [1]
      for _ in range(2  ,						depth + 1 ):
          UpperCamelCase :Union[str, Any]										=     [*previous_coefficients, 0]
          UpperCamelCase :List[str]										=     [0, *previous_coefficients]
          UpperCamelCase :List[str]										=     []
          for x, y in zip(SCREAMING_SNAKE_CASE__  ,						SCREAMING_SNAKE_CASE__ ):
              coefficients.add(x + y )
              previous_coefficients.append(x + y )
      return coefficients
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			set[int] ):
      UpperCamelCase :Union[str, Any]										=     set()
      for number in unique_coefficients:
          UpperCamelCase :Optional[int]										=     2
          UpperCamelCase :List[Any]										=     number
          while divisor**2 <= copy_number:
              UpperCamelCase :List[Any]										=     0
              while copy_number % divisor == 0:
                  copy_number //= divisor
                  multiplicity += 1
              if multiplicity >= 2:
                  non_squarefrees.add(SCREAMING_SNAKE_CASE__ )
                  break
              divisor += 1
  
      return unique_coefficients.difference(SCREAMING_SNAKE_CASE__ )
  
  
  def 						_A     (       SCREAMING_SNAKE_CASE__						:			int = 51 ):
      UpperCamelCase :Any										=     get_pascal_triangle_unique_coefficients(SCREAMING_SNAKE_CASE__ )
      UpperCamelCase :str										=     get_squarefrees(SCREAMING_SNAKE_CASE__ )
      return sum(SCREAMING_SNAKE_CASE__ )
  
  
  if __name__ == "__main__":
   print(f'''{solution() = }''')
  
  
  
  
  
  