code: 
  
  
  '''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():
        A__	:      int				=						True
        A__	:      int				=						"""p3dn.24xlarge"""
        import smdistributed.dataparallel.tensorflow as sdp
  else:
        A__	:      Optional[Any]				=						False
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Tuple   ,					UpperCAmelCase_					:       Dict   ,					UpperCAmelCase_					:       List[Any]   ,					UpperCAmelCase_					:       Optional[Any]   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       Tuple=None							)  ->	Optional[Any]:
       __lowerCamelCase     :			int              =	tqdm(lowercase__							)
       for i, batch in enumerate(lowercase__							):
            with tf.GradientTape() as tape:
                 __lowerCamelCase       , __lowerCamelCase     :			Tuple              =	batch
                 __lowerCamelCase     :			Dict              =	model(lowercase__							)
                 __lowerCamelCase     :			str              =	loss(lowercase__   ,					outputs.logits							)
  
            if SDP_ENABLED:
                 __lowerCamelCase     :			Any              =	sdp.DistributedGradientTape(lowercase__   ,					sparse_as_dense=lowercase__							)
  
            __lowerCamelCase     :			Tuple              =	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
  
       __lowerCamelCase     :			Tuple              =	{'loss': loss_value.numpy()}
       return train_results
  
  
  
  def     UpperCAmelCase__      (			UpperCAmelCase_					:       Any   ,					UpperCAmelCase_					:       List[str]   ,					UpperCAmelCase_					:       Optional[int]							)  ->	Dict:
       # Load dataset
       __lowerCamelCase       , __lowerCamelCase     :			Optional[Any]              =	load_dataset('imdb'   ,					split=['train', 'test']							)
  
       # Preprocess train dataset
       __lowerCamelCase     :			List[str]              =	train_dataset.map(
           lambda UpperCAmelCase_							: tokenizer(e['text']   ,					truncation=lowercase__   ,					padding='max_length'							)   ,					batched=lowercase__							)
       train_dataset.set_format(type='tensorflow'   ,					columns=['input_ids', 'attention_mask', 'label']							)
  
       __lowerCamelCase     :			List[str]              =	{
           x: train_dataset[x].to_tensor(default_value=0   ,					shape=[None, tokenizer.model_max_length]							)
           for x in ['input_ids', 'attention_mask']
       }
       __lowerCamelCase     :			List[str]              =	tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])							)
  
       # Preprocess test dataset
       __lowerCamelCase     :			List[str]              =	test_dataset.map(
           lambda UpperCAmelCase_							: tokenizer(e['text']   ,					truncation=lowercase__   ,					padding='max_length'							)   ,					batched=lowercase__							)
       test_dataset.set_format(type='tensorflow'   ,					columns=['input_ids', 'attention_mask', 'label']							)
  
       __lowerCamelCase     :			List[Any]              =	{
           x: test_dataset[x].to_tensor(default_value=0   ,					shape=[None, tokenizer.model_max_length]							)
           for x in ['input_ids', 'attention_mask']
       }
       __lowerCamelCase     :			Dict              =	tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])							)
  
       if SDP_ENABLED:
            __lowerCamelCase     :			List[Any]              =	tf_train_dataset.shard(sdp.size()   ,					sdp.rank()							)
            __lowerCamelCase     :			Optional[int]              =	tf_test_dataset.shard(sdp.size()   ,					sdp.rank()							)
       __lowerCamelCase     :			Union[str, Any]              =	tf_train_dataset.batch(lowercase__   ,					drop_remainder=lowercase__							)
       __lowerCamelCase     :			Tuple              =	tf_test_dataset.batch(lowercase__   ,					drop_remainder=lowercase__							)
  
       return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
        A__	:      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"""])
  
        A__		,      A__	:      Optional[int]				=						parser.parse_known_args()
  
        # Set up logging
        A__	:      Union[str, 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()
  
              A__	:      Tuple				=						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
        A__	:      str				=						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
        A__	:      List[str]				=						AutoTokenizer.from_pretrained(args.model_name_or_path)
  
        # get datasets
        A__		,      A__	:      Optional[int]				=						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
        A__	:      int				=						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
        A__	:      Tuple				=						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
        A__	:      Union[str, 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)
              A__	:      Union[str, Any]				=						time.time()
              A__	:      Any				=						fit(
                  model,
                  loss,
                  optimizer,
                  tf_train_dataset,
                  args.epochs,
                  args.per_device_train_batch_size,
                  max_steps=args.max_steps,
              )
              A__	:      Dict				=						time.time() - start_train_time
              logger.info("""*** Train ***""")
              logger.info(f'''train_runtime = {end_train_time}''')
  
              A__	:      int				=						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):
              A__	:      Optional[Any]				=						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
              logger.info("""*** Evaluate ***""")
  
              A__	:      Any				=						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: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, Optional, Union
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  A__	:      Any				=						logging.get_logger(__name__)
  
  A__	:      str				=						{
      """microsoft/prophetnet-large-uncased""": (
          """https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json"""
      ),
  }
  
  
  
  
  
  class      UpperCAmelCase_						(_UpperCAmelCase						):
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
      lowerCamelCase						:      Dict							  =    'prophetnet'
      lowerCamelCase						:      List[str]							  =    ['past_key_values']
      lowerCamelCase						:      List[str]							  =    {
          'num_attention_heads': 'num_encoder_attention_heads',
      }
  
  
  
  
  
  
  
      def __init__(							self    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = "gelu"    ,  SCREAMING_SNAKE_CASE_ = 3_05_22    ,  SCREAMING_SNAKE_CASE_ = 10_24    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 40_96    ,  SCREAMING_SNAKE_CASE_ = 12    ,  SCREAMING_SNAKE_CASE_ = 16    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 0.1    ,  SCREAMING_SNAKE_CASE_ = 5_12    ,  SCREAMING_SNAKE_CASE_ = 0.0_2    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 2    ,  SCREAMING_SNAKE_CASE_ = 32    ,  SCREAMING_SNAKE_CASE_ = 1_28    ,  SCREAMING_SNAKE_CASE_ = False    ,  SCREAMING_SNAKE_CASE_ = 0.0    ,  SCREAMING_SNAKE_CASE_ = True    ,  SCREAMING_SNAKE_CASE_ = 0    ,  SCREAMING_SNAKE_CASE_ = 1    ,  SCREAMING_SNAKE_CASE_ = 2    ,  **SCREAMING_SNAKE_CASE_    ,  )       ->  List[Any]:
           __lowerCamelCase     :			List[Any]              =	vocab_size
           __lowerCamelCase     :			Union[str, Any]              =	hidden_size
           __lowerCamelCase     :			Tuple              =	encoder_ffn_dim
           __lowerCamelCase     :			Any              =	num_encoder_layers
           __lowerCamelCase     :			int              =	num_encoder_attention_heads
           __lowerCamelCase     :			Optional[Any]              =	decoder_ffn_dim
           __lowerCamelCase     :			str              =	num_decoder_layers
           __lowerCamelCase     :			int              =	num_decoder_attention_heads
           __lowerCamelCase     :			Union[str, Any]              =	max_position_embeddings
           __lowerCamelCase     :			Optional[Any]              =	init_std  # Normal(0, this parameter)
           __lowerCamelCase     :			Union[str, Any]              =	activation_function
  
           # parameters for prophetnet
           __lowerCamelCase     :			int              =	ngram
           __lowerCamelCase     :			Optional[int]              =	num_buckets
           __lowerCamelCase     :			str              =	relative_max_distance
           __lowerCamelCase     :			Optional[Any]              =	disable_ngram_loss
           __lowerCamelCase     :			str              =	eps
  
           # 3 Types of Dropout
           __lowerCamelCase     :			Optional[Any]              =	attention_dropout
           __lowerCamelCase     :			str              =	activation_dropout
           __lowerCamelCase     :			Optional[Any]              =	dropout
  
           __lowerCamelCase     :			Optional[Any]              =	use_cache
  
           super().__init__(
               pad_token_id=SCREAMING_SNAKE_CASE_    ,  bos_token_id=SCREAMING_SNAKE_CASE_    ,  eos_token_id=SCREAMING_SNAKE_CASE_    ,  is_encoder_decoder=SCREAMING_SNAKE_CASE_    ,  add_cross_attention=SCREAMING_SNAKE_CASE_    ,  decoder_start_token_id=SCREAMING_SNAKE_CASE_    ,  **SCREAMING_SNAKE_CASE_    ,  )
  
  
  
  
  
  
  
      @property
      def    lowercase_       (							self			)       ->  int:
           return self.num_encoder_layers + self.num_decoder_layers
  
  
  
  
  
  
  
      @num_hidden_layers.setter
      def    lowercase_       (							self    ,  SCREAMING_SNAKE_CASE_			)       ->  Union[str, Any]:
           raise NotImplementedError(
               'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
               ' `num_decoder_layers`.'			)
  
  
  
  
  
  
  