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():
         lowerCamelCase_     :     List[str]    		=						True
         lowerCamelCase_     :     Dict    		=						"""p3dn.24xlarge"""
         import smdistributed.dataparallel.tensorflow as sdp
  else:
         lowerCamelCase_     :     Dict    		=						False
  def _A		(		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 _A		(		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__":
         lowerCamelCase_     :     Dict    		=						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=1_6)
         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_     :     str    		=						parser.parse_known_args()
  
         # Set up logging
         lowerCamelCase_     :     str    		=						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_     :     Dict    		=						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_     :     Any    		=						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
         lowerCamelCase_     :     List[Any]    		=						AutoTokenizer.from_pretrained(args.model_name_or_path)
  
         # get datasets
         lowerCamelCase_     :     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
         lowerCamelCase_     :     Tuple    		=						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
         lowerCamelCase_     :     Optional[Any]    		=						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
         lowerCamelCase_     :     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)
                lowerCamelCase_     :     List[str]    		=						time.time()
                lowerCamelCase_     :     List[str]    		=						fit(
                    model,
                    loss,
                    optimizer,
                    tf_train_dataset,
                    args.epochs,
                    args.per_device_train_batch_size,
                    max_steps=args.max_steps,
                )
                lowerCamelCase_     :     Union[str, Any]    		=						time.time() - start_train_time
                logger.info("""*** Train ***""")
                logger.info(F'train_runtime = {end_train_time}')
  
                lowerCamelCase_     :     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):
                lowerCamelCase_     :     List[str]    		=						model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
                logger.info("""*** Evaluate ***""")
  
                lowerCamelCase_     :     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)
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  lowerCamelCase_     :     int    		=						logging.get_logger(__name__)
  
  lowerCamelCase_     :     Optional[Any]    		=						{
      """dandelin/vilt-b32-mlm""": """https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"""
  }
  
  
  
  
  
  
  class    __A       ( _SCREAMING_SNAKE_CASE    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						"vilt"
  
  
   def __init__(	self				,					__A=3_0522				,					__A=2				,					__A=2				,					__A=40				,					__A=768				,					__A=12				,					__A=12				,					__A=3072				,					__A="gelu"				,					__A=0.0				,					__A=0.0				,					__A=0.02				,					__A=1E-1_2				,					__A=384				,					__A=32				,					__A=3				,					__A=True				,					__A=-1				,					__A=False				,					__A=-1				,					**__A				,					)      ->  Dict:
       super().__init__(tie_word_embeddings=__A				,					**__A		)
  
       a					=vocab_size
       a					=type_vocab_size
       a					=modality_type_vocab_size
       a					=max_position_embeddings
  
       a					=hidden_size
       a					=num_hidden_layers
       a					=num_attention_heads
       a					=intermediate_size
       a					=hidden_act
       a					=hidden_dropout_prob
       a					=attention_probs_dropout_prob
       a					=initializer_range
       a					=layer_norm_eps
  
       a					=image_size
       a					=patch_size
       a					=num_channels
       a					=qkv_bias
       a					=max_image_length
       a					=num_images