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				:	Any				    =       True
  				_lowerCamelCase				:	List[str]				    =       'p3dn.24xlarge'
  				import smdistributed.dataparallel.tensorflow as sdp
  else:
  				_lowerCamelCase				:	Any				    =       False
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase=None    )      ->Any:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      tqdm(UpperCAmelCase    )
  	for i, batch in enumerate(UpperCAmelCase    ):
  		with tf.GradientTape() as tape:
  			A  ,    A							  =      batch
  			A							  =      model(UpperCAmelCase    )
  			A							  =      loss(UpperCAmelCase       ,			outputs.logits    )
  
  		if SDP_ENABLED:
  			A							  =      sdp.DistributedGradientTape(UpperCAmelCase       ,			sparse_as_dense=UpperCAmelCase    )
  
  		A							  =      tape.gradient(UpperCAmelCase       ,			model.trainable_variables    )
  		opt.apply_gradients(zip(UpperCAmelCase       ,			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 (     UpperCAmelCase       ,			UpperCAmelCase       ,			UpperCAmelCase    )      ->Optional[int]:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A  ,    A							  =      load_dataset("""imdb"""       ,			split=["""train""", """test"""]    )
  
  	# Preprocess train dataset
  	A							  =      train_dataset.map(
  	    lambda UpperCAmelCase    : tokenizer(e["""text"""]       ,			truncation=UpperCAmelCase       ,			padding="""max_length"""    )       ,			batched=UpperCAmelCase    )
  	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 UpperCAmelCase    : tokenizer(e["""text"""]       ,			truncation=UpperCAmelCase       ,			padding="""max_length"""    )       ,			batched=UpperCAmelCase    )
  	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(UpperCAmelCase       ,			drop_remainder=UpperCAmelCase    )
  	A							  =      tf_test_dataset.batch(UpperCAmelCase       ,			drop_remainder=UpperCAmelCase    )
  
  	return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
  				_lowerCamelCase				:	Optional[int]				    =       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'])
  
  				_lowerCamelCase				:	Union[str, Any]				    =       parser.parse_known_args()
  
  				# Set up logging
  				_lowerCamelCase				:	List[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				:	Optional[Any]				    =       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				:	Union[str, Any]				    =       TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  				_lowerCamelCase				:	Optional[int]				    =       AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  				# get datasets
  				_lowerCamelCase				:	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
  				_lowerCamelCase				:	List[str]				    =       tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  				_lowerCamelCase				:	Any				    =       tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  				_lowerCamelCase				:	Tuple				    =       [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				:	Optional[int]				    =       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				:	str				    =       time.time() - start_train_time
  								logger.info('*** Train ***')
  								logger.info(f"train_runtime = {end_train_time}")
  
  								_lowerCamelCase				:	List[str]				    =       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				:	Any				    =       model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  								logger.info('*** Evaluate ***')
  
  								_lowerCamelCase				:	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'''
  
  
  _lowerCamelCase				:	List[str]				    =       [[0, 1, 1], [0, 0, 1], [1, 0, 0]]
  
  
  
  class 							__UpperCAmelCase  :
  
  
  
  
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def __init__(self						:      List[Any]      ,      _lowerCAmelCase						:      List[Any]      ):
  			A							  =      name
  			A							  =      []
  			A							  =      []
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      str      ):
  			self.inbound.append(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def        A     (self						:      Dict      ,      _lowerCAmelCase						:      Any      ):
  			self.outbound.append(_lowerCAmelCase      )
  
  
  
  
  
  
  
  		def __repr__(self						:      Tuple      ):
  			return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""
  
  
  
  
  
  
  def    __a (     UpperCAmelCase       ,			UpperCAmelCase=3       ,			UpperCAmelCase=0.85    )      ->Tuple:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      {}
  	for node in nodes:
  		A							  =      1
  
  	A							  =      {}
  	for node in nodes:
  		A							  =      len(node.outbound    )
  
  	for i in range(UpperCAmelCase    ):
  		print(f"""======= Iteration {i + 1} ======="""    )
  		for _, node in enumerate(UpperCAmelCase    ):
  			A							  =      (1 - d) + d * sum(
  			    ranks[ib] / outbounds[ib] for ib in node.inbound    )
  		print(UpperCAmelCase    )
  
  
  
  
  def    __a (     )      ->Any:
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	A							  =      list(input("""Enter Names of the Nodes: """    ).split()    )
  
  	A							  =      [Node(UpperCAmelCase    ) for name in names]
  
  	for ri, row in enumerate(UpperCAmelCase    ):
  		for ci, col in enumerate(UpperCAmelCase    ):
  			if col == 1:
  				nodes[ci].add_inbound(names[ri]    )
  				nodes[ri].add_outbound(names[ci]    )
  
  	print("""======= Nodes ======="""    )
  	for node in nodes:
  		print(UpperCAmelCase    )
  
  	page_rank(UpperCAmelCase    )
  
  
  if __name__ == "__main__":
  				main()
  
  