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():
  							__lowerCAmelCase				      =					True
  							__lowerCAmelCase				      =					'p3dn.24xlarge'
  							import smdistributed.dataparallel.tensorflow as sdp
  else:
  							__lowerCAmelCase				      =					False
  
  
  def       a			(   a		,	a		,	a		,	a		,	a		,	a		,	a=None  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   tqdm(a  )
  			for i, batch in enumerate(a  ):
  						with tf.GradientTape() as tape:
  									SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   batch
  									SCREAMING_SNAKE_CASE    	=   model(a  )
  									SCREAMING_SNAKE_CASE    	=   loss(a		,	outputs.logits  )
  
  						if SDP_ENABLED:
  									SCREAMING_SNAKE_CASE    	=   sdp.DistributedGradientTape(a		,	sparse_as_dense=a  )
  
  						SCREAMING_SNAKE_CASE    	=   tape.gradient(a		,	model.trainable_variables  )
  						opt.apply_gradients(zip(a		,	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
  
  			SCREAMING_SNAKE_CASE    	=   {'''loss''': loss_value.numpy()}
  			return train_results
  
  
  def       a			(   a		,	a		,	a  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   load_dataset('''imdb'''		,	split=['''train''', '''test''']  )
  
  			# Preprocess train dataset
  			SCREAMING_SNAKE_CASE    	=   train_dataset.map(
  			    lambda a  : tokenizer(e['''text''']		,	truncation=a		,	padding='''max_length'''  )		,	batched=a  )
  			train_dataset.set_format(type='''tensorflow'''		,	columns=['''input_ids''', '''attention_mask''', '''label''']  )
  
  			SCREAMING_SNAKE_CASE    	=   {
  			    x: train_dataset[x].to_tensor(default_value=0		,	shape=[None, tokenizer.model_max_length]  )
  			    for x in ['''input_ids''', '''attention_mask''']
  			}
  			SCREAMING_SNAKE_CASE    	=   tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])  )
  
  			# Preprocess test dataset
  			SCREAMING_SNAKE_CASE    	=   test_dataset.map(
  			    lambda a  : tokenizer(e['''text''']		,	truncation=a		,	padding='''max_length'''  )		,	batched=a  )
  			test_dataset.set_format(type='''tensorflow'''		,	columns=['''input_ids''', '''attention_mask''', '''label''']  )
  
  			SCREAMING_SNAKE_CASE    	=   {
  			    x: test_dataset[x].to_tensor(default_value=0		,	shape=[None, tokenizer.model_max_length]  )
  			    for x in ['''input_ids''', '''attention_mask''']
  			}
  			SCREAMING_SNAKE_CASE    	=   tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])  )
  
  			if SDP_ENABLED:
  						SCREAMING_SNAKE_CASE    	=   tf_train_dataset.shard(sdp.size()		,	sdp.rank()  )
  						SCREAMING_SNAKE_CASE    	=   tf_test_dataset.shard(sdp.size()		,	sdp.rank()  )
  			SCREAMING_SNAKE_CASE    	=   tf_train_dataset.batch(a		,	drop_remainder=a  )
  			SCREAMING_SNAKE_CASE    	=   tf_test_dataset.batch(a		,	drop_remainder=a  )
  
  			return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
  							__lowerCAmelCase				      =					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				      =					parser.parse_known_args()
  
  							# Set up logging
  							__lowerCAmelCase				      =					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				      =					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				      =					TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  							__lowerCAmelCase				      =					AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  							# get datasets
  							__lowerCAmelCase				      =					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				      =					tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  							__lowerCAmelCase				      =					tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  							__lowerCAmelCase				      =					[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				      =					time.time()
  														__lowerCAmelCase				      =					fit(
  														    model,
  														    loss,
  														    optimizer,
  														    tf_train_dataset,
  														    args.epochs,
  														    args.per_device_train_batch_size,
  														    max_steps=args.max_steps,
  														)
  														__lowerCAmelCase				      =					time.time() - start_train_time
  														logger.info('*** Train ***')
  														logger.info(F'''train_runtime = {end_train_time}''')
  
  														__lowerCAmelCase				      =					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				      =					model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  														logger.info('*** Evaluate ***')
  
  														__lowerCAmelCase				      =					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: 
  import numpy as np
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Any      ,    lowercase			:List[Any]=5      ,    lowercase			:Optional[Any]=5 )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   depth
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   min_leaf_size
  				SCREAMING_SNAKE_CASE    	=   None
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:int      ,    lowercase			:int )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if labels.ndim != 1:
  							print('''Error: Input labels must be one dimensional''' )
  
  				return np.mean((labels - prediction) ** 2 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:Dict )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if x.ndim != 1:
  							print('''Error: Input data set must be one dimensional''' )
  							return
  				if len(lowercase ) != len(lowercase ):
  							print('''Error: X and y have different lengths''' )
  							return
  				if y.ndim != 1:
  							print('''Error: Data set labels must be one dimensional''' )
  							return
  
  				if len(lowercase ) < 2 * self.min_leaf_size:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				if self.depth == 1:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(lowercase      ,    np.mean(lowercase ) ) * 2
  				for i in range(len(lowercase ) ):
  							if len(x[:i] ) < self.min_leaf_size:
  										continue
  							elif len(x[i:] ) < self.min_leaf_size:
  										continue
  							else:
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[:i]      ,    np.mean(y[:i] ) )
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[i:]      ,    np.mean(y[i:] ) )
  										SCREAMING_SNAKE_CASE    	=   error_left + error_right
  										if error < min_error:
  													SCREAMING_SNAKE_CASE    	=   i
  													SCREAMING_SNAKE_CASE    	=   error
  
  				if best_split != 0:
  							SCREAMING_SNAKE_CASE    	=   x[:best_split]
  							SCREAMING_SNAKE_CASE    	=   y[:best_split]
  							SCREAMING_SNAKE_CASE    	=   x[best_split:]
  							SCREAMING_SNAKE_CASE    	=   y[best_split:]
  
  							SCREAMING_SNAKE_CASE    	=   x[best_split]
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							self.left.train(lowercase      ,    lowercase )
  							self.right.train(lowercase      ,    lowercase )
  				else:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  
  				return
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[Any] )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if self.prediction is not None:
  							return self.prediction
  				elif self.left or self.right is not None:
  							if x >= self.decision_boundary:
  										return self.right.predict(lowercase )
  							else:
  										return self.left.predict(lowercase )
  				else:
  							print('''Error: Decision tree not yet trained''' )
  							return None
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	@staticmethod
  	def    snake_case__    (    lowercase			:Optional[int]      ,    lowercase			:Optional[Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   float(0 )
  				for label in labels:
  							squared_error_sum += (label - prediction) ** 2
  
  				return float(squared_error_sum / labels.size )
  
  
  
  def       a			(   )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   np.arange(-1.0		,	1.0		,	0.0_05  )
  			SCREAMING_SNAKE_CASE    	=   np.sin(a  )
  
  			SCREAMING_SNAKE_CASE    	=   DecisionTree(depth=10		,	min_leaf_size=10  )
  			tree.train(a		,	a  )
  
  			SCREAMING_SNAKE_CASE    	=   (np.random.rand(10  ) * 2) - 1
  			SCREAMING_SNAKE_CASE    	=   np.array([tree.predict(a  ) for x in test_cases]  )
  			SCREAMING_SNAKE_CASE    	=   np.mean((predictions - test_cases) ** 2  )
  
  			print('''Test values: ''' + str(a  )  )
  			print('''Predictions: ''' + str(a  )  )
  			print('''Average error: ''' + str(a  )  )
  
  
  if __name__ == "__main__":
  							main()
  							import doctest
  
  							doctest.testmod(name='mean_squarred_error', verbose=True)