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():
  			UpperCamelCase_	:    List[Any]  					=       True
  			UpperCamelCase_	:    int  					=       """p3dn.24xlarge"""
  			import smdistributed.dataparallel.tensorflow as sdp
  else:
  			UpperCamelCase_	:    List[str]  					=       False
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      int   , _UpperCAmelCase     :      Dict   , _UpperCAmelCase     :      str   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      List[Any]   , _UpperCAmelCase     :      Optional[int]   , _UpperCAmelCase     :      Dict=None     )      -> Optional[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase					:  str    			=      tqdm(lowercase__     )
  						for i, batch in enumerate(lowercase__     ):
  												with tf.GradientTape() as tape:
  																		_lowercase			,						_lowercase					:  Optional[Any]    			=      batch
  																		_lowercase					:  List[Any]    			=      model(lowercase__     )
  																		_lowercase					:  Optional[int]    			=      loss(lowercase__   , outputs.logits     )
  
  												if SDP_ENABLED:
  																		_lowercase					:  int    			=      sdp.DistributedGradientTape(lowercase__   , sparse_as_dense=lowercase__     )
  
  												_lowercase					:  Any    			=      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
  
  						_lowercase					:  int    			=      {"loss": loss_value.numpy()}
  						return train_results
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      Union[str, Any]   , _UpperCAmelCase     :      Any   , _UpperCAmelCase     :      int     )      -> Optional[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase			,						_lowercase					:  Optional[int]    			=      load_dataset("imdb"   , split=["train", "test"]     )
  
  						# Preprocess train dataset
  						_lowercase					:  Any    			=      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"]     )
  
  						_lowercase					:  List[str]    			=      {
  						    x: train_dataset[x].to_tensor(default_value=0   , shape=[None, tokenizer.model_max_length]     )
  						    for x in ["input_ids", "attention_mask"]
  						}
  						_lowercase					:  Tuple    			=      tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])     )
  
  						# Preprocess test dataset
  						_lowercase					:  Optional[int]    			=      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"]     )
  
  						_lowercase					:  str    			=      {
  						    x: test_dataset[x].to_tensor(default_value=0   , shape=[None, tokenizer.model_max_length]     )
  						    for x in ["input_ids", "attention_mask"]
  						}
  						_lowercase					:  Optional[Any]    			=      tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])     )
  
  						if SDP_ENABLED:
  												_lowercase					:  int    			=      tf_train_dataset.shard(sdp.size()   , sdp.rank()     )
  												_lowercase					:  Optional[Any]    			=      tf_test_dataset.shard(sdp.size()   , sdp.rank()     )
  						_lowercase					:  Optional[int]    			=      tf_train_dataset.batch(lowercase__   , drop_remainder=lowercase__     )
  						_lowercase					:  int    			=      tf_test_dataset.batch(lowercase__   , drop_remainder=lowercase__     )
  
  						return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
  			UpperCamelCase_	:    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"""])
  
  			UpperCamelCase_					,     UpperCamelCase_	:    Union[str, Any]  					=       parser.parse_known_args()
  
  			# Set up logging
  			UpperCamelCase_	:    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()
  
  						UpperCamelCase_	:    str  					=       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
  			UpperCamelCase_	:    Tuple  					=       TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  			UpperCamelCase_	:    Tuple  					=       AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  			# get datasets
  			UpperCamelCase_					,     UpperCamelCase_	:    Tuple  					=       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
  			UpperCamelCase_	:    Dict  					=       tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  			UpperCamelCase_	:    Dict  					=       tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  			UpperCamelCase_	:    Optional[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)
  						UpperCamelCase_	:    Any  					=       time.time()
  						UpperCamelCase_	:    List[Any]  					=       fit(
  						    model,
  						    loss,
  						    optimizer,
  						    tf_train_dataset,
  						    args.epochs,
  						    args.per_device_train_batch_size,
  						    max_steps=args.max_steps,
  						)
  						UpperCamelCase_	:    List[Any]  					=       time.time() - start_train_time
  						logger.info("""*** Train ***""")
  						logger.info(f'''train_runtime = {end_train_time}''')
  
  						UpperCamelCase_	:    Optional[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):
  						UpperCamelCase_	:    Any  					=       model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
  						logger.info("""*** Evaluate ***""")
  
  						UpperCamelCase_	:    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: 
  from __future__ import annotations
  
  
  
  def 		UpperCamelCase    (					_UpperCAmelCase     :      list[int]   , _UpperCAmelCase     :      int     )      -> int:
  
  
  
  
  
  
  						'''simple docstring'''
  
  						_lowercase			,						_lowercase					:  Any    			=      0, len(_UpperCAmelCase     ) - 1
  						while start_ind <= end_ind:
  												if array[start_ind] == search_item:
  																		return start_ind
  												elif array[end_ind] == search_item:
  																		return end_ind
  												else:
  																		start_ind += 1
  																		end_ind -= 1
      # returns -1 if search_item is not found in array
  						return -1
  
  
  if __name__ == "__main__":
  							print(double_linear_search(list(range(100)), 40))
  
  
  
  
  