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[str]		   =      True
  			UpperCAmelCase_       :				str		   =      '''p3dn.24xlarge'''
  			import smdistributed.dataparallel.tensorflow as sdp
  else:
  			UpperCAmelCase_       :				Tuple		   =      False
  def        __SCREAMING_SNAKE_CASE    (      a__						:      List[str] ,a__						:      Union[str, Any] ,a__						:      List[str] ,a__						:      List[Any] ,a__						:      Any ,a__						:      Optional[Any] ,a__						:      Optional[int]=None					)				->							Union[str, Any]:
  	__A   : Dict								= tqdm(a__					)
  	for i, batch in enumerate(a__					):
  		with tf.GradientTape() as tape:
  			__A						,							__A   : Optional[int]								= batch
  			__A   : List[str]								= model(a__					)
  			__A   : int								= loss(a__ ,outputs.logits					)
  
  		if SDP_ENABLED:
  			__A   : Union[str, Any]								= sdp.DistributedGradientTape(a__ ,sparse_as_dense=a__					)
  
  		__A   : Dict								= 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
  
  	__A   : Union[str, Any]								= {"""loss""": loss_value.numpy()}
  	return train_results
  def        __SCREAMING_SNAKE_CASE    (      a__						:      Optional[Any] ,a__						:      List[str] ,a__						:      str					)				->							int:
  	# Load dataset
  	__A						,							__A   : List[Any]								= load_dataset("""imdb""" ,split=["""train""", """test"""]					)
  
  	# Preprocess train dataset
  	__A   : Optional[Any]								= 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"""]					)
  
  	__A   : List[str]								= {
  	    x: train_dataset[x].to_tensor(default_value=0 ,shape=[None, tokenizer.model_max_length]					)
  	    for x in ["""input_ids""", """attention_mask"""]
  	}
  	__A   : Optional[Any]								= tf.data.Dataset.from_tensor_slices((train_features, train_dataset["""label"""])					)
  
  	# Preprocess test dataset
  	__A   : List[str]								= 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"""]					)
  
  	__A   : Dict								= {
  	    x: test_dataset[x].to_tensor(default_value=0 ,shape=[None, tokenizer.model_max_length]					)
  	    for x in ["""input_ids""", """attention_mask"""]
  	}
  	__A   : Optional[int]								= tf.data.Dataset.from_tensor_slices((test_features, test_dataset["""label"""])					)
  
  	if SDP_ENABLED:
  		__A   : Optional[Any]								= tf_train_dataset.shard(sdp.size() ,sdp.rank()					)
  		__A   : Dict								= tf_test_dataset.shard(sdp.size() ,sdp.rank()					)
  	__A   : str								= tf_train_dataset.batch(a__ ,drop_remainder=a__					)
  	__A   : int								= tf_test_dataset.batch(a__ ,drop_remainder=a__					)
  
  	return tf_train_dataset, tf_test_dataset
  
  
  if __name__ == "__main__":
  			UpperCAmelCase_       :				Optional[Any]		   =      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_       :				List[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_       :				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
  			UpperCAmelCase_       :				Optional[Any]		   =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  			UpperCAmelCase_       :				Union[str, Any]		   =      AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  			# get datasets
  			UpperCAmelCase_   ,		UpperCAmelCase_       :				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
  			UpperCAmelCase_       :				Union[str, Any]		   =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  			UpperCAmelCase_       :				Any		   =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  			UpperCAmelCase_       :				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_       :				Dict		   =      fit(
  						    model,
  						    loss,
  						    optimizer,
  						    tf_train_dataset,
  						    args.epochs,
  						    args.per_device_train_batch_size,
  						    max_steps=args.max_steps,
  						)
  						UpperCAmelCase_       :				Optional[int]		   =      time.time() - start_train_time
  						logger.info('''*** Train ***''')
  						logger.info(f"""train_runtime = {end_train_time}""")
  
  						UpperCAmelCase_       :				Union[str, 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_       :				int		   =      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: 
  
  import os
  import tempfile
  import unittest
  from typing import List
  
  from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
  from transformers.testing_utils import require_tokenizers
  from transformers.tokenization_utils import PreTrainedTokenizer
  from transformers.tokenization_utils_base import PreTrainedTokenizerBase
  
  
  
  
  
  
  @require_tokenizers
  class     lowerCamelCase_			(  unittest.TestCase  ):
  	_lowercase		:      List[str]    				=							EsmTokenizer
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		super().setUp()
  		__A   : Dict								= tempfile.mkdtemp()
  		# fmt: off
  		__A   : List[str]								= ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
  		# fmt: on
  		__A   : int								= os.path.join(self.tmpdirname				,			VOCAB_FILES_NAMES["""vocab_file"""]       )
  		with open(self.vocab_file				,			"""w"""				,			encoding="""utf-8"""       ) as vocab_writer:
  			vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]       )       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict				,			**__A	:    int       ):
  		return [self.get_tokenizer(**__A       )]
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Optional[int]				,			**__A	:    List[Any]       ):
  		return self.tokenizer_class.from_pretrained(self.tmpdirname				,			**__A       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : int								= tokenizer.tokenize("""LAGVS"""       )
  		self.assertListEqual(__A				,			["""L""", """A""", """G""", """V""", """S"""]       )
  		self.assertListEqual(tokenizer.convert_tokens_to_ids(__A       )				,			[4, 5, 6, 7, 8]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    str       ):
  		__A   : Union[str, Any]								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : int								= """LAGVS"""
  		self.assertListEqual(tokenizer.encode(__A       )				,			[0, 4, 5, 6, 7, 8, 2]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    Dict       ):
  		__A   : str								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : Tuple								= ["""LAGVS""", """WCB"""]
  		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]
  
  		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[Any]       ):
  		__A   : Dict								= self.tokenizer_class(self.vocab_file       )
  
  		__A   : Any								= ["""LAGVS""", """WCB"""]
  		__A   : str								= tokenizer(__A				,			padding=__A       )["""input_ids"""]
  
  		self.assertListEqual(__A				,			[[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]       )
  
  
  
  
  	def 					lowerCAmelCase_					(    self	:    List[str]       ):
  		__A   : Optional[int]								= self.get_tokenizers(fast=__A       )
  		for tokenizer in tokenizers:
  			with self.subTest(F"""{tokenizer.__class__.__name__}"""       ):
  				__A   : Any								= """<unk>"""
  				__A   : str								= """<mask>"""
  
  				__A   : Optional[Any]								= tokenizer.tokenize(__A       )
  				__A   : str								= tokenizer.tokenize(__A       )
  
  				self.assertEqual(len(__A       )				,			1       )
  				self.assertEqual(len(__A       )				,			1       )
  				self.assertEqual(token_a[0]				,			__A       )
  				self.assertEqual(token_a[0]				,			__A       )
  
  
  
  
  
  