code: 
  import argparse
  import logging
  import sys
  import time
  
  import tensorflow as tf
  from datasets import load_dataset
  
  from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
  
  
  if __name__ == "__main__":
  				snake_case				:	Any							   =						argparse.ArgumentParser()
  
  				# Hyperparameters sent by the client are passed as command-line arguments to the script.
  				parser.add_argument("--epochs", type=int, default=1)
  				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)
  
  				snake_case				:	Optional[int]							   =						parser.parse_known_args()
  
  				# overwrite batch size until we have tf_glue.py
  				snake_case				:	Optional[Any]							   =						16
  				snake_case				:	Union[str, Any]							   =						16
  
  				# Set up logging
  				snake_case				:	str							   =						logging.getLogger(__name__)
  
  				logging.basicConfig(
  				    level=logging.getLevelName("INFO"),
  				    handlers=[logging.StreamHandler(sys.stdout)],
  				    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
  				)
  
  				# Load model and tokenizer
  				snake_case				:	Optional[int]							   =						TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  				snake_case				:	Union[str, Any]							   =						AutoTokenizer.from_pretrained(args.model_name_or_path)
  
  				# Load dataset
  				snake_case				:	int							   =						load_dataset("imdb", split=["train", "test"])
  				snake_case				:	Union[str, Any]							   =						train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  				snake_case				:	int							   =						test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500
  
  				# Preprocess train dataset
  				snake_case				:	str							   =						train_dataset.map(
  				    lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
  				)
  				train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])
  
  				snake_case				:	Any							   =						{
  				    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  				    for x in ["input_ids", "attention_mask"]
  				}
  				snake_case				:	Union[str, Any]							   =						tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
  				    args.per_device_train_batch_size
  				)
  
  				# Preprocess test dataset
  				snake_case				:	List[str]							   =						test_dataset.map(
  				    lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
  				)
  				test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])
  
  				snake_case				:	str							   =						{
  				    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
  				    for x in ["input_ids", "attention_mask"]
  				}
  				snake_case				:	int							   =						tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])).batch(
  				    args.per_device_eval_batch_size
  				)
  
  				# fine optimizer and loss
  				snake_case				:	List[Any]							   =						tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  				snake_case				:	List[str]							   =						tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  				snake_case				:	Union[str, Any]							   =						[tf.keras.metrics.SparseCategoricalAccuracy()]
  				model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
  
  				snake_case				:	Optional[Any]							   =						time.time()
  				snake_case				:	Dict							   =						model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  				snake_case				:	Tuple							   =						time.time() - start_train_time
  
  				logger.info("*** Train ***")
  				logger.info(F"train_runtime = {end_train_time}")
  				for key, value in train_results.history.items():
  								logger.info(F"  {key} = {value}")
  
style_context: 
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  snake_case				:	Optional[int]							   =						{
      "configuration_mobilevitv2": [
          "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
          "MobileViTV2Config",
          "MobileViTV2OnnxConfig",
      ],
  }
  
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				snake_case				:	Optional[int]							   =						[
  				    "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "MobileViTV2ForImageClassification",
  				    "MobileViTV2ForSemanticSegmentation",
  				    "MobileViTV2Model",
  				    "MobileViTV2PreTrainedModel",
  				]
  
  if TYPE_CHECKING:
  				from .configuration_mobilevitva import (
  				    MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
  				    MobileViTVaConfig,
  				    MobileViTVaOnnxConfig,
  				)
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_mobilevitva import (
  								    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    MobileViTVaForImageClassification,
  								    MobileViTVaForSemanticSegmentation,
  								    MobileViTVaModel,
  								    MobileViTVaPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				snake_case				:	Tuple							   =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  