





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():
      __lowercase				    =      True
      __lowercase				    =      '''p3dn.24xlarge'''
      import smdistributed.dataparallel.tensorflow as sdp
else:
      __lowercase				    =      False


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		List[str]  ,				__lowercase:		Optional[Any]  ,				__lowercase:		str  ,				__lowercase:		Dict  ,				__lowercase:		List[str]  ,				__lowercase:		Union[str, Any]  ,				__lowercase:		int=None):



  '''simple docstring'''






  __lowerCAmelCase								=					tqdm(__lowercase)
  for i, batch in enumerate(__lowercase):
    with tf.GradientTape() as tape:
      __lowerCAmelCase								=					batch
      __lowerCAmelCase								=					model(__lowercase)
      __lowerCAmelCase								=					loss(__lowercase  ,				outputs.logits)

    if SDP_ENABLED:
      __lowerCAmelCase								=					sdp.DistributedGradientTape(__lowercase  ,				sparse_as_dense=__lowercase)

    __lowerCAmelCase								=					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

  __lowerCAmelCase								=					{"""loss""": loss_value.numpy()}
  return train_results


def        __SCREAMING_SNAKE_CASE	(				__lowercase:		Tuple  ,				__lowercase:		Optional[int]  ,				__lowercase:		Optional[int]):



  '''simple docstring'''






  __lowerCAmelCase								=					load_dataset('''imdb'''  ,				split=['''train''', '''test'''])

  # Preprocess train dataset
  __lowerCAmelCase								=					train_dataset.map(
      lambda __lowercase: tokenizer(e['''text''']  ,				truncation=__lowercase  ,				padding='''max_length''')  ,				batched=__lowercase)
  train_dataset.set_format(type='''tensorflow'''  ,				columns=['''input_ids''', '''attention_mask''', '''label'''])

  __lowerCAmelCase								=					{
      x: train_dataset[x].to_tensor(default_value=0  ,				shape=[None, tokenizer.model_max_length])
      for x in ["""input_ids""", """attention_mask"""]
  }
  __lowerCAmelCase								=					tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label''']))

  # Preprocess test dataset
  __lowerCAmelCase								=					test_dataset.map(
      lambda __lowercase: tokenizer(e['''text''']  ,				truncation=__lowercase  ,				padding='''max_length''')  ,				batched=__lowercase)
  test_dataset.set_format(type='''tensorflow'''  ,				columns=['''input_ids''', '''attention_mask''', '''label'''])

  __lowerCAmelCase								=					{
      x: test_dataset[x].to_tensor(default_value=0  ,				shape=[None, tokenizer.model_max_length])
      for x in ["""input_ids""", """attention_mask"""]
  }
  __lowerCAmelCase								=					tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label''']))

  if SDP_ENABLED:
    __lowerCAmelCase								=					tf_train_dataset.shard(sdp.size()  ,				sdp.rank())
    __lowerCAmelCase								=					tf_test_dataset.shard(sdp.size()  ,				sdp.rank())
  __lowerCAmelCase								=					tf_train_dataset.batch(__lowercase  ,				drop_remainder=__lowercase)
  __lowerCAmelCase								=					tf_test_dataset.batch(__lowercase  ,				drop_remainder=__lowercase)

  return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
      __lowercase				    =      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"""])

      __lowercase				    =      parser.parse_known_args()

      # Set up logging
      __lowercase				    =      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()

            __lowercase				    =      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
      __lowercase				    =      TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
      __lowercase				    =      AutoTokenizer.from_pretrained(args.model_name_or_path)

      # get datasets
      __lowercase				    =      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
      __lowercase				    =      tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
      __lowercase				    =      tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
      __lowercase				    =      [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)
            __lowercase				    =      time.time()
            __lowercase				    =      fit(
                model,
                loss,
                optimizer,
                tf_train_dataset,
                args.epochs,
                args.per_device_train_batch_size,
                max_steps=args.max_steps,
            )
            __lowercase				    =      time.time() - start_train_time
            logger.info("""*** Train ***""")
            logger.info(f"""train_runtime = {end_train_time}""")

            __lowercase				    =      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):
            __lowercase				    =      model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
            logger.info("""*** Evaluate ***""")

            __lowercase				    =      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)




_A		:					Optional[int]          =							{
    '''+''': lambda x, y: x + y,
    '''-''': lambda x, y: x - y,
    '''*''': lambda x, y: x * y,
    '''/''': lambda x, y: x / y,
}



def      __lowerCAmelCase    (				snake_case						:     Union[str, Any]			)							->				str:
   return c.isdigit()



def      __lowerCAmelCase    (				snake_case						:     Optional[Any]			)							->				str:
   __lowerCamelCase:					List[str]	 =						[]

   # iterate over the string in reverse order
   for c in expression.split()[::-1]:
      # push operand to stack
      if is_operand(snake_case			):
         stack.append(int(snake_case			)			)

      else:
         # pop values from stack can calculate the result
         # push the result onto the stack again
         __lowerCamelCase:					Any	 =						stack.pop()
         __lowerCamelCase:					int	 =						stack.pop()
         stack.append(calc[c](snake_case    ,   snake_case			)			)

   return stack.pop()


# Driver code
if __name__ == "__main__":
   _A		:					int          =							'''+ 9 * 2 6'''
   print(evaluate(test_expression))

   _A		:					str          =							'''/ * 10 2 + 4 1 '''
   print(evaluate(test_expression))


