



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():
     A_		:  Dict										=  True
     A_		:  Union[str, Any]										=  '''p3dn.24xlarge'''
     import smdistributed.dataparallel.tensorflow as sdp
else:
     A_		:  Optional[int]										=  False


def 						UpperCAmelCase__   (   UpperCAmelCase__			:int			,       UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:Union[str, Any]			,       UpperCAmelCase__			:Any			,       UpperCAmelCase__			:List[Any]			,       UpperCAmelCase__			:Optional[int]=None       ):






 '''simple docstring'''

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

  if SDP_ENABLED:
   a							=		sdp.DistributedGradientTape(UpperCAmelCase__			,       sparse_as_dense=UpperCAmelCase__       )

  a							=		tape.gradient(UpperCAmelCase__			,       model.trainable_variables       )
  opt.apply_gradients(zip(UpperCAmelCase__			,       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							=		{"loss": loss_value.numpy()}
 return train_results


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Optional[int]			,       UpperCAmelCase__			:int			,       UpperCAmelCase__			:List[str]       ):






 '''simple docstring'''

 a		,	a							=		load_dataset("imdb"			,       split=["train", "test"]       )

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

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

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

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

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

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
     A_		:  List[str]										=  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'''])

     A_			,	A_		:  Dict										=  parser.parse_known_args()

     # Set up logging
     A_		:  Any										=  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()

          A_		:  int										=  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
     A_		:  Optional[int]										=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     A_		:  List[Any]										=  AutoTokenizer.from_pretrained(args.model_name_or_path)

     # get datasets
     A_			,	A_		:  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
     A_		:  Any										=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
     A_		:  Dict										=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     A_		:  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)
          A_		:  int										=  time.time()
          A_		:  Dict										=  fit(
              model,
              loss,
              optimizer,
              tf_train_dataset,
              args.epochs,
              args.per_device_train_batch_size,
              max_steps=args.max_steps,
          )
          A_		:  Tuple										=  time.time() - start_train_time
          logger.info('''*** Train ***''')
          logger.info(F"""train_runtime = {end_train_time}""")

          A_		:  Dict										=  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):
          A_		:  List[str]										=  model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
          logger.info('''*** Evaluate ***''')

          A_		:  List[str]										=  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)



'''simple docstring'''





def   A			(							A_				:       int							):
						return n * (2 * n - 1)

def   A			(							A_				:       int							):
						snake_case							:  Optional[Any]			=     (1 + 24 * n) ** 0.5
						return ((1 + root) / 6) % 1 == 0

def   A			(							A_				:       int = 144							):
						snake_case							:  Any			=     start
						snake_case							:  Any			=     hexagonal_num(A_							)
						while not is_pentagonal(A_							):
												n += 1
												snake_case							:  Optional[Any]			=     hexagonal_num(A_							)
						return num


if __name__ == "__main__":
						print(f'''{solution()} = ''')

