

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







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_=None     ):





     """simple docstring"""







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

          if SDP_ENABLED:
               SCREAMING_SNAKE_CASE 		=sdp.DistributedGradientTape(lowerCAmelCase_,       sparse_as_dense=lowerCAmelCase_     )

          SCREAMING_SNAKE_CASE 		=tape.gradient(lowerCAmelCase_,       model.trainable_variables     )
          opt.apply_gradients(zip(lowerCAmelCase_,       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

     SCREAMING_SNAKE_CASE 		={'loss': loss_value.numpy()}
     return train_results







def      snake_case__							(  lowerCAmelCase_,       lowerCAmelCase_,       lowerCAmelCase_     ):





     """simple docstring"""







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

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

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

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

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

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

     return tf_train_dataset, tf_test_dataset


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

 _lowerCamelCase			,					_lowerCamelCase							=parser.parse_known_args()

 # Set up logging
 _lowerCamelCase							=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()

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

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

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

  _lowerCamelCase							=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)




import os







def      snake_case__							(  ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE 		=os.path.dirname(os.path.realpath(lowerCAmelCase_     )     )
     SCREAMING_SNAKE_CASE 		=os.path.join(lowerCAmelCase_,       'triangle.txt'     )

     with open(lowerCAmelCase_     ) as f:
          SCREAMING_SNAKE_CASE 		=f.readlines()

     SCREAMING_SNAKE_CASE 		=[[int(lowerCAmelCase_     ) for y in x.rstrip('\r\n'     ).split(' '     )] for x in triangle]

     for i in range(1,       len(lowerCAmelCase_     )     ):
          for j in range(len(a[i]     )     ):
               SCREAMING_SNAKE_CASE 		=a[i - 1][j] if j != len(a[i - 1]     ) else 0
               SCREAMING_SNAKE_CASE 		=a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(lowerCAmelCase_,       lowerCAmelCase_     )
     return max(a[-1]     )


if __name__ == "__main__":
 print(solution())

