





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  :				Any						=				True
  _lowerCAmelCase  :				Optional[Any]						=				'p3dn.24xlarge'
  import smdistributed.dataparallel.tensorflow as sdp
else:
  _lowerCAmelCase  :				List[Any]						=				False



def   lowerCAmelCase						(						_lowerCAmelCase							:				List[Any]  ,							_lowerCAmelCase							:				Optional[Any]  ,							_lowerCAmelCase							:				List[str]  ,							_lowerCAmelCase							:				Optional[int]  ,							_lowerCAmelCase							:				Optional[int]  ,							_lowerCAmelCase							:				Any  ,							_lowerCAmelCase							:				Optional[Any]=None		):




 """simple docstring"""







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

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

  UpperCAmelCase__								=  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

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



def   lowerCAmelCase						(						_lowerCAmelCase							:				Any  ,							_lowerCAmelCase							:				Dict  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







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

 # Preprocess train dataset
 UpperCAmelCase__								=  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"]		)

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

 # Preprocess test dataset
 UpperCAmelCase__								=  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"]		)

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

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

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
  _lowerCAmelCase  :				Union[str, 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=1_6)
  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  :				Tuple						=				parser.parse_known_args()

  # Set up logging
  _lowerCAmelCase  :				Dict						=				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  :				List[Any]						=				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  :				Any						=				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  _lowerCAmelCase  :				List[Any]						=				AutoTokenizer.from_pretrained(args.model_name_or_path)

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

    _lowerCAmelCase  :				str						=				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  :				Optional[Any]						=				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
    logger.info("*** Evaluate ***")

    _lowerCAmelCase  :				Optional[int]						=				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)











from __future__ import annotations



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 assert (
     c % greatest_common_divisor(_lowerCAmelCase  ,							_lowerCAmelCase		) == 0
 )  # greatest_common_divisor(a,b) function implemented below
 ((UpperCAmelCase__)	,	(UpperCAmelCase__)	,	(UpperCAmelCase__))								=  extended_gcd(_lowerCAmelCase  ,							_lowerCAmelCase		)  # extended_gcd(a,b) function implemented below
 UpperCAmelCase__								=  c / d
 return (r * x, r * y)



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int = 2		):




 """simple docstring"""







 ((UpperCAmelCase__)	,	(UpperCAmelCase__))								=  diophantine(_lowerCAmelCase  ,							_lowerCAmelCase  ,							_lowerCAmelCase		)  # Initial value
 UpperCAmelCase__								=  greatest_common_divisor(_lowerCAmelCase  ,							_lowerCAmelCase		)
 UpperCAmelCase__								=  a // d
 UpperCAmelCase__								=  b // d

 for i in range(_lowerCAmelCase		):
  UpperCAmelCase__								=  xa + i * q
  UpperCAmelCase__								=  ya - i * p
  print(_lowerCAmelCase  ,							_lowerCAmelCase		)



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 if a < b:
  UpperCAmelCase__	,	UpperCAmelCase__								=  b, a

 while a % b != 0:
  UpperCAmelCase__	,	UpperCAmelCase__								=  b, a % b

 return b



def   lowerCAmelCase						(						_lowerCAmelCase							:				int  ,							_lowerCAmelCase							:				int		):




 """simple docstring"""







 assert a >= 0 and b >= 0

 if b == 0:
  UpperCAmelCase__	,	UpperCAmelCase__	,	UpperCAmelCase__								=  a, 1, 0
 else:
  ((UpperCAmelCase__)	,	(UpperCAmelCase__)	,	(UpperCAmelCase__))								=  extended_gcd(_lowerCAmelCase  ,							a % b		)
  UpperCAmelCase__								=  q
  UpperCAmelCase__								=  p - q * (a // b)

 assert a % d == 0 and b % d == 0
 assert d == a * x + b * y

 return (d, x, y)


if __name__ == "__main__":
  from doctest import testmod

  testmod(name="diophantine", verbose=True)
  testmod(name="diophantine_all_soln", verbose=True)
  testmod(name="extended_gcd", verbose=True)
  testmod(name="greatest_common_divisor", verbose=True)





