




'''simple docstring'''




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

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]       ,						SCREAMING_SNAKE_CASE__			:      List[Any]       ,						SCREAMING_SNAKE_CASE__			:      Tuple       ,						SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      Optional[int]       ,						SCREAMING_SNAKE_CASE__			:      Optional[Any]=None       ):
 '''simple docstring'''





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

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

  UpperCAmelCase__										=    tape.gradient(SCREAMING_SNAKE_CASE__       ,						model.trainable_variables       )
  opt.apply_gradients(zip(SCREAMING_SNAKE_CASE__       ,						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    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      int       ,						SCREAMING_SNAKE_CASE__			:      List[str]       ,						SCREAMING_SNAKE_CASE__			:      Dict       ):
 '''simple docstring'''





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

 # Preprocess train dataset
 UpperCAmelCase__										=    train_dataset.map(
     lambda SCREAMING_SNAKE_CASE__       : tokenizer(e["""text"""]       ,						truncation=SCREAMING_SNAKE_CASE__       ,						padding="""max_length"""       )       ,						batched=SCREAMING_SNAKE_CASE__       )
 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 SCREAMING_SNAKE_CASE__       : tokenizer(e["""text"""]       ,						truncation=SCREAMING_SNAKE_CASE__       ,						padding="""max_length"""       )       ,						batched=SCREAMING_SNAKE_CASE__       )
 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(SCREAMING_SNAKE_CASE__       ,						drop_remainder=SCREAMING_SNAKE_CASE__       )
 UpperCAmelCase__										=    tf_test_dataset.batch(SCREAMING_SNAKE_CASE__       ,						drop_remainder=SCREAMING_SNAKE_CASE__       )

 return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
    UpperCAmelCase_       				=      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'])

    UpperCAmelCase_				,		UpperCAmelCase_       				=      parser.parse_known_args()

    # Set up logging
    UpperCAmelCase_       				=      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()

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

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

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

        UpperCAmelCase_       				=      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'''




from __future__ import annotations

import random
import string

class 						lowerCAmelCase_   :







  '''simple docstring'''







  def __init__(						self    :	int						,		_UpperCAmelCase    :	str | None = None ):






   """simple docstring"""






   UpperCAmelCase__										=    passcode or self.__passcode_creator()
   UpperCAmelCase__										=    self.__make_key_list()
   UpperCAmelCase__										=    self.__make_shift_key()


  def __str__(						self    :	int ):






   """simple docstring"""






   return "".join(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	list[int] ):






   """simple docstring"""






   for i in range(1						,		len(_UpperCAmelCase )						,		2 ):
    iterlist[i] *= -1
   return iterlist


  def 							SCREAMING_SNAKE_CASE__			(						self    :	int ):






   """simple docstring"""






   UpperCAmelCase__										=    string.ascii_letters + string.digits
   UpperCAmelCase__										=    [random.choice(_UpperCAmelCase ) for _ in range(random.randint(10						,		20 ) )]
   return password


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    (
       string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
   )

   UpperCAmelCase__										=    []

   # creates points known as breakpoints to break the key_list_options at those
   # points and pivot each substring
   UpperCAmelCase__										=    sorted(set(self.__passcode ) )
   UpperCAmelCase__										=    []

   # algorithm for creating a new shuffled list, keys_l, out of key_list_options
   for i in key_list_options:
    temp_list.extend(_UpperCAmelCase )

    # checking breakpoints at which to pivot temporary sublist and add it into
    # keys_l
    if i in breakpoints or i == key_list_options[-1]:
     keys_l.extend(temp_list[::-1] )
     temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
   return keys_l


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Tuple ):






   """simple docstring"""






   UpperCAmelCase__										=    sum(self.__neg_pos([ord(_UpperCAmelCase ) for x in self.__passcode] ) )
   return num if num > 0 else len(self.__passcode )


  def 							SCREAMING_SNAKE_CASE__			(						self    :	Optional[Any]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # decoding shift like Caesar cipher algorithm implementing negative shift or
   # reverse shift or left shift
   for i in encoded_message:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    decoded_message += self.__key_list[
        (position - self.__shift_key) % -len(self.__key_list )
    ]

   return decoded_message



  def 							SCREAMING_SNAKE_CASE__			(						self    :	List[str]						,		_UpperCAmelCase    :	str ):






   """simple docstring"""






   UpperCAmelCase__										=    """"""

   # encoding shift like Caesar cipher algorithm implementing positive shift or
   # forward shift or right shift
   for i in plaintext:
    UpperCAmelCase__										=    self.__key_list.index(_UpperCAmelCase )
    encoded_message += self.__key_list[
        (position + self.__shift_key) % len(self.__key_list )
    ]

   return encoded_message

def    _UpperCamelCase      (				SCREAMING_SNAKE_CASE__			:      str = "Hello, this is a modified Caesar cipher"       ):
 '''simple docstring'''





 UpperCAmelCase__										=    ShuffledShiftCipher()
 return cipa.decrypt(cipa.encrypt(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
