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():
     _SCREAMING_SNAKE_CASE     =     True
     _SCREAMING_SNAKE_CASE     =     """p3dn.24xlarge"""
     import smdistributed.dataparallel.tensorflow as sdp
else:
     _SCREAMING_SNAKE_CASE     =     False
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__=None       )					->List[str]:

  '''simple docstring'''




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

    if SDP_ENABLED:
      a__  		=  sdp.DistributedGradientTape(lowerCamelCase__			,					sparse_as_dense=lowerCamelCase__       )

    a__  		=  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

  a__  		=  {"loss": loss_value.numpy()}
  return train_results
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->List[Any]:

  '''simple docstring'''




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

  # Preprocess train dataset
  a__  		=  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"]       )

  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 lowerCamelCase__       : tokenizer(e["text"]			,					truncation=lowerCamelCase__			,					padding="max_length"       )			,					batched=lowerCamelCase__       )
  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(lowerCamelCase__			,					drop_remainder=lowerCamelCase__       )
  a__  		=  tf_test_dataset.batch(lowerCamelCase__			,					drop_remainder=lowerCamelCase__       )

  return tf_train_dataset, tf_test_dataset


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

     _SCREAMING_SNAKE_CASE    ,      _SCREAMING_SNAKE_CASE     =     parser.parse_known_args()

     # Set up logging
     _SCREAMING_SNAKE_CASE     =     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()

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

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

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

          _SCREAMING_SNAKE_CASE     =     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

from statistics import mean
def 					__lowercase						(    lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  a__  		=  2
  a__  		=  list(lowerCamelCase__       )
  a__  		=  [0] * len(lowerCamelCase__       )
  a__  		=  0
  while True:
    a__  		=  True
    for i, burst_time in enumerate(lowerCamelCase__       ):
      if rem_burst_times[i] > 0:
        a__  		=  False
        if rem_burst_times[i] > quantum:
          t += quantum
          rem_burst_times[i] -= quantum
        else:
          t += rem_burst_times[i]
          a__  		=  t - burst_time
          a__  		=  0
    if done is True:
      return waiting_times
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__       )					->list[int]:

  '''simple docstring'''




  return [burst + waiting for burst, waiting in zip(lowerCamelCase__			,					lowerCamelCase__       )]


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     [3, 5, 7]
     _SCREAMING_SNAKE_CASE     =     calculate_waiting_times(burst_times)
     _SCREAMING_SNAKE_CASE     =     calculate_turn_around_times(burst_times, waiting_times)
     print("""Process ID \tBurst Time \tWaiting Time \tTurnaround Time""")
     for i, burst_time in enumerate(burst_times):
          print(
              F"""  {i + 1}\t\t  {burst_time}\t\t  {waiting_times[i]}\t\t  """
              F"""{turn_around_times[i]}"""
          )
     print(F"""\nAverage waiting time = {mean(waiting_times):.5f}""")
     print(F"""Average turn around time = {mean(turn_around_times):.5f}""")

