



import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


a_      :      Tuple    =     logging.getLogger(__name__)






def     __a					(		):
 a__												= argparse.ArgumentParser(
     description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''       )
 parser.add_argument(
     '''--dataset_name'''							,  type=__UpperCAmelCase							,  default='''wikitext'''							,  help='''Name of the training. Explore datasets at: hf.co/datasets.'''							,  )
 parser.add_argument(
     '''--dataset_config'''							,  type=__UpperCAmelCase							,  default='''wikitext-103-raw-v1'''							,  help='''Configuration name of the dataset.'''       )
 parser.add_argument(
     '''--tokenizer_name_or_path'''							,  type=__UpperCAmelCase							,  default='''sayakpaul/unigram-tokenizer-wikitext'''							,  help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''							,  )
 parser.add_argument(
     '''--shard_size'''							,  type=__UpperCAmelCase							,  default=1000							,  help='''Number of entries to go in a single shard.'''							,  )
 parser.add_argument('''--split'''							,  type=__UpperCAmelCase							,  default='''train'''							,  choices=['''train''', '''test''', '''validation''']       )
 parser.add_argument(
     '''--limit'''							,  default=__UpperCAmelCase							,  type=__UpperCAmelCase							,  help='''Limit the number of shards (used for debugging).'''							,  )
 parser.add_argument(
     '''--max_length'''							,  type=__UpperCAmelCase							,  default=512							,  help='''Maximum sequence length. For training on TPUs, it helps to have a maximum'''
     ''' sequence length that is a multiple of 8.'''							,  )
 parser.add_argument(
     '''--output_dir'''							,  default='''tf-tpu'''							,  type=__UpperCAmelCase							,  help='''Output directory where the TFRecord shards will be saved. If the'''
     ''' path is appended with `gs://` (\'gs://tf-tpu\', for example) then the TFRecord'''
     ''' shards will be directly saved to a Google Cloud Storage bucket.'''							,  )

 a__												= parser.parse_args()
 return args






def     __a					(		__UpperCAmelCase       ):
 def fn(__UpperCAmelCase       ):
  return tokenizer(examples['''text''']       )

 return fn






def     __a					(		__UpperCAmelCase       ):
 a__												= []
 for i in range(len(tokenized_data['''input_ids''']       )       ):
  a__												= {
      '''input_ids''': tf.train.Feature(intaa_list=tf.train.IntaaList(value=tokenized_data['''input_ids'''][i]       )       ),
      '''attention_mask''': tf.train.Feature(
          intaa_list=tf.train.IntaaList(value=tokenized_data['''attention_mask'''][i]       )       ),
  }
  a__												= tf.train.Features(feature=__UpperCAmelCase       )
  a__												= tf.train.Example(features=__UpperCAmelCase       )
  a__												= example.SerializeToString()
  records.append(__UpperCAmelCase       )
 return records






def     __a					(		__UpperCAmelCase       ):
 a__												= datasets.load_dataset(args.dataset_name							,  args.dataset_config							,  split=args.split       )

 if args.limit is not None:
  a__												= min(len(__UpperCAmelCase       )							,  args.limit       )
  a__												= dataset.select(range(__UpperCAmelCase       )       )
  print(f"Limiting the dataset to {args.limit} entries."       )

 a__												= AutoTokenizer.from_pretrained(args.tokenizer_name_or_path       )

 # Handle output directory creation.
 # For serializing into a Google Cloud Storage Bucket, one needs to first
 # create a bucket.
 if "gs" not in args.output_dir:
  if not os.path.exists(args.output_dir       ):
   os.makedirs(args.output_dir       )
  a__												= os.path.join(args.output_dir							,  args.split       )
  if not os.path.exists(__UpperCAmelCase       ):
   os.makedirs(__UpperCAmelCase       )
 else:
  a__												= os.path.join(args.output_dir							,  args.split       )

 # Tokenize the whole dataset at once.
 a__												= tokenize_function(__UpperCAmelCase       )
 a__												= dataset.map(__UpperCAmelCase							,  batched=__UpperCAmelCase							,  num_proc=4							,  remove_columns=['''text''']       )

 # We need to concatenate all our texts together, and then split the result
 # into chunks of a fixed size, which we will call block_size. To do this, we
 # will use the map method again, with the option batched=True. When we use batched=True,
 # the function we pass to map() will be passed multiple inputs at once, allowing us
 # to group them into more or fewer examples than we had in the input.
 # This allows us to create our new fixed-length samples. The advantage of this
 # method is that we don't lose a whole lot of content from the dataset compared to the
 # case where we simply tokenize with a pre-defined max_length.

 def group_texts(__UpperCAmelCase       ):
  # Concatenate all texts.
  a__												= {k: sum(examples[k]							,  []       ) for k in examples.keys()}
  a__												= len(concatenated_examples[list(examples.keys()       )[0]]       )
  # We drop the small remainder, though you could add padding instead if the model supports it
  # In this, as in all things, we advise you to follow your heart 🫀
  a__												= (total_length // args.max_length) * args.max_length
  # Split by chunks of max_len.
  a__												= {
      k: [t[i : i + args.max_length] for i in range(0							,  __UpperCAmelCase							,  args.max_length       )]
      for k, t in concatenated_examples.items()
  }
  return result

 a__												= dataset_tokenized.map(__UpperCAmelCase							,  batched=__UpperCAmelCase							,  batch_size=1000							,  num_proc=4       )

 a__												= 0
 a__												= 0
 for shard in range(0							,  len(__UpperCAmelCase       )							,  args.shard_size       ):
  a__												= grouped_dataset[shard : shard + args.shard_size]
  a__												= len(dataset_snapshot['''input_ids''']       )
  a__												= os.path.join(__UpperCAmelCase							,  f"dataset-{shard_count}-{records_containing}.tfrecord"       )
  a__												= get_serialized_examples(__UpperCAmelCase       )

  with tf.io.TFRecordWriter(__UpperCAmelCase       ) as out_file:
   for i in range(len(__UpperCAmelCase       )       ):
    a__												= serialized_examples[i]
    out_file.write(__UpperCAmelCase       )
   print('''Wrote file {} containing {} records'''.format(__UpperCAmelCase							,  __UpperCAmelCase       )       )

  shard_count += 1
  total_records += records_containing

 with open(f"split-{args.split}-records-count.txt"							,  '''w'''       ) as f:
  print(f"Total {args.split} records: {total_records}"							,  file=__UpperCAmelCase       )


if __name__ == "__main__":
     a_      :      List[Any]    =     parse_args()
     main(args)










from statistics import mean

import numpy as np






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= 0
 # Number of processes finished
 a__												= 0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 a__												= [0] * no_of_process
 # List to include calculation results
 a__												= [0] * no_of_process

 # Sort by arrival time.
 a__												= [burst_time[i] for i in np.argsort(__UpperCAmelCase       )]
 a__												= [process_name[i] for i in np.argsort(__UpperCAmelCase       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  a__												= 0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   a__												= arrival_time[i]

  a__												= 0
  # Index showing the location of the process being performed
  a__												= 0
  # Saves the current response ratio.
  a__												= 0
  for i in range(0							,  __UpperCAmelCase       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    a__												= (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    a__												= temp
    a__												= i

        # Calculate the turn around time
  a__												= current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  a__												= 1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= [0] * no_of_process
 for i in range(0							,  __UpperCAmelCase       ):
  a__												= turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     a_      :      List[Any]    =     5
     a_      :      Optional[int]    =     ['A', 'B', 'C', 'D', 'E']
     a_      :      Union[str, Any]    =     [1, 2, 3, 4, 5]
     a_      :      Dict    =     [1, 2, 3, 4, 5]

     a_      :      List[Any]    =     calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     a_      :      int    =     calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print('Process name \tArrival time \tBurst time \tTurn around time \tWaiting time')
     for i in range(0, no_of_process):
          print(
              f'{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'
              f'{turn_around_time[i]}\t\t\t{waiting_time[i]}'
          )

     print(f'average waiting time : {mean(waiting_time):.5f}')
     print(f'average turn around time : {mean(turn_around_time):.5f}')





