





'''simple docstring'''

import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowercase__						:      List[str]		=    logging.getLogger(__name__)







def  a__    (    )		->		int:





     """simple docstring"""

     _UpperCamelCase       =	argparse.ArgumentParser(
         description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''				)
     parser.add_argument(
         '''--dataset_name''', type=_A, default='''wikitext''', help='''Name of the training. Explore datasets at: hf.co/datasets.''', )
     parser.add_argument(
         '''--dataset_config''', type=_A, default='''wikitext-103-raw-v1''', help='''Configuration name of the dataset.'''				)
     parser.add_argument(
         '''--tokenizer_name_or_path''', type=_A, default='''sayakpaul/unigram-tokenizer-wikitext''', help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.''', )
     parser.add_argument(
         '''--shard_size''', type=_A, default=1000, help='''Number of entries to go in a single shard.''', )
     parser.add_argument('''--split''', type=_A, default='''train''', choices=['''train''', '''test''', '''validation''']				)
     parser.add_argument(
         '''--limit''', default=_A, type=_A, help='''Limit the number of shards (used for debugging).''', )
     parser.add_argument(
         '''--max_length''', type=_A, 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=_A, 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.''', )

     _UpperCamelCase       =	parser.parse_args()
     return args







def  a__    (    lowercase :					Dict				)		->		Optional[int]:





     """simple docstring"""

     def fn(lowercase :					List[Any]				):
          return tokenizer(examples['''text''']				)

     return fn







def  a__    (    lowercase :					Dict				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	[]
     for i in range(len(tokenized_data['''input_ids''']				)				):
          _UpperCamelCase       =	{
              '''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]				)				),
          }
          _UpperCamelCase       =	tf.train.Features(feature=_A				)
          _UpperCamelCase       =	tf.train.Example(features=_A				)
          _UpperCamelCase       =	example.SerializeToString()
          records.append(_A				)
     return records







def  a__    (    lowercase :					Any				)		->		Dict:





     """simple docstring"""

     _UpperCamelCase       =	datasets.load_dataset(args.dataset_name, args.dataset_config, split=args.split				)

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

     _UpperCamelCase       =	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				)
          _UpperCamelCase       =	os.path.join(args.output_dir, args.split				)
          if not os.path.exists(_A				):
               os.makedirs(_A				)
     else:
          _UpperCamelCase       =	os.path.join(args.output_dir, args.split				)

     # Tokenize the whole dataset at once.
     _UpperCamelCase       =	tokenize_function(_A				)
     _UpperCamelCase       =	dataset.map(_A, batched=_A, 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(lowercase :					Any				):
          # Concatenate all texts.
          _UpperCamelCase       =	{k: sum(examples[k], []				) for k in examples.keys()}
          _UpperCamelCase       =	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 🫀
          _UpperCamelCase       =	(total_length // args.max_length) * args.max_length
          # Split by chunks of max_len.
          _UpperCamelCase       =	{
              k: [t[i : i + args.max_length] for i in range(0, _A, args.max_length				)]
              for k, t in concatenated_examples.items()
          }
          return result

     _UpperCamelCase       =	dataset_tokenized.map(_A, batched=_A, batch_size=1000, num_proc=4				)

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

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

          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=_A				)


if __name__ == "__main__":
   lowercase__						:      Dict		=    parse_args()
   main(args)










'''simple docstring'''

import random
class     __lowerCAmelCase					:






       """simple docstring"""





       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		str					)   ->					tuple[list[int], list[int]]:

            '''simple docstring'''




            _UpperCamelCase       =	[ord(lowerCAmelCase__					) for i in text]
            _UpperCamelCase       =	[]
            _UpperCamelCase       =	[]
            for i in plain:
                 _UpperCamelCase       =	random.randint(1	,			300					)
                 _UpperCamelCase       =	(i + k) * k
                 cipher.append(lowerCAmelCase__					)
                 key.append(lowerCAmelCase__					)
            return cipher, key







       @staticmethod
       def snake_case__						(							lowerCAmelCase__       :		list[int]	,			lowerCAmelCase__       :		list[int]					)   ->					str:

            '''simple docstring'''




            _UpperCamelCase       =	[]
            for i in range(len(lowerCAmelCase__					)					):
                 _UpperCamelCase       =	int((cipher[i] - (key[i]) ** 2) / key[i]					)
                 plain.append(chr(lowerCAmelCase__					)					)
            return "".join(lowerCAmelCase__					)


if __name__ == "__main__":
   lowercase__	,       lowercase__						:      List[str]	=    Onepad().encrypt('Hello')
   print(c, k)
   print(Onepad().decrypt(c, k))



