





import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


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

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

   __lowerCAmelCase							     =     parser.parse_args()
   return args

def   a_   (			lowerCAmelCase_				:      Any     ):
   def fn(lowerCAmelCase_				:      List[str]     ):
      return tokenizer(examples['text']     )

   return fn

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

def   a_   (			lowerCAmelCase_				:      List[str]     ):
   __lowerCAmelCase							     =     datasets.load_dataset(args.dataset_name,     args.dataset_config,     split=args.split     )

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

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

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

   __lowerCAmelCase							     =     dataset_tokenized.map(lowerCAmelCase_,     batched=lowerCAmelCase_,     batch_size=1000,     num_proc=4     )

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

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

      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=lowerCAmelCase_     )


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








from functools import lru_cache

@lru_cache
def   a_   (			lowerCAmelCase_				:      int     ):
   if num < 0:
      raise ValueError('Number should not be negative.'     )

   return 1 if num in (0, 1) else num * factorial(num - 1     )


if __name__ == "__main__":
     import doctest

     doctest.testmod()

