
def      lowerCamelCase_					(							lowerCamelCase__       ):
				lowerCamelCase_         =     0

				# if input_string is "aba" than new_input_string become "a|b|a"
				lowerCamelCase_         =     ""
				lowerCamelCase_         =     ""

				# append each character + "|" in new_string for range(0, length-1)
				for i in input_string[: len(lowerCamelCase__       ) - 1]:
								new_input_string += i + "|"
				# append last character
				new_input_string += input_string[-1]

				# we will store the starting and ending of previous furthest ending palindromic
				# substring
				lowerCamelCase_      ,       lowerCamelCase_         =     0, 0

				# length[i] shows the length of palindromic substring with center i
				lowerCamelCase_         =     [1 for i in range(len(lowerCamelCase__       )       )]

				# for each character in new_string find corresponding palindromic string
				lowerCamelCase_         =     0
				for j in range(len(lowerCamelCase__       )       ):
								lowerCamelCase_         =     1 if j > r else min(length[l + r - j] // 2      ,					r - j + 1       )
								while (
								    j - k >= 0
								    and j + k < len(lowerCamelCase__       )
								    and new_input_string[k + j] == new_input_string[j - k]
								):
												k += 1

								lowerCamelCase_         =     2 * k - 1

								# does this string is ending after the previously explored end (that is r) ?
								# if yes the update the new r to the last index of this
								if j + k - 1 > r:
												lowerCamelCase_         =     j - k + 1  # noqa: E741
												lowerCamelCase_         =     j + k - 1

								# update max_length and start position
								if max_length < length[j]:
												lowerCamelCase_         =     length[j]
												lowerCamelCase_         =     j

    # create that string
				lowerCamelCase_         =     new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
				for i in s:
								if i != "|":
												output_string += i

				return output_string


if __name__ == "__main__":
			import doctest

			doctest.testmod()




import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


__A							=logging.getLogger(__name__)





def      lowerCamelCase_					(							):
				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=1_0_0_0      ,					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=5_1_2      ,					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      lowerCamelCase_					(							lowerCamelCase__       ):
				def fn(lowerCamelCase__       ):
								return tokenizer(examples["text"]       )

				return fn





def      lowerCamelCase_					(							lowerCamelCase__       ):
				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      lowerCamelCase_					(							lowerCamelCase__       ):
				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__       ):
								# 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=1_0_0_0      ,					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__":
			__A							=parse_args()
			main(args)


