


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


snake_case							:	Any					      =				logging.getLogger(__name__)






def 							__lowercase       (			):
							a__					=					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.' ,   )

							a__					=					parser.parse_args()
							return args






def 							__lowercase       (			__lowerCAmelCase							:    Any       ):
							def fn(__lowerCAmelCase							:    Any       ):
														return tokenizer(examples['text']       )

							return fn






def 							__lowercase       (			__lowerCAmelCase							:    Union[str, Any]       ):
							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=__lowerCAmelCase       )
														a__					=					tf.train.Example(features=__lowerCAmelCase       )
														a__					=					example.SerializeToString()
														records.append(__lowerCAmelCase       )
							return records






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

							if args.limit is not None:
														a__					=					min(len(__lowerCAmelCase       ) ,   args.limit       )
														a__					=					dataset.select(range(__lowerCAmelCase       )       )
														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(__lowerCAmelCase       ):
																					os.makedirs(__lowerCAmelCase       )
							else:
														a__					=					os.path.join(args.output_dir ,   args.split       )

							# Tokenize the whole dataset at once.
							a__					=					tokenize_function(__lowerCAmelCase       )
							a__					=					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							:    int       ):
														# 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 ,   __lowerCAmelCase ,   args.max_length       )]
														    for k, t in concatenated_examples.items()
														}
														return result

							a__					=					dataset_tokenized.map(__lowerCAmelCase ,   batched=__lowerCAmelCase ,   batch_size=1_0_0_0 ,   num_proc=4       )

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

														with tf.io.TFRecordWriter(__lowerCAmelCase       ) as out_file:
																					for i in range(len(__lowerCAmelCase       )       ):
																												a__					=					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							:	str					      =				parse_args()
						main(args)








from __future__ import annotations






def 							__lowercase       (			__lowerCAmelCase							:    List[Any] ,   __lowerCAmelCase							:    Optional[Any] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    List[str]       ):  # noqa: E741
							while r - l > 1:
														a__					=					(l + r) // 2
														if v[m] >= key:
																					a__					=					m
														else:
																					a__					=					m  # noqa: E741
							return r






def 							__lowercase       (			__lowerCAmelCase							:    list[int]       ):
							if len(__lowerCAmelCase       ) == 0:
														return 0

							a__					=					[0] * len(__lowerCAmelCase       )
							a__					=					1

							a__					=					v[0]

							for i in range(1 ,   len(__lowerCAmelCase       )       ):
														if v[i] < tail[0]:
																					a__					=					v[i]
														elif v[i] > tail[length - 1]:
																					a__					=					v[i]
																					length += 1
														else:
																					a__					=					v[i]

							return length


if __name__ == "__main__":
						import doctest

						doctest.testmod()




