




'''simple docstring'''







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


A									=logging.getLogger(__name__)





def 		snake_case_							():
    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=1_0_0_0					,     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=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=_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 		snake_case_							(_a			:							Dict  ):
    def fn(_a			:							Tuple  ):
        return tokenizer(examples['''text''']  )

    return fn





def 		snake_case_							(_a			:							str  ):
    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 		snake_case_							(_a			:							List[Any]  ):
    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(_a			:							Dict  ):
        # 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=1_0_0_0					,     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__":
   A									=parse_args()
   main(args)










'''simple docstring'''







from __future__ import annotations





def 		snake_case_							(_a			:							str					,     _a			:							list[str] | None = None  ):
    UpperCAmelCase     =	word_bank or []
    # create a table
    UpperCAmelCase     =	len(_a  ) + 1

    UpperCAmelCase     =	[]
    for _ in range(_a  ):
        table.append([]  )
    # seed value
    UpperCAmelCase     =	[[]]  # because empty string has empty combination

    # iterate through the indices
    for i in range(_a  ):
        # condition
        if table[i] != []:
            for word in word_bank:
                # slice condition
                if target[i : i + len(_a  )] == word:
                    UpperCAmelCase     =	[
                        [word, *way] for way in table[i]
                    ]
                    # adds the word to every combination the current position holds
                    # now,push that combination to the table[i+len(word)]
                    table[i + len(_a  )] += new_combinations

    # combinations are in reverse order so reverse for better output
    for combination in table[len(_a  )]:
        combination.reverse()

    return table[len(_a  )]


if __name__ == "__main__":
   print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
   print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
   print(
       all_construct(
           'hexagonosaurus',
           ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
       )
   )




