


"""simple docstring"""







import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCamelCase_   :   List[Any]									=  logging.getLogger(__name__)
def _A						(						):







    """simple docstring"""
    a 		=argparse.ArgumentParser(
        description='''Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset.'''		)
    parser.add_argument(
        '''--dataset_name'''       ,    type=lowercase       ,    default='''wikitext'''       ,    help='''Name of the training. Explore datasets at: hf.co/datasets.'''       ,    )
    parser.add_argument(
        '''--dataset_config'''       ,    type=lowercase       ,    default='''wikitext-103-raw-v1'''       ,    help='''Configuration name of the dataset.'''		)
    parser.add_argument(
        '''--tokenizer_name_or_path'''       ,    type=lowercase       ,    default='''sayakpaul/unigram-tokenizer-wikitext'''       ,    help='''Tokenizer identifier. Can be a local filepath or a Hub identifier.'''       ,    )
    parser.add_argument(
        '''--shard_size'''       ,    type=lowercase       ,    default=10_00       ,    help='''Number of entries to go in a single shard.'''       ,    )
    parser.add_argument('''--split'''       ,    type=lowercase       ,    default='''train'''       ,    choices=['''train''', '''test''', '''validation''']		)
    parser.add_argument(
        '''--limit'''       ,    default=lowercase       ,    type=lowercase       ,    help='''Limit the number of shards (used for debugging).'''       ,    )
    parser.add_argument(
        '''--max_length'''       ,    type=lowercase       ,    default=5_12       ,    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=lowercase       ,    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 _A						(						lowercase		):







    """simple docstring"""
    def fn(lowercase		):
        return tokenizer(examples['''text''']		)

    return fn
def _A						(						lowercase		):







    """simple docstring"""
    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=lowercase		)
        a 		=tf.train.Example(features=lowercase		)
        a 		=example.SerializeToString()
        records.append(lowercase		)
    return records
def _A						(						lowercase		):







    """simple docstring"""
    a 		=datasets.load_dataset(args.dataset_name       ,    args.dataset_config       ,    split=args.split		)

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

    # Tokenize the whole dataset at once.
    a 		=tokenize_function(lowercase		)
    a 		=dataset.map(lowercase       ,    batched=lowercase       ,    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		):
        # 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       ,    lowercase       ,    args.max_length		)]
            for k, t in concatenated_examples.items()
        }
        return result

    a 		=dataset_tokenized.map(lowercase       ,    batched=lowercase       ,    batch_size=10_00       ,    num_proc=4		)

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

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

        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=lowercase		)


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






'''simple docstring'''




import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class  SCREAMING_SNAKE_CASE    (a__							):


     def __init__(						self						,	_UpperCAmelCase = "▁"						,	_UpperCAmelCase = True						,	_UpperCAmelCase = "<unk>"						,	_UpperCAmelCase = "</s>"						,	_UpperCAmelCase = "<pad>"						,	):

          '''simple docstring'''






          __A						: Dict      =       {
              'pad': {'id': 0, 'token': pad_token},
              'eos': {'id': 1, 'token': eos_token},
              'unk': {'id': 2, 'token': unk_token},
          }

          __A						: List[Any]      =       [None] * len(self.special_tokens)
          for token_dict in self.special_tokens.values():
               __A						: List[str]      =       token_dict['token']

          __A						: str      =       Tokenizer(Unigram())

          __A						: Dict      =       normalizers.Sequence(
              [
                  normalizers.Nmt(),
                  normalizers.NFKC(),
                  normalizers.Replace(Regex(' {2,}')						,	' '),
                  normalizers.Lowercase(),
              ])
          __A						: Union[str, Any]      =       pre_tokenizers.Sequence(
              [
                  pre_tokenizers.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase),
                  pre_tokenizers.Digits(individual_digits=_UpperCAmelCase),
                  pre_tokenizers.Punctuation(),
              ])
          __A						: Any      =       decoders.Metaspace(replacement=_UpperCAmelCase						,	add_prefix_space=_UpperCAmelCase)

          __A						: Dict      =       TemplateProcessing(
              single=F'$A {self.special_tokens["eos"]["token"]}'						,	special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]						,	)

          __A						: Any      =       {
              'model': 'SentencePieceUnigram',
              'replacement': replacement,
              'add_prefix_space': add_prefix_space,
          }

          super().__init__(_UpperCAmelCase						,	_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: str      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          if isinstance(_UpperCAmelCase						,	_UpperCAmelCase):
               __A						: Union[str, Any]      =       [files]
          self._tokenizer.train(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self						,	_UpperCAmelCase						,	_UpperCAmelCase = 8000						,	_UpperCAmelCase = True						,	):

          '''simple docstring'''






          __A						: Dict      =       trainers.UnigramTrainer(
              vocab_size=_UpperCAmelCase						,	special_tokens=self.special_tokens_list						,	show_progress=_UpperCAmelCase						,	)

          self._tokenizer.train_from_iterator(_UpperCAmelCase						,	trainer=_UpperCAmelCase)

          self.add_unk_id()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						: Any      =       json.loads(self._tokenizer.to_str())

          __A						: Union[str, Any]      =       self.special_tokens['unk']['id']

          __A						: str      =       Tokenizer.from_str(json.dumps(_UpperCAmelCase))