


"""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
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {'vocab_file': 'vocab.json'}

_snake_case          =   {
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_snake_case          =   {'mgp-str': 27}






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       List[str]			=					VOCAB_FILES_NAMES
 UpperCamelCase					:       Union[str, Any]			=					PRETRAINED_VOCAB_FILES_MAP
 UpperCamelCase					:       Optional[int]			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						List[Any]="[GO]"							,						UpperCAmelCase__		:						Tuple="[GO]"							,						UpperCAmelCase__		:						Optional[int]="[s]"							,						UpperCAmelCase__		:						int="[GO]"							,						**UpperCAmelCase__		:						Dict							)					->       int:
   super().__init__(
       unk_token=UpperCAmelCase__							,						bos_token=UpperCAmelCase__							,						eos_token=UpperCAmelCase__							,						pad_token=UpperCAmelCase__							,						**UpperCAmelCase__							,						)

   with open(UpperCAmelCase__							,						encoding="""utf-8"""							) as vocab_handle:
     _a  :       int             =	json.load(UpperCAmelCase__							)
   _a  :       Optional[int]             =	{v: k for k, v in self.vocab.items()}


 @property
 def _lowercase	(     self		:						Dict							)					->       Union[str, Any]:
   return len(self.vocab							)


 def _lowercase	(     self		:						Union[str, Any]							)					->       str:
   return dict(self.vocab							,						**self.added_tokens_encoder							)


 def _lowercase	(     self		:						Dict							,						UpperCAmelCase__		:						str							)					->       Union[str, Any]:
   _a  :       Tuple             =	[]
   for s in text:
     char_tokens.extend(UpperCAmelCase__							)
   return char_tokens


 def _lowercase	(     self		:						List[Any]							,						UpperCAmelCase__		:						str							)					->       Dict:
   return self.vocab.get(UpperCAmelCase__							,						self.vocab.get(self.unk_token							)							)


 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						Tuple							)					->       List[Any]:
   return self.decoder.get(UpperCAmelCase__							)







 def _lowercase	(     self		:						Optional[int]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Optional[str] = None							)					->       Tuple[str]:
   if not os.path.isdir(UpperCAmelCase__							):
     logger.error("""Vocabulary path ({}) should be a directory""".format(UpperCAmelCase__							)							)
     return
   _a  :       Tuple             =	os.path.join(
       UpperCAmelCase__							,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

   with open(UpperCAmelCase__							,						"""w"""							,						encoding="""utf-8"""							) as f:
     f.write(json.dumps(self.vocab							,						indent=2							,						sort_keys=UpperCAmelCase__							,						ensure_ascii=UpperCAmelCase__							) + """\n"""							)

   return (vocab_file,)

