


import argparse
import logging
import os

import datasets
import tensorflow as tf

from transformers import AutoTokenizer


lowerCamelCase_       :       int      						=  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_       :       Optional[Any]      						=  parse_args()
       main(args)



"""simple docstring"""







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_rembert import RemBertTokenizer
else:
       lowerCamelCase_       :       Optional[int]      						=  None

lowerCamelCase_       :       Union[str, Any]      						=  logging.get_logger(__name__)
lowerCamelCase_       :       str      						=  {"""vocab_file""": """sentencepiece.model""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase_       :       int      						=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
    """tokenizer_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/tokenizer.json""",
    },
}

lowerCamelCase_       :       Optional[Any]      						=  {
    """google/rembert""": 2_5_6,
}

lowerCamelCase_       :       Optional[Any]      						=  """▁"""






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  VOCAB_FILES_NAMES
 __lowerCAmelCase			 =  PRETRAINED_VOCAB_FILES_MAP
 __lowerCAmelCase			 =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCAmelCase			 =  RemBertTokenizer


 def __init__(		self			,   __A=None			,   __A=None			,   __A=True			,   __A=True			,   __A=False			,   __A="[CLS]"			,   __A="[SEP]"			,   __A="<unk>"			,   __A="[SEP]"			,   __A="<pad>"			,   __A="[CLS]"			,   __A="[MASK]"			,   **__A			,   ) ->					Dict:
     # Mask token behave like a normal word, i.e. include the space before it
     a					=AddedToken(__A			,   lstrip=__A			,   rstrip=__A						) if isinstance(__A			,   __A						) else mask_token

     super().__init__(
         __A			,   tokenizer_file=__A			,   do_lower_case=__A			,   remove_space=__A			,   keep_accents=__A			,   bos_token=__A			,   eos_token=__A			,   unk_token=__A			,   sep_token=__A			,   pad_token=__A			,   cls_token=__A			,   mask_token=__A			,   **__A			,   )

     a					=do_lower_case
     a					=remove_space
     a					=keep_accents
     a					=vocab_file
     a					=False if not self.vocab_file else True


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[self.cls_token_id]
     if token_ids_a is None:
         return cls + token_ids_a + sep
     return cls + token_ids_a + sep + token_ids_a + sep


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None			,   __A = False						) ->					List[int]:

     if already_has_special_tokens:
         if token_ids_a is not None:
             raise ValueError(
                 '''You should not supply a second sequence if the provided sequence of '''
                 '''ids is already formatted with special tokens for the model.'''						)
         return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

     if token_ids_a is not None:
         return [1] + ([0] * len(__A						)) + [1] + ([0] * len(__A						)) + [1]
     return [1] + ([0] * len(__A						)) + [1]


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A = None						) ->					List[int]:
     a					=[self.sep_token_id]
     a					=[self.cls_token_id]

     if token_ids_a is None:
         return len(cls + token_ids_a + sep						) * [0]
     return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]





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

     if os.path.abspath(self.vocab_file						) != os.path.abspath(__A						):
         copyfile(self.vocab_file			,   __A						)

     return (out_vocab_file,)