import argparse
import logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


_SCREAMING_SNAKE_CASE     =     logging.getLogger(__name__)
def 					__lowercase						(    )					->int:

  '''simple docstring'''




  a__  		=  argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset."       )
  parser.add_argument(
      "--dataset_name"			,					type=_UpperCamelCase			,					default="wikitext"			,					help="Name of the training. Explore datasets at: hf.co/datasets."			,					)
  parser.add_argument(
      "--dataset_config"			,					type=_UpperCamelCase			,					default="wikitext-103-raw-v1"			,					help="Configuration name of the dataset."       )
  parser.add_argument(
      "--batch_size"			,					type=_UpperCamelCase			,					default=10_00			,					help="Batch size during training."			,					)
  parser.add_argument(
      "--vocab_size"			,					type=_UpperCamelCase			,					default=1_00_48			,					help="Size of the desired vocabulary."			,					)
  parser.add_argument(
      "--limit"			,					default=_UpperCamelCase			,					type=_UpperCamelCase			,					help="Limit the number of shards (used for debugging)."			,					)
  parser.add_argument(
      "--export_to_hub"			,					action="store_true"			,					)

  a__  		=  parser.parse_args()
  return args
def 					__lowercase						(    lowerCamelCase__       )					->Optional[Any]:

  '''simple docstring'''




  a__  		=  datasets.load_dataset(args.dataset_name			,					args.dataset_config			,					split="train"       )

  if args.limit is not None:
    a__  		=  min(len(_UpperCamelCase       )			,					args.limit       )
    a__  		=  dataset.select(range(_UpperCamelCase       )       )
    logger.info(f"Limiting the dataset to {args.limit} entries."       )

  def batch_iterator():
    for i in range(0			,					len(_UpperCamelCase       )			,					args.batch_size       ):
      yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
  a__  		=  Tokenizer(Unigram()       )
  a__  		=  normalizers.Sequence([normalizers.Replace("``"			,					"\""       ), normalizers.Replace("\'\'"			,					"\""       )]       )
  a__  		=  pre_tokenizers.Metaspace()

  # Prepare the trainer.
  a__  		=  UnigramTrainer(
      unk_token="<unk>"			,					special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"]			,					vocab_size=args.vocab_size			,					)

  logger.info("Training the tokenizer."       )
  tokenizer.train_from_iterator(batch_iterator()			,					trainer=_UpperCamelCase       )
  logger.info("Tokenizer training complete!"       )

  a__  		=  tokenizer.token_to_id("[CLS]"       )
  a__  		=  tokenizer.token_to_id("[SEP]"       )
  a__  		=  processors.TemplateProcessing(
      single="[CLS]:0 $A:0 [SEP]:0"			,					pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1"			,					special_tokens=[
          ("[CLS]", cls_token_id),
          ("[SEP]", sep_token_id),
      ]			,					)
  a__  		=  decoders.Metaspace()

  if args.export_to_hub:
    logger.info("Exporting the trained tokenzier to Hub."       )
    a__  		=  AlbertTokenizerFast(tokenizer_object=_UpperCamelCase       )
    new_tokenizer.push_to_hub("unigram-tokenizer-dataset"       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     parse_args()
     main(args)





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_torch_available


__a      			= {"configuration_bert_generation": ["BertGenerationConfig"]}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= ["BertGenerationTokenizer"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a      			= [
						    "BertGenerationDecoder",
						    "BertGenerationEncoder",
						    "BertGenerationPreTrainedModel",
						    "load_tf_weights_in_bert_generation",
						]


if TYPE_CHECKING:
						from .configuration_bert_generation import BertGenerationConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_bert_generation import BertGenerationTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_bert_generation import (
												    BertGenerationDecoder,
												    BertGenerationEncoder,
												    BertGenerationPreTrainedModel,
												    load_tf_weights_in_bert_generation,
												)

else:
						import sys

						__a      			= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)