

"""simple docstring"""





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       :  Union[str, Any]        = logging.getLogger(__name__)



def        _lowerCAmelCase      (						):


     '''simple docstring'''






     UpperCamelCase__		:    List[str]         =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=1_000      ,							help='''Batch size during training.'''      ,							)
     parser.add_argument(
         '''--vocab_size'''      ,							type=UpperCAmelCase      ,							default=10_048      ,							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'''      ,							)

     UpperCamelCase__		:    Tuple         =parser.parse_args()
     return args



def        _lowerCAmelCase      (						UpperCAmelCase     :							List[Any]				):


     '''simple docstring'''






     UpperCamelCase__		:    int         =datasets.load_dataset(args.dataset_name      ,							args.dataset_config      ,							split='''train'''				)

     if args.limit is not None:
          UpperCamelCase__		:    List[Any]         =min(len(UpperCAmelCase				)      ,							args.limit				)
          UpperCamelCase__		:    int         =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.
     UpperCamelCase__		:    Dict         =Tokenizer(Unigram()				)
     UpperCamelCase__		:    Dict         =normalizers.Sequence([normalizers.Replace('''``'''      ,							'''"'''				), normalizers.Replace('''\'\''''      ,							'''"'''				)]				)
     UpperCamelCase__		:    str         =pre_tokenizers.Metaspace()

     # Prepare the trainer.
     UpperCamelCase__		:    Dict         =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!'''				)

     UpperCamelCase__		:    Any         =tokenizer.token_to_id('''[CLS]'''				)
     UpperCamelCase__		:    int         =tokenizer.token_to_id('''[SEP]'''				)
     UpperCamelCase__		:    Union[str, Any]         =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),
         ]      ,							)
     UpperCamelCase__		:    Union[str, Any]         =decoders.Metaspace()

     if args.export_to_hub:
          logger.info('''Exporting the trained tokenzier to Hub.'''				)
          UpperCamelCase__		:    str         =AlbertTokenizerFast(tokenizer_object=UpperCAmelCase				)
          new_tokenizer.push_to_hub('''unigram-tokenizer-dataset'''				)


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE       :  Optional[Any]        = parse_args()
    main(args)





"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_SCREAMING_SNAKE_CASE       :  List[Any]        = {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Optional[Any]        = ["""T5Tokenizer"""]

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  str        = ["""T5TokenizerFast"""]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """T5EncoderModel""",
				    """T5ForConditionalGeneration""",
				    """T5Model""",
				    """T5PreTrainedModel""",
				    """load_tf_weights_in_t5""",
				    """T5ForQuestionAnswering""",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Dict        = [
				    """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """TFT5EncoderModel""",
				    """TFT5ForConditionalGeneration""",
				    """TFT5Model""",
				    """TFT5PreTrainedModel""",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_SCREAMING_SNAKE_CASE       :  Any        = [
				    """FlaxT5EncoderModel""",
				    """FlaxT5ForConditionalGeneration""",
				    """FlaxT5Model""",
				    """FlaxT5PreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta import TaTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_ta_fast import TaTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ta import (
								    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TaEncoderModel,
								    TaForConditionalGeneration,
								    TaForQuestionAnswering,
								    TaModel,
								    TaPreTrainedModel,
								    load_tf_weights_in_ta,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ta import (
								    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFTaEncoderModel,
								    TFTaForConditionalGeneration,
								    TFTaModel,
								    TFTaPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_ta import (
								    FlaxTaEncoderModel,
								    FlaxTaForConditionalGeneration,
								    FlaxTaModel,
								    FlaxTaPreTrainedModel,
								)


else:
				import sys

				_SCREAMING_SNAKE_CASE       :  str        = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


