'''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


_lowerCamelCase :				List[str]														= logging.getLogger(__name__)



def  _lowerCAmelCase				(				)  ->      Union[str, Any]:
     '''simple docstring'''







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

     _UpperCamelCase  :Union[str, Any]       =parser.parse_args()
     return args



def  _lowerCAmelCase				(				__a      )  ->      Dict:
     '''simple docstring'''







     _UpperCamelCase  :Optional[int]       =datasets.load_dataset(args.dataset_name			,      args.dataset_config			,      split="""train"""      )

     if args.limit is not None:
          _UpperCamelCase  :Union[str, Any]       =min(len(a_      )			,      args.limit      )
          _UpperCamelCase  :int       =dataset.select(range(a_      )      )
          logger.info(F'''Limiting the dataset to {args.limit} entries.'''      )

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

    # Prepare the tokenizer.
     _UpperCamelCase  :Optional[Any]       =Tokenizer(Unigram()      )
     _UpperCamelCase  :Dict       =normalizers.Sequence([normalizers.Replace("""``"""			,      """\""""      ), normalizers.Replace("""\'\'"""			,      """\""""      )]      )
     _UpperCamelCase  :Any       =pre_tokenizers.Metaspace()

     # Prepare the trainer.
     _UpperCamelCase  :List[Any]       =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=a_      )
     logger.info("""Tokenizer training complete!"""      )

     _UpperCamelCase  :Dict       =tokenizer.token_to_id("""[CLS]"""      )
     _UpperCamelCase  :Any       =tokenizer.token_to_id("""[SEP]"""      )
     _UpperCamelCase  :int       =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  :str       =decoders.Metaspace()

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


if __name__ == "__main__":
      _lowerCamelCase :				List[Any]														= parse_args()
      main(args)







import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
							from .testing_utils import (
							    floats_tensor,
							    load_hf_numpy,
							    load_image,
							    load_numpy,
							    load_pt,
							    nightly,
							    parse_flag_from_env,
							    print_tensor_test,
							    require_torch_a,
							    require_torch_gpu,
							    skip_mps,
							    slow,
							    torch_all_close,
							    torch_device,
							)
							from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


A_			:List[str]	    =		get_logger(__name__)






def 				A	(			a_   )	->		Tuple:
					if version.parse(a_   ) < version.parse(a_   ):
										if "dev" in min_version:
															__UpperCamelCase :   List[str]									=(
															    'This example requires a source install from HuggingFace diffusers (see '
															    '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
															)
										else:
															__UpperCamelCase :   Tuple									=F'This example requires a minimum version of {min_version},'
										error_message += F' but the version found is {__version__}.\n'
										raise ImportError(a_   )

