"""simple docstring"""







import importlib
import inspect
from functools import wraps
from typing import TYPE_CHECKING, Optional, Union

from .download.streaming_download_manager import (
    xbasename,
    xdirname,
    xet_parse,
    xexists,
    xgetsize,
    xglob,
    xgzip_open,
    xisdir,
    xisfile,
    xjoin,
    xlistdir,
    xnumpy_load,
    xopen,
    xpandas_read_csv,
    xpandas_read_excel,
    xPath,
    xrelpath,
    xsio_loadmat,
    xsplit,
    xsplitext,
    xwalk,
    xxml_dom_minidom_parse,
)
from .utils.logging import get_logger
from .utils.patching import patch_submodule
from .utils.py_utils import get_imports


_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   get_logger(__name__)


if TYPE_CHECKING:
     from .builder import DatasetBuilder



def       A__    (	lowercase_     :	int      , lowercase_     :	Optional[Union[str, bool]] = None						)  ->							Optional[Any]:
   __lowerCamelCase    		=					importlib.import_module(lowercase_						)

   # TODO(QL): always update the module to add subsequent new authentication
   if hasattr(lowercase_      , '_patched_for_streaming'						) and module._patched_for_streaming:
      return

   def wrap_auth(lowercase_     :	Optional[int]						):
      @wraps(lowercase_						)
      def wrapper(*lowercase_     :	Union[str, Any]      , **lowercase_     :	Tuple						):
         return function(*lowercase_      , use_auth_token=lowercase_      , **lowercase_						)

      __lowerCamelCase    		=					'wrap_auth'
      return wrapper

   # open files in a streaming fashion
   patch_submodule(lowercase_      , 'open'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'os.listdir'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'os.walk'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'glob.glob'      , wrap_auth(lowercase_						)						).start()
   # allow to navigate in remote zip files
   patch_submodule(lowercase_      , 'os.path.join'      , lowercase_						).start()
   patch_submodule(lowercase_      , 'os.path.dirname'      , lowercase_						).start()
   patch_submodule(lowercase_      , 'os.path.basename'      , lowercase_						).start()
   patch_submodule(lowercase_      , 'os.path.relpath'      , lowercase_						).start()
   patch_submodule(lowercase_      , 'os.path.split'      , lowercase_						).start()
   patch_submodule(lowercase_      , 'os.path.splitext'      , lowercase_						).start()
   # allow checks on paths
   patch_submodule(lowercase_      , 'os.path.exists'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'os.path.isdir'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'os.path.isfile'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'os.path.getsize'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'pathlib.Path'      , lowercase_						).start()
   # file readers
   patch_submodule(lowercase_      , 'gzip.open'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'numpy.load'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'pandas.read_csv'      , wrap_auth(lowercase_						)      , attrs=['__version__']						).start()
   patch_submodule(lowercase_      , 'pandas.read_excel'      , wrap_auth(lowercase_						)      , attrs=['__version__']						).start()
   patch_submodule(lowercase_      , 'scipy.io.loadmat'      , wrap_auth(lowercase_						)      , attrs=['__version__']						).start()
   patch_submodule(lowercase_      , 'xml.etree.ElementTree.parse'      , wrap_auth(lowercase_						)						).start()
   patch_submodule(lowercase_      , 'xml.dom.minidom.parse'      , wrap_auth(lowercase_						)						).start()
   __lowerCamelCase    		=					True



def       A__    (	lowercase_     :	"DatasetBuilder"						)  ->							Optional[int]:
   extend_module_for_streaming(builder.__module__      , use_auth_token=builder.use_auth_token						)
   # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
   if not builder.__module__.startswith('datasets.'						):  # check that it's not a packaged builder like csv
      for imports in get_imports(inspect.getfile(builder.__class__						)						):
         if imports[0] == "internal":
            __lowerCamelCase    		=					imports[1]
            __lowerCamelCase    		=					'.'.join(builder.__module__.split('.'						)[:-1] + [internal_import_name]						)
            extend_module_for_streaming(lowercase_      , use_auth_token=builder.use_auth_token						)

    # builders can inherit from other builders that might use streaming functionality
    # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
    # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
   from .builder import DatasetBuilder

   __lowerCamelCase    		=					[
       cls.__module__
       for cls in type(lowercase_						).__mro__[1:]  # make sure it's not the same module we've already patched
       if issubclass(cls      , lowercase_						) and cls.__module__ != DatasetBuilder.__module__
   ]  # check it's not a standard builder from datasets.builder
   for module in parent_builder_modules:
      extend_module_for_streaming(lowercase_      , use_auth_token=builder.use_auth_token						)







'''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


_lowercase		:					str													=		logging.getLogger(__name__)




def 				lowerCamelCase      ( )     -> Tuple:
							lowercase_   :     Any									=			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=1000 , help="""Batch size during training.""" , )
							parser.add_argument(
							    """--vocab_size""" , type=UpperCAmelCase__ , default=10048 , 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""" , )

							lowercase_   :     List[Any]									=			parser.parse_args()
							return args




def 				lowerCamelCase      ( UpperCAmelCase__			:   Optional[int]					)     -> List[Any]:
							lowercase_   :     Any									=			datasets.load_dataset(args.dataset_name , args.dataset_config , split="""train"""					)

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

							# Prepare the trainer.
							lowercase_   :     str									=			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!"""					)

							lowercase_   :     Dict									=			tokenizer.token_to_id("""[CLS]"""					)
							lowercase_   :     Any									=			tokenizer.token_to_id("""[SEP]"""					)
							lowercase_   :     Optional[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),
							    ] , )
							lowercase_   :     int									=			decoders.Metaspace()

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


if __name__ == "__main__":
						_lowercase		:					Dict													=		parse_args()
						main(args)





