"""simple docstring"""




from typing import TYPE_CHECKING

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


_a				:		int	   =       {}

try:
     if not is_sentencepiece_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		str	   =       ["""NllbTokenizer"""]

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _a				:		Dict	   =       ["""NllbTokenizerFast"""]


if TYPE_CHECKING:
     try:
          if not is_sentencepiece_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb import NllbTokenizer

     try:
          if not is_tokenizers_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .tokenization_nllb_fast import NllbTokenizerFast

else:
     import sys

     _a				:		List[str]	   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





"""simple docstring"""




from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__    ,					a__ = None    ,					a__ = None    ,					a__ = True    ,					a__ = None    ,					a__ = False    ,					a__ = None    ,					a__ = True    ,					a__ = "arrow"    ,					**a__    ,					):
       super().__init__(
           split=a__    ,					features=a__    ,					cache_dir=a__    ,					keep_in_memory=a__    ,					streaming=a__    ,					**a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					load_from_cache_file
       _lowerCAmelCase						:	str        =					file_format
       _lowerCAmelCase						:	Dict        =					Spark(
           df=a__    ,					features=a__    ,					cache_dir=a__    ,					working_dir=a__    ,					**a__    ,					)






     def    __A  (		self  ):
       if self.streaming:
         return self.builder.as_streaming_dataset(split=self.split  )
       _lowerCAmelCase						:	Dict        =					None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
       self.builder.download_and_prepare(
           download_mode=a__    ,					file_format=self._file_format    ,					)
       return self.builder.as_dataset(split=self.split  )




