

from typing import Optional

from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



class 						_A     (				snake_case				):





  '''simple docstring'''



  def __init__(  self   ,SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = None   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = False   ,SCREAMING_SNAKE_CASE_ = None   ,**SCREAMING_SNAKE_CASE_   ,):

      '''simple docstring'''
      super().__init__(
          SCREAMING_SNAKE_CASE_   ,split=SCREAMING_SNAKE_CASE_   ,features=SCREAMING_SNAKE_CASE_   ,cache_dir=SCREAMING_SNAKE_CASE_   ,keep_in_memory=SCREAMING_SNAKE_CASE_   ,streaming=SCREAMING_SNAKE_CASE_   ,num_proc=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)
      snake_case							:		Any        =		path_or_paths if isinstance(SCREAMING_SNAKE_CASE_   ,SCREAMING_SNAKE_CASE_    ) else {self.split: path_or_paths}
      snake_case							:		str        =		Text(
          cache_dir=SCREAMING_SNAKE_CASE_   ,data_files=SCREAMING_SNAKE_CASE_   ,features=SCREAMING_SNAKE_CASE_   ,**SCREAMING_SNAKE_CASE_   ,)





  def    snake_case_       (  self    ):

      '''simple docstring'''
      # Build iterable dataset
      if self.streaming:
          snake_case							:		List[str]        =		self.builder.as_streaming_dataset(split=self.split    )
      # Build regular (map-style) dataset
      else:
          snake_case							:		int        =		None
          snake_case							:		Tuple        =		None
          snake_case							:		str        =		None
          snake_case							:		List[str]        =		None

          self.builder.download_and_prepare(
              download_config=SCREAMING_SNAKE_CASE_   ,download_mode=SCREAMING_SNAKE_CASE_   ,verification_mode=SCREAMING_SNAKE_CASE_   ,base_path=SCREAMING_SNAKE_CASE_   ,num_proc=self.num_proc   ,)
          snake_case							:		Any        =		self.builder.as_dataset(
              split=self.split   ,verification_mode=SCREAMING_SNAKE_CASE_   ,in_memory=self.keep_in_memory    )
      return dataset





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A  =	{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SEWForCTC",
			    "SEWForSequenceClassification",
			    "SEWModel",
			    "SEWPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sew import (
						    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SEWForCTC,
						    SEWForSequenceClassification,
						    SEWModel,
						    SEWPreTrainedModel,
						)


else:
			import sys

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



