


'''simple docstring'''






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       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = None ,			__magic_name__ = False ,			__magic_name__ = False ,			__magic_name__ = None ,			**__magic_name__ ,			)   ->							List[Any]:
      super().__init__(
          __magic_name__ ,			split=__magic_name__ ,			features=__magic_name__ ,			cache_dir=__magic_name__ ,			keep_in_memory=__magic_name__ ,			streaming=__magic_name__ ,			num_proc=__magic_name__ ,			**__magic_name__ ,			)
      _a      =							path_or_paths if isinstance(__magic_name__ ,			__magic_name__  ) else {self.split: path_or_paths}
      _a      =							Text(
          cache_dir=__magic_name__ ,			data_files=__magic_name__ ,			features=__magic_name__ ,			**__magic_name__ ,			)




 def 							__UpperCAmelCase						(	self  )   ->							List[str]:
      # Build iterable dataset
      if self.streaming:
           _a      =							self.builder.as_streaming_dataset(split=self.split  )
      # Build regular (map-style) dataset
      else:
           _a      =							None
           _a      =							None
           _a      =							None
           _a      =							None

           self.builder.download_and_prepare(
               download_config=__magic_name__ ,			download_mode=__magic_name__ ,			verification_mode=__magic_name__ ,			base_path=__magic_name__ ,			num_proc=self.num_proc ,			)
           _a      =							self.builder.as_dataset(
               split=self.split ,			verification_mode=__magic_name__ ,			in_memory=self.keep_in_memory  )
      return dataset





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

 a_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

