
from typing import TYPE_CHECKING

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


A		: List[Any]								=     {
    '''configuration_nezha''': ['''NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NezhaConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A		: str								=     [
		    '''NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''NezhaForNextSentencePrediction''',
		    '''NezhaForMaskedLM''',
		    '''NezhaForPreTraining''',
		    '''NezhaForMultipleChoice''',
		    '''NezhaForQuestionAnswering''',
		    '''NezhaForSequenceClassification''',
		    '''NezhaForTokenClassification''',
		    '''NezhaModel''',
		    '''NezhaPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_nezha import (
				    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    NezhaForMaskedLM,
				    NezhaForMultipleChoice,
				    NezhaForNextSentencePrediction,
				    NezhaForPreTraining,
				    NezhaForQuestionAnswering,
				    NezhaForSequenceClassification,
				    NezhaForTokenClassification,
				    NezhaModel,
				    NezhaPreTrainedModel,
				)


else:
		import sys

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






'''simple docstring'''






from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream




class 					UpperCAmelCase	(     snake_case_  ):




      def __init__(     self     :					Union[str, Any]     ,     __snake_case     :					Callable     ,     __snake_case     :					Optional[Features] = None     ,     __snake_case     :					str = None     ,     __snake_case     :					bool = False     ,     __snake_case     :					bool = False     ,     __snake_case     :					Optional[dict] = None     ,     __snake_case     :					Optional[int] = None     ,     **__snake_case     :					Optional[Any]     ,     )							-> Union[str, Any]:
            super().__init__(
                features=__snake_case     ,     cache_dir=__snake_case     ,     keep_in_memory=__snake_case     ,     streaming=__snake_case     ,     num_proc=__snake_case     ,     **__snake_case     ,     )
            _lowerCAmelCase					=				Generator(
                cache_dir=__snake_case     ,     features=__snake_case     ,     generator=__snake_case     ,     gen_kwargs=__snake_case     ,     **__snake_case     ,     )




      def 							lowercase__       (     self     :					str    )							-> Dict:
            # Build iterable dataset
            if self.streaming:
                  _lowerCAmelCase					=				self.builder.as_streaming_dataset(split="""train"""    )
            # Build regular (map-style) dataset
            else:
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None
                  _lowerCAmelCase					=				None

                  self.builder.download_and_prepare(
                      download_config=__snake_case     ,     download_mode=__snake_case     ,     verification_mode=__snake_case     ,     base_path=__snake_case     ,     num_proc=self.num_proc     ,     )
                  _lowerCAmelCase					=				self.builder.as_dataset(
                      split="""train"""     ,     verification_mode=__snake_case     ,     in_memory=self.keep_in_memory    )
            return dataset






