
"""simple docstring"""


from typing import Callable, Optional

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







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


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




						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Optional[int]:
							# Build iterable dataset
							if self.streaming:
								A     = self.builder.as_streaming_dataset(split='train'   )
							# Build regular (map-style) dataset
							else:
								A     = None
								A     = None
								A     = None
								A     = None

								self.builder.download_and_prepare(
								    download_config=A_	,download_mode=A_	,verification_mode=A_	,base_path=A_	,num_proc=self.num_proc	,)
								A     = self.builder.as_dataset(
								    split='train'	,verification_mode=A_	,in_memory=self.keep_in_memory   )
							return dataset

"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase				 =					{
    '''configuration_xlm_roberta_xl''': [
        '''XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaXLConfig''',
        '''XLMRobertaXLOnnxConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XLMRobertaXLForCausalLM''',
		    '''XLMRobertaXLForMaskedLM''',
		    '''XLMRobertaXLForMultipleChoice''',
		    '''XLMRobertaXLForQuestionAnswering''',
		    '''XLMRobertaXLForSequenceClassification''',
		    '''XLMRobertaXLForTokenClassification''',
		    '''XLMRobertaXLModel''',
		    '''XLMRobertaXLPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_xlm_roberta_xl import (
		    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XLMRobertaXLConfig,
		    XLMRobertaXLOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_roberta_xl import (
				    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMRobertaXLForCausalLM,
				    XLMRobertaXLForMaskedLM,
				    XLMRobertaXLForMultipleChoice,
				    XLMRobertaXLForQuestionAnswering,
				    XLMRobertaXLForSequenceClassification,
				    XLMRobertaXLForTokenClassification,
				    XLMRobertaXLModel,
				    XLMRobertaXLPreTrainedModel,
				)

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure)