






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase   :   Dict      =    {
    """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:
						__UpperCamelCase   :   Any      =    [
						    """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

						__UpperCamelCase   :   Optional[Any]      =    _LazyModule(__name__, globals()["""__file__"""], _import_structure)



"""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