





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase_    =				{
    '''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:
	lowerCAmelCase_    =				[
	    '''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

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







def      lowerCamelCase_						(				_UpperCamelCase ,				_UpperCamelCase )		->						str:






				"""simple docstring"""


				return "\n".join(
				    f'''{number} * {i} = {number * i}''' for i in range(1 ,				number_of_terms + 1 ) )


if __name__ == "__main__":
	print(multiplication_table(number=5, number_of_terms=1_0))
