


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case							: Dict      =   {
    '''configuration_xlm_roberta''': [
        '''XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''XLMRobertaConfig''',
        '''XLMRobertaOnnxConfig''',
    ],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: List[str]      =   ['''XLMRobertaTokenizer''']

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Optional[Any]      =   ['''XLMRobertaTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: Union[str, Any]      =   [
						    '''XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''XLMRobertaForCausalLM''',
						    '''XLMRobertaForMaskedLM''',
						    '''XLMRobertaForMultipleChoice''',
						    '''XLMRobertaForQuestionAnswering''',
						    '''XLMRobertaForSequenceClassification''',
						    '''XLMRobertaForTokenClassification''',
						    '''XLMRobertaModel''',
						    '''XLMRobertaPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: str      =   [
						    '''TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFXLMRobertaForCausalLM''',
						    '''TFXLMRobertaForMaskedLM''',
						    '''TFXLMRobertaForMultipleChoice''',
						    '''TFXLMRobertaForQuestionAnswering''',
						    '''TFXLMRobertaForSequenceClassification''',
						    '''TFXLMRobertaForTokenClassification''',
						    '''TFXLMRobertaModel''',
						    '''TFXLMRobertaPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							: int      =   [
						    '''FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''FlaxXLMRobertaForMaskedLM''',
						    '''FlaxXLMRobertaForCausalLM''',
						    '''FlaxXLMRobertaForMultipleChoice''',
						    '''FlaxXLMRobertaForQuestionAnswering''',
						    '''FlaxXLMRobertaForSequenceClassification''',
						    '''FlaxXLMRobertaForTokenClassification''',
						    '''FlaxXLMRobertaModel''',
						    '''FlaxXLMRobertaPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_xlm_roberta import (
						    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    XLMRobertaConfig,
						    XLMRobertaOnnxConfig,
						)

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta import XLMRobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_xlm_roberta import (
												    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    XLMRobertaForCausalLM,
												    XLMRobertaForMaskedLM,
												    XLMRobertaForMultipleChoice,
												    XLMRobertaForQuestionAnswering,
												    XLMRobertaForSequenceClassification,
												    XLMRobertaForTokenClassification,
												    XLMRobertaModel,
												    XLMRobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_xlm_roberta import (
												    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFXLMRobertaForCausalLM,
												    TFXLMRobertaForMaskedLM,
												    TFXLMRobertaForMultipleChoice,
												    TFXLMRobertaForQuestionAnswering,
												    TFXLMRobertaForSequenceClassification,
												    TFXLMRobertaForTokenClassification,
												    TFXLMRobertaModel,
												    TFXLMRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_xlm_roberta import (
												    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlaxXLMRobertaForCausalLM,
												    FlaxXLMRobertaForMaskedLM,
												    FlaxXLMRobertaForMultipleChoice,
												    FlaxXLMRobertaForQuestionAnswering,
												    FlaxXLMRobertaForSequenceClassification,
												    FlaxXLMRobertaForTokenClassification,
												    FlaxXLMRobertaModel,
												    FlaxXLMRobertaPreTrainedModel,
												)

else:
						import sys

						snake_case							: int      =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if length <= 0 or not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise ValueError('Length must be a positive integer.'     )
							return [n * (2 * n - 1) for n in range(__lowerCAmelCase     )]


if __name__ == "__main__":
						print(hexagonal_numbers(length=5))
						print(hexagonal_numbers(length=10))




