
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,
)


lowercase__	:Any										=					{
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Optional[int]										=					['XLMRobertaTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	:Dict										=					['XLMRobertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__	: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:
							lowercase__	:Tuple										=					[
							    '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:
							lowercase__	:Dict										=					[
							    '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

							lowercase__	:Optional[int]										=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)








def  UpperCamelCase					(							lowerCAmelCase__ = 1000	):
					'''simple docstring'''
					lowercase					=		-1
					lowercase					=		0
					for a in range(1				,    n // 3	):
										# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
										lowercase					=		(n * n - 2 * a * n) // (2 * n - 2 * a)
										lowercase					=		n - a - b
										if c * c == (a * a + b * b):
															lowercase					=		a * b * c
															if candidate >= product:
																				lowercase					=		candidate
					return product


if __name__ == "__main__":
							print(F'{solution() = }')




