






from typing import TYPE_CHECKING

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


lowercase     :							Tuple	     =			{
    '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
    '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase     :							str	     =			['''DebertaV2TokenizerFast''']

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase     :							Optional[Any]	     =			[
							    '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFDebertaV2ForMaskedLM''',
							    '''TFDebertaV2ForQuestionAnswering''',
							    '''TFDebertaV2ForSequenceClassification''',
							    '''TFDebertaV2ForTokenClassification''',
							    '''TFDebertaV2Model''',
							    '''TFDebertaV2PreTrainedModel''',
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase     :							Tuple	     =			[
							    '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''DebertaV2ForMaskedLM''',
							    '''DebertaV2ForMultipleChoice''',
							    '''DebertaV2ForQuestionAnswering''',
							    '''DebertaV2ForSequenceClassification''',
							    '''DebertaV2ForTokenClassification''',
							    '''DebertaV2Model''',
							    '''DebertaV2PreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

							lowercase     :							str	     =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)








def    lowerCAmelCase__		(			_a				:	int			):
		if not isinstance(_a     ,					_a			):
				snake_case_ :							Optional[int]         =				F'''Input value of [number={number}] must be an integer'''
				raise TypeError(_a			)
		if number < 1:
				raise ValueError("Input must be a positive integer"			)
		return number * (2 * number - 1)


if __name__ == "__main__":
							import doctest

							doctest.testmod()
