



from typing import TYPE_CHECKING

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


_lowercase:					int      =						{
    "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:					List[Any]      =						["DebertaV2TokenizerFast"]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:					Tuple      =						[
				    "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:					Optional[int]      =						[
				    "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:					Optional[Any]      =						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



def  _snake_case		(snake_case_					:     int = 100    ,       snake_case_					:     int = 100  )							->		int:
  return max(
      sum(int(snake_case_  ) for x in str(base**power  )  )
      for base in range(snake_case_  )
      for power in range(snake_case_  )  )


# Tests
if __name__ == "__main__":
      import doctest

      doctest.testmod()





