






from typing import TYPE_CHECKING

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


__snake_case         =     {
    """configuration_deberta""": ["""DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaConfig""", """DebertaOnnxConfig"""],
    """tokenization_deberta""": ["""DebertaTokenizer"""],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case         =     ["""DebertaTokenizerFast"""]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case         =     [
					    """DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """DebertaForMaskedLM""",
					    """DebertaForQuestionAnswering""",
					    """DebertaForSequenceClassification""",
					    """DebertaForTokenClassification""",
					    """DebertaModel""",
					    """DebertaPreTrainedModel""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__snake_case         =     [
					    """TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """TFDebertaForMaskedLM""",
					    """TFDebertaForQuestionAnswering""",
					    """TFDebertaForSequenceClassification""",
					    """TFDebertaForTokenClassification""",
					    """TFDebertaModel""",
					    """TFDebertaPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
					from .tokenization_deberta import DebertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_fast import DebertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta import (
										    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaForMaskedLM,
										    DebertaForQuestionAnswering,
										    DebertaForSequenceClassification,
										    DebertaForTokenClassification,
										    DebertaModel,
										    DebertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta import (
										    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaForMaskedLM,
										    TFDebertaForQuestionAnswering,
										    TFDebertaForSequenceClassification,
										    TFDebertaForTokenClassification,
										    TFDebertaModel,
										    TFDebertaPreTrainedModel,
										)


else:
					import sys

					__snake_case         =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



						if not isinstance(UpperCamelCase_    ,       UpperCamelCase_		):
												raise ValueError('multiplicative_persistence() only accepts integral values'		)
						if num < 0:
												raise ValueError('multiplicative_persistence() does not accept negative values'		)

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						str(UpperCamelCase_		)

						while len(UpperCamelCase_		) != 1:
												lowerCAmelCase__    						=						[int(UpperCamelCase_		) for i in num_string]

												lowerCAmelCase__    						=						1
												for i in range(0    ,       len(UpperCamelCase_		)		):
																		total *= numbers[i]

												lowerCAmelCase__    						=						str(UpperCamelCase_		)

												steps += 1
						return steps







def 	_UpperCamelCase   (    UpperCamelCase_       :					int		)  ->   int:

						"""simple docstring"""



						if not isinstance(UpperCamelCase_    ,       UpperCamelCase_		):
												raise ValueError('additive_persistence() only accepts integral values'		)
						if num < 0:
												raise ValueError('additive_persistence() does not accept negative values'		)

						lowerCAmelCase__    						=						0
						lowerCAmelCase__    						=						str(UpperCamelCase_		)

						while len(UpperCamelCase_		) != 1:
												lowerCAmelCase__    						=						[int(UpperCamelCase_		) for i in num_string]

												lowerCAmelCase__    						=						0
												for i in range(0    ,       len(UpperCamelCase_		)		):
																		total += numbers[i]

												lowerCAmelCase__    						=						str(UpperCamelCase_		)

												steps += 1
						return steps


if __name__ == "__main__":
					import doctest

					doctest.testmod()





