


from typing import TYPE_CHECKING

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


lowercase_								=    {
    '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_								=    ['DebertaV2TokenizerFast']

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_								=    [
		    '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_								=    [
		    '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_								=    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






import math


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
						_a     =			math.log(2							,     10				) * number
						_a     =			round((log_number - int(_UpperCAmelCase				))							,     15				)

						return difference


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase = 678910				)    ->  int:
						_a     =			90
						_a     =			0

						_a     =			math.log(1.23							,     10				)
						_a     =			math.log(1.24							,     10				)
						_a     =			0

						while position < number:
												_a     =			log_difference(_UpperCAmelCase				)

												if difference >= upper_limit:
																		power_iterator += 93

												elif difference < lower_limit:
																		power_iterator += 196

												else:
																		_a     =			power_iterator
																		power_iterator += 196
																		position += 1

						return previous_power


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		print(f"""{solution() = }""")


