


"""simple docstring"""




from typing import TYPE_CHECKING

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


__magic_name__						: Optional[int]    =				{
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Tuple    =				['DebertaTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Optional[Any]    =				[
					    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'DebertaForMaskedLM',
					    'DebertaForQuestionAnswering',
					    'DebertaForSequenceClassification',
					    'DebertaForTokenClassification',
					    'DebertaModel',
					    'DebertaPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__magic_name__						: Dict    =				[
					    '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

					__magic_name__						: Any    =				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)








"""simple docstring"""




def       a_ (	lowercase__ :str     ):
			__lowerCamelCase          =      [int(lowercase__     ) for i in ip_va_address.split("""."""     ) if i.isdigit()]
			return len(lowercase__     ) == 4 and all(0 <= int(lowercase__     ) <= 254 for octet in octets     )


if __name__ == "__main__":
					__magic_name__						: Tuple    =				input().strip()
					__magic_name__						: Optional[int]    =				'valid' if is_ip_va_address_valid(ip) else 'invalid'
					print(f"""{ip} is a {valid_or_invalid} IP v4 address.""")




