






def   __lowercase       (			snake_case			):







				"""simple docstring"""






				__magic_name__		:Optional[int]   =   [int(snake_case			) for i in ip_va_address.split('''.'''			) if i.isdigit()]
				return len(snake_case			) == 4 and all(0 <= int(snake_case			) <= 2_5_4 for octet in octets			)


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














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE__   :      Dict				=		{
    """configuration_canine""": ["""CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CanineConfig"""],
    """tokenization_canine""": ["""CanineTokenizer"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :      str				=		[
					    """CANINE_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """CanineForMultipleChoice""",
					    """CanineForQuestionAnswering""",
					    """CanineForSequenceClassification""",
					    """CanineForTokenClassification""",
					    """CanineLayer""",
					    """CanineModel""",
					    """CaninePreTrainedModel""",
					    """load_tf_weights_in_canine""",
					]


if TYPE_CHECKING:
					from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
					from .tokenization_canine import CanineTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_canine import (
										    CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CanineForMultipleChoice,
										    CanineForQuestionAnswering,
										    CanineForSequenceClassification,
										    CanineForTokenClassification,
										    CanineLayer,
										    CanineModel,
										    CaninePreTrainedModel,
										    load_tf_weights_in_canine,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






