






def  a__ (__lowercase							:str   )		-> bool:
				_A	:				str   =      [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__":
	_UpperCamelCase			:	Tuple								=input().strip()
	_UpperCamelCase			:	str								='valid' if is_ip_va_address_valid(ip) else 'invalid'
	print(f'''{ip} is a {valid_or_invalid} IP v4 address.''')











import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase			:	List[Any]								=logging.get_logger(__name__)

_UpperCamelCase			:	Dict								={
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Tuple     =   "sew"







		def __init__(					self       ,A__=32       ,A__=768       ,A__=12       ,A__=12       ,A__=3072       ,A__=2       ,A__="gelu"       ,A__=0.1       ,A__=0.1       ,A__=0.1       ,A__=0.0       ,A__=0.1       ,A__=0.1       ,A__=0.02       ,A__=1E-5       ,A__="group"       ,A__="gelu"       ,A__=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,A__=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,A__=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,A__=False       ,A__=128       ,A__=16       ,A__=True       ,A__=0.05       ,A__=10       ,A__=2       ,A__=0.0       ,A__=10       ,A__=0       ,A__="mean"       ,A__=False       ,A__=False       ,A__=256       ,A__=0       ,A__=1       ,A__=2       ,**A__       ,):
						super().__init__(**A__       ,pad_token_id=A__       ,bos_token_id=A__       ,eos_token_id=A__		)
						_A	:				str   =      hidden_size
						_A	:				Optional[Any]   =      feat_extract_norm
						_A	:				Any   =      feat_extract_activation
						_A	:				Optional[int]   =      list(A__		)
						_A	:				str   =      list(A__		)
						_A	:				Optional[Any]   =      list(A__		)
						_A	:				List[Any]   =      conv_bias
						_A	:				Optional[int]   =      num_conv_pos_embeddings
						_A	:				Any   =      num_conv_pos_embedding_groups
						_A	:				Union[str, Any]   =      len(self.conv_dim		)
						_A	:				List[Any]   =      num_hidden_layers
						_A	:				str   =      intermediate_size
						_A	:				List[Any]   =      squeeze_factor
						_A	:				Tuple   =      hidden_act
						_A	:				Union[str, Any]   =      num_attention_heads
						_A	:				Optional[int]   =      hidden_dropout
						_A	:				Optional[int]   =      attention_dropout
						_A	:				Optional[int]   =      activation_dropout
						_A	:				Tuple   =      feat_proj_dropout
						_A	:				Optional[Any]   =      final_dropout
						_A	:				str   =      layerdrop
						_A	:				int   =      layer_norm_eps
						_A	:				List[str]   =      initializer_range
						_A	:				List[Any]   =      vocab_size

						if (
						    (len(self.conv_stride		) != self.num_feat_extract_layers)
						    or (len(self.conv_kernel		) != self.num_feat_extract_layers)
						    or (len(self.conv_dim		) != self.num_feat_extract_layers)
						):
										raise ValueError(
										    '''Configuration for convolutional layers is incorrect.'''
										    '''It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'''
										    f"""but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)"""
										    f"""= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`."""		)

						# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
						_A	:				Union[str, Any]   =      apply_spec_augment
						_A	:				str   =      mask_time_prob
						_A	:				Any   =      mask_time_length
						_A	:				int   =      mask_time_min_masks
						_A	:				str   =      mask_feature_prob
						_A	:				Optional[Any]   =      mask_feature_length
						_A	:				Dict   =      mask_feature_min_masks

						# ctc loss
						_A	:				Optional[int]   =      ctc_loss_reduction
						_A	:				List[str]   =      ctc_zero_infinity

						# sequence classification
						_A	:				List[Any]   =      use_weighted_layer_sum
						_A	:				Optional[int]   =      classifier_proj_size







		@property
		def  A__      (					self		):
						return functools.reduce(operator.mul       ,self.conv_stride       ,1		)



