






import functools
import operator

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


A        =			logging.get_logger(__name__)

A        =			{
    'microsoft/unispeech-sat-base-100h-libri-ft': (
        'https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json'
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				int				    =						"""unispeech-sat"""


		def __init__(						self ,    UpperCamelCase__=32 ,    UpperCamelCase__=768 ,    UpperCamelCase__=12 ,    UpperCamelCase__=12 ,    UpperCamelCase__=3072 ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=0.02 ,    UpperCamelCase__=1E-5 ,    UpperCamelCase__="group" ,    UpperCamelCase__="gelu" ,    UpperCamelCase__=(512, 512, 512, 512, 512, 512, 512) ,    UpperCamelCase__=(5, 2, 2, 2, 2, 2, 2) ,    UpperCamelCase__=(10, 3, 3, 3, 3, 2, 2) ,    UpperCamelCase__=False ,    UpperCamelCase__=128 ,    UpperCamelCase__=16 ,    UpperCamelCase__=False ,    UpperCamelCase__=True ,    UpperCamelCase__=0.05 ,    UpperCamelCase__=10 ,    UpperCamelCase__=2 ,    UpperCamelCase__=0.0 ,    UpperCamelCase__=10 ,    UpperCamelCase__=0 ,    UpperCamelCase__=320 ,    UpperCamelCase__=2 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__=100 ,    UpperCamelCase__=256 ,    UpperCamelCase__=256 ,    UpperCamelCase__=0.1 ,    UpperCamelCase__="mean" ,    UpperCamelCase__=False ,    UpperCamelCase__=False ,    UpperCamelCase__=256 ,    UpperCamelCase__=(512, 512, 512, 512, 1500) ,    UpperCamelCase__=(5, 3, 3, 1, 1) ,    UpperCamelCase__=(1, 2, 3, 1, 1) ,    UpperCamelCase__=512 ,    UpperCamelCase__=0 ,    UpperCamelCase__=1 ,    UpperCamelCase__=2 ,    UpperCamelCase__=504 ,    **UpperCamelCase__ ,    ):
						super().__init__(**UpperCamelCase__ ,    pad_token_id=UpperCamelCase__ ,    bos_token_id=UpperCamelCase__ ,    eos_token_id=UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   hidden_size
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   feat_extract_norm
						SCREAMING_SNAKE_CASE_   :	str													=   feat_extract_activation
						SCREAMING_SNAKE_CASE_   :	str													=   list(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[str]													=   list(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   list(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	List[Any]													=   conv_bias
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   num_conv_pos_embeddings
						SCREAMING_SNAKE_CASE_   :	str													=   num_conv_pos_embedding_groups
						SCREAMING_SNAKE_CASE_   :	int													=   len(self.conv_dim       )
						SCREAMING_SNAKE_CASE_   :	int													=   num_hidden_layers
						SCREAMING_SNAKE_CASE_   :	Dict													=   intermediate_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   hidden_act
						SCREAMING_SNAKE_CASE_   :	Tuple													=   num_attention_heads
						SCREAMING_SNAKE_CASE_   :	Tuple													=   hidden_dropout
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   attention_dropout
						SCREAMING_SNAKE_CASE_   :	Any													=   activation_dropout
						SCREAMING_SNAKE_CASE_   :	Any													=   feat_proj_dropout
						SCREAMING_SNAKE_CASE_   :	str													=   final_dropout
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   layerdrop
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   layer_norm_eps
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   initializer_range
						SCREAMING_SNAKE_CASE_   :	str													=   vocab_size
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   num_clusters
						SCREAMING_SNAKE_CASE_   :	Tuple													=   do_stable_layer_norm
						SCREAMING_SNAKE_CASE_   :	str													=   use_weighted_layer_sum

						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)`, but is `len(config.conv_dim) ='
										    F''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
										    F''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

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

						# parameters for pretraining with codevector quantized representations
						SCREAMING_SNAKE_CASE_   :	List[str]													=   num_codevectors_per_group
						SCREAMING_SNAKE_CASE_   :	Tuple													=   num_codevector_groups
						SCREAMING_SNAKE_CASE_   :	List[str]													=   contrastive_logits_temperature
						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   feat_quantizer_dropout
						SCREAMING_SNAKE_CASE_   :	Any													=   num_negatives
						SCREAMING_SNAKE_CASE_   :	Any													=   codevector_dim
						SCREAMING_SNAKE_CASE_   :	Tuple													=   proj_codevector_dim
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   diversity_loss_weight

						# ctc loss
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   ctc_loss_reduction
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   ctc_zero_infinity

						# SequenceClassification-specific parameter. Feel free to ignore for other classes.
						SCREAMING_SNAKE_CASE_   :	Dict													=   classifier_proj_size

						# XVector-specific parameters. Feel free to ignore for other classes.
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   list(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   list(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	Any													=   list(UpperCamelCase__       )
						SCREAMING_SNAKE_CASE_   :	int													=   xvector_output_dim






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







def 					_lowerCamelCase(			):





				'''simple docstring'''







				return 1



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else two_pence(x - 2      ) + one_pence()



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else five_pence(x - 5      ) + two_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else ten_pence(x - 10      ) + five_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else twenty_pence(x - 20      ) + ten_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else fifty_pence(x - 50      ) + twenty_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else one_pound(x - 100      ) + fifty_pence(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int      ):





				'''simple docstring'''







				return 0 if x < 0 else two_pound(x - 200      ) + one_pound(lowerCAmelCase__      )



def 					_lowerCamelCase(			lowerCAmelCase__				:		int = 200      ):





				'''simple docstring'''







				return two_pound(lowerCAmelCase__      )


if __name__ == "__main__":
				print(solution(int(input().strip())))