






import math

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


a_		:    int   							=     logging.get_logger(__name__)

a_		:    Optional[Any]   							=     {
    'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class 		_snake_case	(	A__       ):
			_lowercase		:     Optional[Any]        =				'''data2vec-audio'''






			def __init__(							self		,       a=32		,       a=768		,       a=12		,       a=12		,       a=3072		,       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="gelu"		,       a=(512, 512, 512, 512, 512, 512, 512)		,       a=(5, 2, 2, 2, 2, 2, 2)		,       a=(10, 3, 3, 3, 3, 2, 2)		,       a=False		,       a=16		,       a=19		,       a=5		,       a=0.05		,       a=10		,       a=2		,       a=0.0		,       a=10		,       a=0		,       a="sum"		,       a=False		,       a=False		,       a=256		,       a=(512, 512, 512, 512, 1500)		,       a=(5, 3, 3, 1, 1)		,       a=(1, 2, 3, 1, 1)		,       a=512		,       a=0		,       a=1		,       a=2		,       a=False		,       a=3		,       a=2		,       a=3		,       a=None		,       **a		,       )   ->							List[str]:
						super().__init__(**a		,       pad_token_id=a		,       bos_token_id=a		,       eos_token_id=a)
						SCREAMING_SNAKE_CASE							=			hidden_size
						SCREAMING_SNAKE_CASE							=			feat_extract_activation
						SCREAMING_SNAKE_CASE							=			list(a)
						SCREAMING_SNAKE_CASE							=			list(a)
						SCREAMING_SNAKE_CASE							=			list(a)
						SCREAMING_SNAKE_CASE							=			conv_bias
						SCREAMING_SNAKE_CASE							=			num_conv_pos_embeddings
						SCREAMING_SNAKE_CASE							=			num_conv_pos_embedding_groups
						SCREAMING_SNAKE_CASE							=			conv_pos_kernel_size
						SCREAMING_SNAKE_CASE							=			len(self.conv_dim)
						SCREAMING_SNAKE_CASE							=			num_hidden_layers
						SCREAMING_SNAKE_CASE							=			intermediate_size
						SCREAMING_SNAKE_CASE							=			hidden_act
						SCREAMING_SNAKE_CASE							=			num_attention_heads
						SCREAMING_SNAKE_CASE							=			hidden_dropout
						SCREAMING_SNAKE_CASE							=			attention_dropout
						SCREAMING_SNAKE_CASE							=			activation_dropout
						SCREAMING_SNAKE_CASE							=			feat_proj_dropout
						SCREAMING_SNAKE_CASE							=			final_dropout
						SCREAMING_SNAKE_CASE							=			layerdrop
						SCREAMING_SNAKE_CASE							=			layer_norm_eps
						SCREAMING_SNAKE_CASE							=			initializer_range
						SCREAMING_SNAKE_CASE							=			vocab_size
						SCREAMING_SNAKE_CASE							=			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							=			mask_time_prob
						SCREAMING_SNAKE_CASE							=			mask_time_length
						SCREAMING_SNAKE_CASE							=			mask_time_min_masks
						SCREAMING_SNAKE_CASE							=			mask_feature_prob
						SCREAMING_SNAKE_CASE							=			mask_feature_length
						SCREAMING_SNAKE_CASE							=			mask_feature_min_masks

						# ctc loss
						SCREAMING_SNAKE_CASE							=			ctc_loss_reduction
						SCREAMING_SNAKE_CASE							=			ctc_zero_infinity

						# adapter
						SCREAMING_SNAKE_CASE							=			add_adapter
						SCREAMING_SNAKE_CASE							=			adapter_kernel_size
						SCREAMING_SNAKE_CASE							=			adapter_stride
						SCREAMING_SNAKE_CASE							=			num_adapter_layers
						SCREAMING_SNAKE_CASE							=			output_hidden_size or hidden_size

						# SequenceClassification-specific parameter. Feel free to ignore for other classes.
						SCREAMING_SNAKE_CASE							=			classifier_proj_size

						# XVector-specific parameters. Feel free to ignore for other classes.
						SCREAMING_SNAKE_CASE							=			list(a)
						SCREAMING_SNAKE_CASE							=			list(a)
						SCREAMING_SNAKE_CASE							=			list(a)
						SCREAMING_SNAKE_CASE							=			xvector_output_dim







			@property
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						return math.prod(self.conv_stride)








'''simple docstring'''



A__						:   dict[tuple[int, int, int], int]				=    {}



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:

     # if we are absent twice, or late 3 consecutive days,
     # no further prize strings are possible
     if late == 3 or absent == 2:
          return 0

     # if we have no days left, and have not failed any other rules,
     # we have a prize string
     if days == 0:
          return 1

     # No easy solution, so now we need to do the recursive calculation

     # First, check if the combination is already in the cache, and
     # if yes, return the stored value from there since we already
     # know the number of possible prize strings from this point on
     __lowerCamelCase  :      List[Any]														=						(days, absent, late)
     if key in cache:
          return cache[key]

     # now we calculate the three possible ways that can unfold from
     # this point on, depending on our attendance today

     # 1) if we are late (but not absent), the "absent" counter stays as
     # it is, but the "late" counter increases by one
     __lowerCamelCase  :      Tuple														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       late + 1				)

     # 2) if we are absent, the "absent" counter increases by 1, and the
     # "late" counter resets to 0
     __lowerCamelCase  :      int														=						_calculate(days - 1 ,       absent + 1 ,       0				)

     # 3) if we are on time, this resets the "late" counter and keeps the
     # absent counter
     __lowerCamelCase  :      List[Any]														=						_calculate(days - 1 ,       UpperCAmelCase_ ,       0				)

     __lowerCamelCase  :      Optional[int]														=						state_late + state_absent + state_ontime

     __lowerCamelCase  :      Union[str, Any]														=						prizestrings
     return prizestrings



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int = 30				)	->						int:
     return _calculate(UpperCAmelCase_ ,       absent=0 ,       late=0				)


if __name__ == "__main__":
      print(solution())






