import math

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


lowerCamelCase__    :		Union[str, Any]          =   logging.get_logger(__name__)

lowerCamelCase__    :		Union[str, 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   (	__SCREAMING_SNAKE_CASE							):
 __lowerCAmelCase  :     Union[str, Any]        =			'data2vec-audio'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=32					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-5					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12)					,  SCREAMING_SNAKE_CASE_=(5, 2, 2, 2, 2, 2, 2)					,  SCREAMING_SNAKE_CASE_=(10, 3, 3, 3, 3, 2, 2)					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=19					,  SCREAMING_SNAKE_CASE_=5					,  SCREAMING_SNAKE_CASE_=0.0_5					,  SCREAMING_SNAKE_CASE_=10					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=10					,  SCREAMING_SNAKE_CASE_=0					,  SCREAMING_SNAKE_CASE_="sum"					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=(5_12, 5_12, 5_12, 5_12, 15_00)					,  SCREAMING_SNAKE_CASE_=(5, 3, 3, 1, 1)					,  SCREAMING_SNAKE_CASE_=(1, 2, 3, 1, 1)					,  SCREAMING_SNAKE_CASE_=5_12					,  SCREAMING_SNAKE_CASE_=0					,  SCREAMING_SNAKE_CASE_=1					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=2					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**_a					,  pad_token_id=_a					,  bos_token_id=_a					,  eos_token_id=_a)
   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   Tuple    		=      feat_extract_activation
   lowercase__  :   Optional[Any]    		=      list(_a)
   lowercase__  :   List[str]    		=      list(_a)
   lowercase__  :   Dict    		=      list(_a)
   lowercase__  :   Any    		=      conv_bias
   lowercase__  :   Any    		=      num_conv_pos_embeddings
   lowercase__  :   Tuple    		=      num_conv_pos_embedding_groups
   lowercase__  :   List[Any]    		=      conv_pos_kernel_size
   lowercase__  :   List[Any]    		=      len(self.conv_dim)
   lowercase__  :   int    		=      num_hidden_layers
   lowercase__  :   Any    		=      intermediate_size
   lowercase__  :   Dict    		=      hidden_act
   lowercase__  :   Any    		=      num_attention_heads
   lowercase__  :   str    		=      hidden_dropout
   lowercase__  :   Dict    		=      attention_dropout
   lowercase__  :   Any    		=      activation_dropout
   lowercase__  :   Dict    		=      feat_proj_dropout
   lowercase__  :   Union[str, Any]    		=      final_dropout
   lowercase__  :   int    		=      layerdrop
   lowercase__  :   int    		=      layer_norm_eps
   lowercase__  :   Tuple    		=      initializer_range
   lowercase__  :   List[str]    		=      vocab_size
   lowercase__  :   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
   lowercase__  :   Any    		=      mask_time_prob
   lowercase__  :   int    		=      mask_time_length
   lowercase__  :   Optional[int]    		=      mask_time_min_masks
   lowercase__  :   Any    		=      mask_feature_prob
   lowercase__  :   Union[str, Any]    		=      mask_feature_length
   lowercase__  :   Any    		=      mask_feature_min_masks

   # ctc loss
   lowercase__  :   Dict    		=      ctc_loss_reduction
   lowercase__  :   str    		=      ctc_zero_infinity

   # adapter
   lowercase__  :   Tuple    		=      add_adapter
   lowercase__  :   str    		=      adapter_kernel_size
   lowercase__  :   List[Any]    		=      adapter_stride
   lowercase__  :   Dict    		=      num_adapter_layers
   lowercase__  :   Optional[Any]    		=      output_hidden_size or hidden_size

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

   # XVector-specific parameters. Feel free to ignore for other classes.
   lowercase__  :   Optional[int]    		=      list(_a)
   lowercase__  :   Any    		=      list(_a)
   lowercase__  :   Tuple    		=      list(_a)
   lowercase__  :   Optional[Any]    		=      xvector_output_dim



 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return math.prod(self.conv_stride)








import sys

lowerCamelCase__    :		List[Any]          =   (
    """73167176531330624919225119674426574742355349194934"""
    """96983520312774506326239578318016984801869478851843"""
    """85861560789112949495459501737958331952853208805511"""
    """12540698747158523863050715693290963295227443043557"""
    """66896648950445244523161731856403098711121722383113"""
    """62229893423380308135336276614282806444486645238749"""
    """30358907296290491560440772390713810515859307960866"""
    """70172427121883998797908792274921901699720888093776"""
    """65727333001053367881220235421809751254540594752243"""
    """52584907711670556013604839586446706324415722155397"""
    """53697817977846174064955149290862569321978468622482"""
    """83972241375657056057490261407972968652414535100474"""
    """82166370484403199890008895243450658541227588666881"""
    """16427171479924442928230863465674813919123162824586"""
    """17866458359124566529476545682848912883142607690042"""
    """24219022671055626321111109370544217506941658960408"""
    """07198403850962455444362981230987879927244284909188"""
    """84580156166097919133875499200524063689912560717606"""
    """05886116467109405077541002256983155200055935729725"""
    """71636269561882670428252483600823257530420752963450"""
)

def 		UpperCamelCase	(      lowercase_ = N   )		->	int:


  '''simple docstring'''






  lowercase__  :   int    		=      -sys.maxsize - 1
  for i in range(len(lowercase_   ) - 12   ):
    lowercase__  :   Optional[int]    		=      1
    for j in range(13   ):
      product *= int(n[i + j]   )
    if product > largest_product:
      lowercase__  :   int    		=      product
  return largest_product


if __name__ == "__main__":
     print(f'''{solution() = }''')






