






"""simple docstring"""






import functools
import operator

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


UpperCAmelCase_   : Optional[int]   			= logging.get_logger(__name__)

UpperCAmelCase_   : List[Any]   			= {
    """asapp/sew-d-tiny-100k""": """https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json""",
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  "sew-d"
      def __init__(						self     :  Dict						,	lowercase_     :  Optional[Any]=32						,	lowercase_     :  List[Any]=768						,	lowercase_     :  int=12						,	lowercase_     :  Dict=12						,	lowercase_     :  Union[str, Any]=3072						,	lowercase_     :  Dict=2						,	lowercase_     :  List[Any]=512						,	lowercase_     :  Union[str, Any]=256						,	lowercase_     :  Optional[int]=True						,	lowercase_     :  List[str]=True						,	lowercase_     :  List[Any]=("p2c", "c2p")						,	lowercase_     :  Optional[int]="layer_norm"						,	lowercase_     :  List[Any]="gelu_python"						,	lowercase_     :  int=0.1						,	lowercase_     :  Optional[int]=0.1						,	lowercase_     :  Optional[int]=0.1						,	lowercase_     :  List[str]=0.0						,	lowercase_     :  Any=0.1						,	lowercase_     :  Dict=0.02						,	lowercase_     :  str=1e-7						,	lowercase_     :  Optional[int]=1e-5						,	lowercase_     :  int="group"						,	lowercase_     :  str="gelu"						,	lowercase_     :  List[str]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)						,	lowercase_     :  int=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)						,	lowercase_     :  List[Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)						,	lowercase_     :  List[Any]=False						,	lowercase_     :  int=128						,	lowercase_     :  List[Any]=16						,	lowercase_     :  Tuple=True						,	lowercase_     :  Any=0.05						,	lowercase_     :  Tuple=10						,	lowercase_     :  List[str]=2						,	lowercase_     :  Any=0.0						,	lowercase_     :  int=10						,	lowercase_     :  Optional[Any]=0						,	lowercase_     :  Optional[Any]="mean"						,	lowercase_     :  List[Any]=False						,	lowercase_     :  int=False						,	lowercase_     :  str=256						,	lowercase_     :  int=0						,	lowercase_     :  str=1						,	lowercase_     :  Any=2						,	**lowercase_     :  Union[str, Any]						,	):




          '''simple docstring'''


          super().__init__(**lowercase_						,	pad_token_id=lowercase_						,	bos_token_id=lowercase_						,	eos_token_id=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		hidden_size
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		feat_extract_norm
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		feat_extract_activation
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  str    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		conv_bias
          SCREAMING_SNAKE_CASE_      :  int    						=		num_conv_pos_embeddings
          SCREAMING_SNAKE_CASE_      :  Dict    						=		num_conv_pos_embedding_groups
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		len(self.conv_dim)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		num_hidden_layers
          SCREAMING_SNAKE_CASE_      :  Dict    						=		intermediate_size
          SCREAMING_SNAKE_CASE_      :  int    						=		squeeze_factor
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		max_position_embeddings
          SCREAMING_SNAKE_CASE_      :  Any    						=		position_buckets
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		share_att_key
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		relative_attention
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		norm_rel_ebd
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		list(lowercase_)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		hidden_act
          SCREAMING_SNAKE_CASE_      :  int    						=		num_attention_heads
          SCREAMING_SNAKE_CASE_      :  Dict    						=		hidden_dropout
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		attention_dropout
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		activation_dropout
          SCREAMING_SNAKE_CASE_      :  str    						=		feat_proj_dropout
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		final_dropout
          SCREAMING_SNAKE_CASE_      :  Any    						=		layer_norm_eps
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		feature_layer_norm_eps
          SCREAMING_SNAKE_CASE_      :  Dict    						=		initializer_range
          SCREAMING_SNAKE_CASE_      :  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
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		apply_spec_augment
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		mask_time_prob
          SCREAMING_SNAKE_CASE_      :  Dict    						=		mask_time_length
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		mask_time_min_masks
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		mask_feature_prob
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		mask_feature_length
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		mask_feature_min_masks

          # ctc loss
          SCREAMING_SNAKE_CASE_      :  int    						=		ctc_loss_reduction
          SCREAMING_SNAKE_CASE_      :  Any    						=		ctc_zero_infinity

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



      @property
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          return functools.reduce(operator.mul						,	self.conv_stride						,	1)






'''simple docstring'''






from math import ceil







def 				UpperCamelCase_			(    A__					:   int = 10_01					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[Any]			=   1

  for i in range(1							,    int(ceil(n / 2.0					)					)					):
    lowerCAmelCase_			:      int			=   2 * i + 1
    lowerCAmelCase_			:      Tuple			=   2 * i
    lowerCAmelCase_			:      Tuple			=   total + 4 * odd**2 - 6 * even

  return total


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution())
  else:
    try:
      __A					:  str											=      int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("Invalid entry - please enter a number")



