






'''simple docstring'''



import functools
import operator

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


SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						=	{
    '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 UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			'''sew-d'''






      def __init__( self			:		Optional[Any]     ,      UpperCAmelCase			:		Any=32     ,      UpperCAmelCase			:		str=768     ,      UpperCAmelCase			:		List[Any]=12     ,      UpperCAmelCase			:		List[str]=12     ,      UpperCAmelCase			:		List[Any]=3072     ,      UpperCAmelCase			:		Optional[int]=2     ,      UpperCAmelCase			:		List[str]=512     ,      UpperCAmelCase			:		str=256     ,      UpperCAmelCase			:		Any=True     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		str=("p2c", "c2p")     ,      UpperCAmelCase			:		Optional[Any]="layer_norm"     ,      UpperCAmelCase			:		Dict="gelu_python"     ,      UpperCAmelCase			:		Optional[Any]=0.1     ,      UpperCAmelCase			:		Optional[Any]=0.1     ,      UpperCAmelCase			:		List[Any]=0.1     ,      UpperCAmelCase			:		List[Any]=0.0     ,      UpperCAmelCase			:		Union[str, Any]=0.1     ,      UpperCAmelCase			:		str=0.0_2     ,      UpperCAmelCase			:		List[str]=1e-7     ,      UpperCAmelCase			:		Dict=1e-5     ,      UpperCAmelCase			:		Optional[Any]="group"     ,      UpperCAmelCase			:		str="gelu"     ,      UpperCAmelCase			:		str=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)     ,      UpperCAmelCase			:		Optional[int]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)     ,      UpperCAmelCase			:		int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)     ,      UpperCAmelCase			:		Union[str, Any]=False     ,      UpperCAmelCase			:		Dict=128     ,      UpperCAmelCase			:		Union[str, Any]=16     ,      UpperCAmelCase			:		int=True     ,      UpperCAmelCase			:		List[str]=0.0_5     ,      UpperCAmelCase			:		Tuple=10     ,      UpperCAmelCase			:		Any=2     ,      UpperCAmelCase			:		Dict=0.0     ,      UpperCAmelCase			:		Tuple=10     ,      UpperCAmelCase			:		List[str]=0     ,      UpperCAmelCase			:		Optional[Any]="mean"     ,      UpperCAmelCase			:		Any=False     ,      UpperCAmelCase			:		int=False     ,      UpperCAmelCase			:		List[str]=256     ,      UpperCAmelCase			:		str=0     ,      UpperCAmelCase			:		List[str]=1     ,      UpperCAmelCase			:		Tuple=2     ,      **UpperCAmelCase			:		int     ,      )  ->  str:


       '''simple docstring'''

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

       # ctc loss
       lowercase					:   List[str]				  =ctc_loss_reduction
       lowercase					:   Dict				  =ctc_zero_infinity

       # sequence classification
       lowercase					:   int				  =use_weighted_layer_sum
       lowercase					:   Dict				  =classifier_proj_size






      @property
      def 				A__      ( self			:		int		)  ->  Any:


       '''simple docstring'''

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











'''simple docstring'''



from __future__ import annotations

import math


def 							lowercase_				(    __A :       float					,      __A :       int			)       ->      float:



 """simple docstring"""




 lowercase					:   str				  =u
 for i in range(1					,      __A			):
  lowercase					:   Any				  =temp * (u - i)
 return temp


def 							lowercase_				(    )       ->      None:



 """simple docstring"""




 lowercase					:   List[str]				  =int(input('''enter the numbers of values: '''			)			)
 lowercase					:   list[list[float]]				  =[]
 for _ in range(__A			):
  y.append([]			)
 for i in range(__A			):
  for j in range(__A			):
   y[i].append(__A			)
   lowercase					:   List[Any]				  =0

 print('''enter the values of parameters in a list: '''			)
 lowercase					:   Optional[int]				  =list(map(__A					,      input().split()			)			)

 print('''enter the values of corresponding parameters: '''			)
 for i in range(__A			):
  lowercase					:   str				  =float(input()			)

 lowercase					:   int				  =int(input('''enter the value to interpolate: '''			)			)
 lowercase					:   Union[str, Any]				  =(value - x[0]) / (x[1] - x[0])

 # for calculating forward difference table

 for i in range(1					,      __A			):
  for j in range(n - i			):
   lowercase					:   str				  =y[j + 1][i - 1] - y[j][i - 1]

 lowercase					:   Any				  =y[0][0]
 for i in range(1					,      __A			):
  summ += (ucal(__A					,      __A			) * y[0][i]) / math.factorial(__A			)

 print(F'the value at {value} is {summ}'			)


if __name__ == "__main__":
      main()



