import functools
import operator

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


UpperCAmelCase					=				logging.get_logger(__name__)

UpperCAmelCase					=				{
    '''facebook/hubert-base-ls960''': '''https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json''',
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}




class    __lowerCAmelCase  (       a_						):



 '''simple docstring'''
 _lowerCamelCase						:	str				    = 'hubert'

 def __init__(			self       :		Optional[Any]       ,			lowerCamelCase       :		str=3_2       ,			lowerCamelCase       :		int=7_6_8       ,			lowerCamelCase       :		Dict=1_2       ,			lowerCamelCase       :		List[Any]=1_2       ,			lowerCamelCase       :		Optional[Any]=3_0_7_2       ,			lowerCamelCase       :		Tuple="gelu"       ,			lowerCamelCase       :		List[str]=0.1       ,			lowerCamelCase       :		List[Any]=0.1       ,			lowerCamelCase       :		str=0.1       ,			lowerCamelCase       :		Tuple=True       ,			lowerCamelCase       :		str=0.0       ,			lowerCamelCase       :		Optional[int]=0.1       ,			lowerCamelCase       :		Optional[int]=0.1       ,			lowerCamelCase       :		Any=0.0_2       ,			lowerCamelCase       :		int=1e-5       ,			lowerCamelCase       :		Tuple="group"       ,			lowerCamelCase       :		List[str]="gelu"       ,			lowerCamelCase       :		Optional[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2)       ,			lowerCamelCase       :		Any=(5, 2, 2, 2, 2, 2, 2)       ,			lowerCamelCase       :		Optional[int]=(1_0, 3, 3, 3, 3, 2, 2)       ,			lowerCamelCase       :		Optional[int]=False       ,			lowerCamelCase       :		str=1_2_8       ,			lowerCamelCase       :		Optional[Any]=1_6       ,			lowerCamelCase       :		Tuple=False       ,			lowerCamelCase       :		Dict=True       ,			lowerCamelCase       :		Any=0.0_5       ,			lowerCamelCase       :		List[str]=1_0       ,			lowerCamelCase       :		str=2       ,			lowerCamelCase       :		Tuple=0.0       ,			lowerCamelCase       :		Optional[int]=1_0       ,			lowerCamelCase       :		Dict=0       ,			lowerCamelCase       :		Optional[int]="sum"       ,			lowerCamelCase       :		List[str]=False       ,			lowerCamelCase       :		List[str]=False       ,			lowerCamelCase       :		int=2_5_6       ,			lowerCamelCase       :		Union[str, Any]=0       ,			lowerCamelCase       :		List[str]=1       ,			lowerCamelCase       :		Dict=2       ,			**lowerCamelCase       :		Any       ,			) ->							Any:
        super().__init__(**lowerCamelCase       ,			pad_token_id=lowerCamelCase       ,			bos_token_id=lowerCamelCase       ,			eos_token_id=lowerCamelCase  )
        _UpperCamelCase     :       Union[str, Any]  						=  hidden_size
        _UpperCamelCase     :       Optional[int]  						=  feat_extract_norm
        _UpperCamelCase     :       str  						=  feat_extract_activation
        _UpperCamelCase     :       List[Any]  						=  list(lowerCamelCase  )
        _UpperCamelCase     :       Optional[Any]  						=  list(lowerCamelCase  )
        _UpperCamelCase     :       str  						=  list(lowerCamelCase  )
        _UpperCamelCase     :       Dict  						=  conv_bias
        _UpperCamelCase     :       int  						=  num_conv_pos_embeddings
        _UpperCamelCase     :       Dict  						=  num_conv_pos_embedding_groups
        _UpperCamelCase     :       Dict  						=  len(self.conv_dim  )
        _UpperCamelCase     :       List[Any]  						=  num_hidden_layers
        _UpperCamelCase     :       Tuple  						=  intermediate_size
        _UpperCamelCase     :       Optional[Any]  						=  hidden_act
        _UpperCamelCase     :       List[str]  						=  num_attention_heads
        _UpperCamelCase     :       int  						=  hidden_dropout
        _UpperCamelCase     :       Optional[Any]  						=  attention_dropout
        _UpperCamelCase     :       Union[str, Any]  						=  activation_dropout
        _UpperCamelCase     :       Optional[Any]  						=  feat_proj_layer_norm
        _UpperCamelCase     :       Optional[Any]  						=  feat_proj_dropout
        _UpperCamelCase     :       List[Any]  						=  final_dropout
        _UpperCamelCase     :       Any  						=  layerdrop
        _UpperCamelCase     :       List[str]  						=  layer_norm_eps
        _UpperCamelCase     :       Tuple  						=  initializer_range
        _UpperCamelCase     :       str  						=  vocab_size
        _UpperCamelCase     :       Dict  						=  do_stable_layer_norm
        _UpperCamelCase     :       Dict  						=  use_weighted_layer_sum
        _UpperCamelCase     :       List[str]  						=  classifier_proj_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)`, 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
        _UpperCamelCase     :       Any  						=  apply_spec_augment
        _UpperCamelCase     :       List[Any]  						=  mask_time_prob
        _UpperCamelCase     :       Tuple  						=  mask_time_length
        _UpperCamelCase     :       Union[str, Any]  						=  mask_time_min_masks
        _UpperCamelCase     :       str  						=  mask_feature_prob
        _UpperCamelCase     :       List[Any]  						=  mask_feature_length
        _UpperCamelCase     :       Union[str, Any]  						=  mask_feature_min_masks

        # ctc loss
        _UpperCamelCase     :       Any  						=  ctc_loss_reduction
        _UpperCamelCase     :       Any  						=  ctc_zero_infinity

 @property
 def 					__snake_case					(			self       :		Optional[Any]  ) ->							Tuple:
        return functools.reduce(operator.mul       ,			self.conv_stride       ,			1  )




UpperCAmelCase					=				[chr(i) for i in range(32, 1_26)]
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(range(len(alphabets)))
UpperCAmelCase					=				list(reversed(range(len(alphabets))))
UpperCAmelCase					=				[]
UpperCAmelCase					=				UpperCAmelCase					=				UpperCAmelCase					=				0


def SCREAMING_SNAKE_CASE				(     ):
       global gear_one_pos
       global gear_two_pos
       global gear_three_pos
       _UpperCamelCase     :       Any  						=  gear_one[0]
       gear_one.append(lowercase_				)
       del gear_one[0]
       gear_one_pos += 1
       if gear_one_pos % int(len(lowercase_				)				) == 0:
              _UpperCamelCase     :       str  						=  gear_two[0]
              gear_two.append(lowercase_				)
              del gear_two[0]
              gear_two_pos += 1
              if gear_two_pos % int(len(lowercase_				)				) == 0:
                     _UpperCamelCase     :       int  						=  gear_three[0]
                     gear_three.append(lowercase_				)
                     del gear_three[0]
                     gear_three_pos += 1


def SCREAMING_SNAKE_CASE				(     lowercase_:	List[Any]				):
       _UpperCamelCase     :       Dict  						=  alphabets.index(lowercase_				)
       _UpperCamelCase     :       Union[str, Any]  						=  gear_one[target]
       _UpperCamelCase     :       Any  						=  gear_two[target]
       _UpperCamelCase     :       Tuple  						=  gear_three[target]
       _UpperCamelCase     :       Tuple  						=  reflector[target]
       _UpperCamelCase     :       Any  						=  gear_three.index(lowercase_				)
       _UpperCamelCase     :       Any  						=  gear_two.index(lowercase_				)
       _UpperCamelCase     :       str  						=  gear_one.index(lowercase_				)
       code.append(alphabets[target]				)
       rotator()


if __name__ == "__main__":
 UpperCAmelCase					=				list(input('''Type your message:\n'''))
 while True:
  try:
   UpperCAmelCase					=				int(input('''Please set token:(must be only digits)\n'''))
   break
  except Exception as error:
   print(error)
 for _ in range(token):
  rotator()
 for j in decode:
  engine(j)
 print('''\n''' + ''''''.join(code))
 print(
     f'''\nYour Token is {token} please write it down.\nIf you want to decode '''
     '''this message again you should input same digits as token!'''
 )



