
"""simple docstring"""




import functools
import operator

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


A__ :       List[str]				= logging.get_logger(__name__)

A__ :       str				= {
    'asapp/sew-tiny-100k': 'https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json',
    # See all SEW models at https://huggingface.co/models?filter=sew
}


class       __magic_name__							(							SCREAMING_SNAKE_CASE__			):
       UpperCamelCase_ =		'''sew'''
       def __init__(  self			,					A_=32			,					A_=768			,					A_=12			,					A_=12			,					A_=3072			,					A_=2			,					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_="group"			,					A_="gelu"			,					A_=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)			,					A_=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)			,					A_=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)			,					A_=False			,					A_=128			,					A_=16			,					A_=True			,					A_=0.05			,					A_=10			,					A_=2			,					A_=0.0			,					A_=10			,					A_=0			,					A_="mean"			,					A_=False			,					A_=False			,					A_=256			,					A_=0			,					A_=1			,					A_=2			,					**A_			,					)  ->						Dict:





         """simple docstring"""




         super().__init__(**A_			,					pad_token_id=A_			,					bos_token_id=A_			,					eos_token_id=A_							)
         _lowercase:      int   			=  hidden_size
         _lowercase:      Union[str, Any]   			=  feat_extract_norm
         _lowercase:      Optional[Any]   			=  feat_extract_activation
         _lowercase:      Optional[int]   			=  list(A_							)
         _lowercase:      Dict   			=  list(A_							)
         _lowercase:      Any   			=  list(A_							)
         _lowercase:      Optional[int]   			=  conv_bias
         _lowercase:      List[str]   			=  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:      Dict   			=  squeeze_factor
         _lowercase:      str   			=  hidden_act
         _lowercase:      List[Any]   			=  num_attention_heads
         _lowercase:      List[str]   			=  hidden_dropout
         _lowercase:      Dict   			=  attention_dropout
         _lowercase:      Union[str, Any]   			=  activation_dropout
         _lowercase:      int   			=  feat_proj_dropout
         _lowercase:      List[Any]   			=  final_dropout
         _lowercase:      Union[str, Any]   			=  layerdrop
         _lowercase:      str   			=  layer_norm_eps
         _lowercase:      Union[str, Any]   			=  initializer_range
         _lowercase:      List[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
         _lowercase:      int   			=  apply_spec_augment
         _lowercase:      Union[str, Any]   			=  mask_time_prob
         _lowercase:      Union[str, Any]   			=  mask_time_length
         _lowercase:      Any   			=  mask_time_min_masks
         _lowercase:      str   			=  mask_feature_prob
         _lowercase:      Dict   			=  mask_feature_length
         _lowercase:      Union[str, Any]   			=  mask_feature_min_masks

         # ctc loss
         _lowercase:      Any   			=  ctc_loss_reduction
         _lowercase:      List[str]   			=  ctc_zero_infinity

         # sequence classification
         _lowercase:      int   			=  use_weighted_layer_sum
         _lowercase:      Optional[int]   			=  classifier_proj_size



       @property
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




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


"""simple docstring"""




def 			_lowerCAmelCase		(		_UpperCamelCase    ):






  """simple docstring"""



  stooge(_UpperCamelCase			,					0			,					len(_UpperCamelCase    ) - 1    )
  return arr




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase			,					_UpperCamelCase    ):






  """simple docstring"""



  if i >= h:
    return

  # If first element is smaller than the last then swap them
  if arr[i] > arr[h]:
    _lowercase			,					_lowercase:      Optional[Any]   			=  arr[h], arr[i]

  # If there are more than 2 elements in the array
  if h - i + 1 > 2:
    _lowercase:      Tuple   			=  (int)((h - i + 1) / 3    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )

    # Recursively sort last 2/3 elements
    stooge(_UpperCamelCase			,					i + t			,					(_UpperCamelCase)    )

    # Recursively sort first 2/3 elements
    stooge(_UpperCamelCase			,					_UpperCamelCase			,					(h - t)    )


if __name__ == "__main__":
  A__ :       Dict				= input('Enter numbers separated by a comma:\n').strip()
  A__ :       Optional[Any]				= [int(item) for item in user_input.split(',')]
  print(stooge_sort(unsorted))
