




"""simple docstring"""

def 		_snake_case	(   _snake_case				:   str				)       ->   str:






      '''simple docstring'''


      _A     =  0

      # if input_string is "aba" than new_input_string become "a|b|a"
      _A     =  ''
      _A     =  ''

      # append each character + "|" in new_string for range(0, length-1)
      for i in input_string[: len(_snake_case				) - 1]:
            new_input_string += i + "|"
      # append last character
      new_input_string += input_string[-1]

      # we will store the starting and ending of previous furthest ending palindromic
      # substring
      _A  ,       _A     =  0, 0

      # length[i] shows the length of palindromic substring with center i
      _A     =  [1 for i in range(len(_snake_case				)				)]

      # for each character in new_string find corresponding palindromic string
      _A     =  0
      for j in range(len(_snake_case				)				):
            _A     =  1 if j > r else min(length[l + r - j] // 2							,    r - j + 1				)
            while (
                j - k >= 0
                and j + k < len(_snake_case				)
                and new_input_string[k + j] == new_input_string[j - k]
            ):
                  k += 1

            _A     =  2 * k - 1

            # does this string is ending after the previously explored end (that is r) ?
            # if yes the update the new r to the last index of this
            if j + k - 1 > r:
                  _A     =  j - k + 1  # noqa: E741
                  _A     =  j + k - 1

            # update max_length and start position
            if max_length < length[j]:
                  _A     =  length[j]
                  _A     =  j

    # create that string
      _A     =  new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
      for i in s:
            if i != "|":
                  output_string += i

      return output_string


if __name__ == "__main__":
       import doctest

       doctest.testmod()







"""simple docstring"""

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


a							      =     logging.get_logger(__name__)

a							      =     {
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	int     =      '''vit_mae'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			Optional[int]=768       ,				_UpperCAmelCase			:			Tuple=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			Optional[int]=3_072       ,				_UpperCAmelCase			:			Any="gelu"       ,				_UpperCAmelCase			:			Optional[Any]=0.0       ,				_UpperCAmelCase			:			Optional[int]=0.0       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			List[Any]=1E-1_2       ,				_UpperCAmelCase			:			Optional[Any]=224       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=3       ,				_UpperCAmelCase			:			Tuple=True       ,				_UpperCAmelCase			:			int=16       ,				_UpperCAmelCase			:			str=512       ,				_UpperCAmelCase			:			int=8       ,				_UpperCAmelCase			:			List[Any]=2_048       ,				_UpperCAmelCase			:			Optional[Any]=0.75       ,				_UpperCAmelCase			:			List[str]=False       ,				**_UpperCAmelCase			:			Union[str, Any]       ,				):
       super().__init__(**_UpperCAmelCase						)

       _A     =  hidden_size
       _A     =  num_hidden_layers
       _A     =  num_attention_heads
       _A     =  intermediate_size
       _A     =  hidden_act
       _A     =  hidden_dropout_prob
       _A     =  attention_probs_dropout_prob
       _A     =  initializer_range
       _A     =  layer_norm_eps
       _A     =  image_size
       _A     =  patch_size
       _A     =  num_channels
       _A     =  qkv_bias
       _A     =  decoder_num_attention_heads
       _A     =  decoder_hidden_size
       _A     =  decoder_num_hidden_layers
       _A     =  decoder_intermediate_size
       _A     =  mask_ratio
       _A     =  norm_pix_loss

