


def 		lowercase_(  SCREAMING_SNAKE_CASE_	):


  '''simple docstring'''

  lowerCamelCase :					int    =				0

  # if input_string is "aba" than new_input_string become "a|b|a"
  lowerCamelCase :					Dict    =				""
  lowerCamelCase :					Optional[Any]    =				""

  # append each character + "|" in new_string for range(0, length-1)
  for i in input_string[: len(SCREAMING_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
  lowerCamelCase    ,	lowerCamelCase :					Tuple    =				0, 0

  # length[i] shows the length of palindromic substring with center i
  lowerCamelCase :					Union[str, Any]    =				[1 for i in range(len(SCREAMING_SNAKE_CASE_	)	)]

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

    lowerCamelCase :					Optional[Any]    =				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:
      lowerCamelCase :					Optional[Any]    =				j - k + 1  # noqa: E741
      lowerCamelCase :					List[str]    =				j + k - 1

    # update max_length and start position
    if max_length < length[j]:
      lowerCamelCase :					int    =				length[j]
      lowerCamelCase :					List[Any]    =				j

    # create that string
  lowerCamelCase :					Dict    =				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()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case             =     {
    '''configuration_squeezebert''': [
        '''SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SqueezeBertConfig''',
        '''SqueezeBertOnnxConfig''',
    ],
    '''tokenization_squeezebert''': ['''SqueezeBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     ['''SqueezeBertTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _snake_case             =     [
       '''SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''SqueezeBertForMaskedLM''',
       '''SqueezeBertForMultipleChoice''',
       '''SqueezeBertForQuestionAnswering''',
       '''SqueezeBertForSequenceClassification''',
       '''SqueezeBertForTokenClassification''',
       '''SqueezeBertModel''',
       '''SqueezeBertModule''',
       '''SqueezeBertPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_squeezebert import (
       SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       SqueezeBertConfig,
       SqueezeBertOnnxConfig,
   )
   from .tokenization_squeezebert import SqueezeBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_squeezebert import (
          SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          SqueezeBertForMaskedLM,
          SqueezeBertForMultipleChoice,
          SqueezeBertForQuestionAnswering,
          SqueezeBertForSequenceClassification,
          SqueezeBertForTokenClassification,
          SqueezeBertModel,
          SqueezeBertModule,
          SqueezeBertPreTrainedModel,
      )

else:
   import sys

   _snake_case             =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



