



"""simple docstring"""
def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[str]     			=0

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

   # append each character + "|" in new_string for range(0, length-1)
   for i in input_string[: len(lowercase ) - 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
   SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=0, 0

   # length[i] shows the length of palindromic substring with center i
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=[1 for i in range(len(lowercase ) )]

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

      SCREAMING_SNAKE_CASE_: Dict     			=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:
         SCREAMING_SNAKE_CASE_: Optional[int]     			=j - k + 1  # noqa: E741
         SCREAMING_SNAKE_CASE_: List[str]     			=j + k - 1

      # update max_length and start position
      if max_length < length[j]:
         SCREAMING_SNAKE_CASE_: int     			=length[j]
         SCREAMING_SNAKE_CASE_: Optional[int]     			=j

    # create that string
   SCREAMING_SNAKE_CASE_: 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()








"""simple docstring"""
from typing import TYPE_CHECKING

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


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



