
"""simple docstring"""







def       A_      (       lowercase   )  ->				str:


     """simple docstring"""







     UpperCAmelCase_    :     Any            =	0

     # if input_string is "aba" than new_input_string become "a|b|a"
     UpperCAmelCase_    :     Union[str, Any]            =	""""""
     UpperCAmelCase_    :     List[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
     UpperCAmelCase_       ,UpperCAmelCase_    :     Dict            =	0, 0

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

     # for each character in new_string find corresponding palindromic string
     UpperCAmelCase_    :     Optional[Any]            =	0
     for j in range(len(lowercase   )   ):
          UpperCAmelCase_    :     str            =	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

          UpperCAmelCase_    :     Union[str, 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:
               UpperCAmelCase_    :     List[Any]            =	j - k + 1  # noqa: E741
               UpperCAmelCase_    :     Union[str, Any]            =	j + k - 1

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

    # create that string
     UpperCAmelCase_    :     List[str]            =	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 . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)

