
"""simple docstring"""







import numpy as np

lowercase_         =       [
    ["a", "b", "c", "d", "e"],
    ["f", "g", "h", "i", "k"],
    ["l", "m", "n", "o", "p"],
    ["q", "r", "s", "t", "u"],
    ["v", "w", "x", "y", "z"],
]



class 		UpperCAmelCase_						:
 """simple docstring"""




 def __init__(			self							:			Dict			)->       None:



      """simple docstring"""

      UpperCAmelCase_    :     Union[str, Any]            =	np.array(a_			)




 def 	a   (			self							:			Tuple  , a_							:			str			)->       np.ndarray:



      """simple docstring"""

      UpperCAmelCase_       ,UpperCAmelCase_    :     Tuple            =	np.where(letter == self.SQUARE			)
      UpperCAmelCase_    :     int            =	np.concatenate([indexa + 1, indexa + 1]			)
      return indexes




 def 	a   (			self							:			str  , a_							:			int  , a_							:			int			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     Any            =	self.SQUARE[indexa - 1, indexa - 1]
      return letter




 def 	a   (			self							:			Any  , a_							:			str			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[Any]            =	message.lower()
      UpperCAmelCase_    :     str            =	message.replace(""" """  , """"""			)
      UpperCAmelCase_    :     Optional[int]            =	message.replace("""j"""  , """i"""			)

      UpperCAmelCase_    :     Dict            =	np.empty((2, len(a_			))			)
      for letter_index in range(len(a_			)			):
           UpperCAmelCase_    :     Union[str, Any]            =	self.letter_to_numbers(message[letter_index]			)

           UpperCAmelCase_    :     str            =	numbers[0]
           UpperCAmelCase_    :     Dict            =	numbers[1]

      UpperCAmelCase_    :     int            =	first_step.reshape(2 * len(a_			)			)
      UpperCAmelCase_    :     Union[str, Any]            =	""""""
      for numbers_index in range(len(a_			)			):
           UpperCAmelCase_    :     List[Any]            =	int(second_step[numbers_index * 2]			)
           UpperCAmelCase_    :     Union[str, Any]            =	int(second_step[(numbers_index * 2) + 1]			)
           UpperCAmelCase_    :     Dict            =	self.numbers_to_letter(a_  , a_			)
           UpperCAmelCase_    :     Union[str, Any]            =	encoded_message + letter

      return encoded_message






 def 	a   (			self							:			str  , a_							:			str			)->       str:



      """simple docstring"""

      UpperCAmelCase_    :     Optional[int]            =	message.lower()
      message.replace(""" """  , """"""			)
      UpperCAmelCase_    :     str            =	np.empty(2 * len(a_			)			)
      for letter_index in range(len(a_			)			):
           UpperCAmelCase_    :     Optional[int]            =	self.letter_to_numbers(message[letter_index]			)
           UpperCAmelCase_    :     Tuple            =	numbers[0]
           UpperCAmelCase_    :     Union[str, Any]            =	numbers[1]

      UpperCAmelCase_    :     Any            =	first_step.reshape((2, len(a_			))			)
      UpperCAmelCase_    :     Union[str, Any]            =	""""""
      for numbers_index in range(len(a_			)			):
           UpperCAmelCase_    :     Dict            =	int(second_step[0, numbers_index]			)
           UpperCAmelCase_    :     List[str]            =	int(second_step[1, numbers_index]			)
           UpperCAmelCase_    :     str            =	self.numbers_to_letter(a_  , a_			)
           UpperCAmelCase_    :     List[Any]            =	decoded_message + letter

      return decoded_message



"""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,
)

