





"""simple docstring"""





import os
import sys

SCREAMING_SNAKE_CASE__					=   os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


SCREAMING_SNAKE_CASE__					=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]


@add_start_docstrings(AutoConfig.__doc__			)
def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						int							,						**SCREAMING_SNAKE_CASE     :						Union[str, Any]			):







      '''simple docstring'''




      return AutoConfig.from_pretrained(*SCREAMING_SNAKE_CASE							,						**SCREAMING_SNAKE_CASE			)


@add_start_docstrings(AutoTokenizer.__doc__			)
def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						List[Any]							,						**SCREAMING_SNAKE_CASE     :						Optional[int]			):







      '''simple docstring'''




      return AutoTokenizer.from_pretrained(*SCREAMING_SNAKE_CASE							,						**SCREAMING_SNAKE_CASE			)


@add_start_docstrings(AutoModel.__doc__			)
def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						Optional[int]							,						**SCREAMING_SNAKE_CASE     :						Any			):







      '''simple docstring'''




      return AutoModel.from_pretrained(*SCREAMING_SNAKE_CASE							,						**SCREAMING_SNAKE_CASE			)


@add_start_docstrings(AutoModelForCausalLM.__doc__			)
def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						Optional[Any]							,						**SCREAMING_SNAKE_CASE     :						List[str]			):







      '''simple docstring'''




      return AutoModelForCausalLM.from_pretrained(*SCREAMING_SNAKE_CASE							,						**SCREAMING_SNAKE_CASE			)


@add_start_docstrings(AutoModelForMaskedLM.__doc__			)
def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						Optional[Any]							,						**SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return AutoModelForMaskedLM.from_pretrained(*SCREAMING_SNAKE_CASE							,						**SCREAMING_SNAKE_CASE			)


@add_start_docstrings(AutoModelForSequenceClassification.__doc__			)
def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						List[Any]							,						**SCREAMING_SNAKE_CASE     :						Optional[Any]			):







      '''simple docstring'''




      return AutoModelForSequenceClassification.from_pretrained(*SCREAMING_SNAKE_CASE							,						**SCREAMING_SNAKE_CASE			)






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__			)
def 			UpperCAmelCase__							(  *SCREAMING_SNAKE_CASE     :						Tuple							,						**SCREAMING_SNAKE_CASE     :						int			):







      '''simple docstring'''




      return AutoModelForQuestionAnswering.from_pretrained(*SCREAMING_SNAKE_CASE							,						**SCREAMING_SNAKE_CASE			)













"""simple docstring"""





from __future__ import annotations


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						str			):







      '''simple docstring'''




      return [ord(SCREAMING_SNAKE_CASE			) - 96 for elem in plain]


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						list[int]			):







      '''simple docstring'''




      return "".join(chr(elem + 96			) for elem in encoded			)


def 			UpperCAmelCase__							(  ):







      '''simple docstring'''




      lowerCAmelCase  						=       encode(input("""-> """			).strip().lower()			)
      print("""Encoded: """							,						SCREAMING_SNAKE_CASE			)
      print("""Decoded:"""							,						decode(SCREAMING_SNAKE_CASE			)			)


if __name__ == "__main__":
       main()






