
"""simple docstring"""






import itertools
import string
from collections.abc import Generator, Iterable





def __A						(   a_ :							Iterable[str]		,		a_ :							int     )->							Generator[tuple[str, ...], None, None]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Union[str, Any]				= iter(a_     )
		while True:
				SCREAMING_SNAKE_CASE       :     Optional[int]				= tuple(itertools.islice(a_		,		a_     )     )
				if not chunk:
						return
				yield chunk





def __A						(   a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     int				= ''.join([c.upper() for c in dirty if c in string.ascii_letters]     )
		SCREAMING_SNAKE_CASE       :     int				= ''

		if len(a_     ) < 2:
				return dirty

		for i in range(len(a_     ) - 1     ):
				clean += dirty[i]

				if dirty[i] == dirty[i + 1]:
						clean += "X"

		clean += dirty[-1]

		if len(a_     ) & 1:
				clean += "X"

		return clean





def __A						(   a_ :							str     )->							list[str]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= 'ABCDEFGHIKLMNOPQRSTUVWXYZ'
		# we're using a list instead of a '2d' array because it makes the math
		# for setting up the table and doing the actual encoding/decoding simpler
		SCREAMING_SNAKE_CASE       :     Any				= []

		# copy key chars into the table if they are in `alphabet` ignoring duplicates
		for char in key.upper():
				if char not in table and char in alphabet:
						table.append(a_     )

    # fill the rest of the table in with the remaining alphabet chars
		for char in alphabet:
				if char not in table:
						table.append(a_     )

		return table





def __A						(   a_ :							str		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     List[str]				= generate_table(a_     )
		SCREAMING_SNAKE_CASE       :     Optional[Any]				= prepare_input(a_     )
		SCREAMING_SNAKE_CASE       :     Any				= ''

		# https://en.wikipedia.org/wiki/Playfair_cipher#Description
		for chara, chara in chunker(a_		,		2     ):
				SCREAMING_SNAKE_CASE       :     Tuple				= divmod(table.index(a_     )		,		5     )
				SCREAMING_SNAKE_CASE       :     List[Any]				= divmod(table.index(a_     )		,		5     )

				if rowa == rowa:
						ciphertext += table[rowa * 5 + (cola + 1) % 5]
						ciphertext += table[rowa * 5 + (cola + 1) % 5]
				elif cola == cola:
						ciphertext += table[((rowa + 1) % 5) * 5 + cola]
						ciphertext += table[((rowa + 1) % 5) * 5 + cola]
				else:  # rectangle
						ciphertext += table[rowa * 5 + cola]
						ciphertext += table[rowa * 5 + cola]

		return ciphertext





def __A						(   a_ :							str		,		a_ :							str     )->							str:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Optional[int]				= generate_table(a_     )
		SCREAMING_SNAKE_CASE       :     int				= ''

		# https://en.wikipedia.org/wiki/Playfair_cipher#Description
		for chara, chara in chunker(a_		,		2     ):
				SCREAMING_SNAKE_CASE       :     Union[str, Any]				= divmod(table.index(a_     )		,		5     )
				SCREAMING_SNAKE_CASE       :     str				= divmod(table.index(a_     )		,		5     )

				if rowa == rowa:
						plaintext += table[rowa * 5 + (cola - 1) % 5]
						plaintext += table[rowa * 5 + (cola - 1) % 5]
				elif cola == cola:
						plaintext += table[((rowa - 1) % 5) * 5 + cola]
						plaintext += table[((rowa - 1) % 5) * 5 + cola]
				else:  # rectangle
						plaintext += table[rowa * 5 + cola]
						plaintext += table[rowa * 5 + cola]

		return plaintext







"""simple docstring"""






import os
import sys

lowerCamelCase__ :       List[Any]						     =			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,
)


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





@add_start_docstrings(AutoConfig.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Union[str, Any]     )->							Dict:




		'''simple docstring'''






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





@add_start_docstrings(AutoTokenizer.__doc__     )
def __A						(   *a_ :							str		,		**a_ :							Union[str, Any]     )->							Union[str, Any]:




		'''simple docstring'''






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





@add_start_docstrings(AutoModel.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							Dict:




		'''simple docstring'''






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





@add_start_docstrings(AutoModelForCausalLM.__doc__     )
def __A						(   *a_ :							Any		,		**a_ :							Tuple     )->							Dict:




		'''simple docstring'''






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





@add_start_docstrings(AutoModelForMaskedLM.__doc__     )
def __A						(   *a_ :							Dict		,		**a_ :							Optional[Any]     )->							Optional[int]:




		'''simple docstring'''






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





@add_start_docstrings(AutoModelForSequenceClassification.__doc__     )
def __A						(   *a_ :							Optional[int]		,		**a_ :							str     )->							Optional[int]:




		'''simple docstring'''






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





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__     )
def __A						(   *a_ :							List[str]		,		**a_ :							int     )->							List[Any]:




		'''simple docstring'''






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





