





'''simple docstring'''






import numpy as np

A             =			[
    ['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 			lowerCamelCase					:


					def __init__(					self	:  List[str]       ):





											'''simple docstring'''





											_snake_case:   Union[str, Any]  					=		np.array(__snake_case       )


					def    SCREAMING_SNAKE_CASE_		(					self	:  Dict       ,     __snake_case	:  str       ):





											'''simple docstring'''





											_snake_case:   List[Any]  					=		np.where(letter == self.SQUARE       )
											_snake_case:   Optional[Any]  					=		np.concatenate([indexa + 1, indexa + 1]       )
											return indexes


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  int       ,     __snake_case	:  int       ):





											'''simple docstring'''





											_snake_case:   str  					=		self.SQUARE[indexa - 1, indexa - 1]
											return letter


					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ,     __snake_case	:  str       ):





											'''simple docstring'''





											_snake_case:   Dict  					=		message.lower()
											_snake_case:   Optional[int]  					=		message.replace(' '       ,     ''       )
											_snake_case:   Optional[int]  					=		message.replace('j'       ,     'i'       )

											_snake_case:   List[str]  					=		np.empty((2, len(__snake_case       ))       )
											for letter_index in range(len(__snake_case       )       ):
																	_snake_case:   List[Any]  					=		self.letter_to_numbers(message[letter_index]       )

																	_snake_case:   Union[str, Any]  					=		numbers[0]
																	_snake_case:   Tuple  					=		numbers[1]

											_snake_case:   Union[str, Any]  					=		first_step.reshape(2 * len(__snake_case       )       )
											_snake_case:   int  					=		''
											for numbers_index in range(len(__snake_case       )       ):
																	_snake_case:   Tuple  					=		int(second_step[numbers_index * 2]       )
																	_snake_case:   Tuple  					=		int(second_step[(numbers_index * 2) + 1]       )
																	_snake_case:   List[str]  					=		self.numbers_to_letter(__snake_case       ,     __snake_case       )
																	_snake_case:   Any  					=		encoded_message + letter

											return encoded_message




					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  str       ):





											'''simple docstring'''





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

											_snake_case:   Dict  					=		first_step.reshape((2, len(__snake_case       ))       )
											_snake_case:   int  					=		''
											for numbers_index in range(len(__snake_case       )       ):
																	_snake_case:   List[Any]  					=		int(second_step[0, numbers_index]       )
																	_snake_case:   Dict  					=		int(second_step[1, numbers_index]       )
																	_snake_case:   Dict  					=		self.numbers_to_letter(__snake_case       ,     __snake_case       )
																	_snake_case:   Any  					=		decoded_message + letter

											return decoded_message












'''simple docstring'''






A   : List[str]             =			'\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A   : List[str]             =			[{'type': 'code', 'content': INSTALL_CONTENT}]
A   : Dict             =			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}





