



'''simple docstring'''




import numpy as np

lowerCamelCase_							:Union[str, Any]       = [
    ["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							):
				A_     :						Tuple     =					np.array(UpperCAmelCase_							)


	def    _a     (self	,				lowercase							):
				A_     :						Union[str, Any]     =					np.where(letter == self.SQUARE							)
				A_     :						Tuple     =					np.concatenate([indexa + 1, indexa + 1]							)
				return indexes


	def    _a     (self	,				lowercase	,				lowercase							):
				A_     :						Optional[Any]     =					self.SQUARE[indexa - 1, indexa - 1]
				return letter


	def    _a     (self	,				lowercase							):
				A_     :						List[str]     =					message.lower()
				A_     :						Any     =					message.replace(""" """	,				""""""							)
				A_     :						Union[str, Any]     =					message.replace("""j"""	,				"""i"""							)

				A_     :						Optional[int]     =					np.empty((2, len(UpperCAmelCase_							))							)
				for letter_index in range(len(UpperCAmelCase_							)							):
							A_     :						str     =					self.letter_to_numbers(message[letter_index]							)

							A_     :						List[Any]     =					numbers[0]
							A_     :						Tuple     =					numbers[1]

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

				return encoded_message


	def    _a     (self	,				lowercase							):
				A_     :						Dict     =					message.lower()
				message.replace(""" """	,				""""""							)
				A_     :						Dict     =					np.empty(2 * len(UpperCAmelCase_							)							)
				for letter_index in range(len(UpperCAmelCase_							)							):
							A_     :						Optional[Any]     =					self.letter_to_numbers(message[letter_index]							)
							A_     :						Optional[int]     =					numbers[0]
							A_     :						Tuple     =					numbers[1]

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

				return decoded_message




'''simple docstring'''




from typing import Dict

from .base import GenericTensor, Pipeline







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def    _a     (self	,				lowercase=None	,				lowercase=None	,				lowercase=None	,				**lowercase							):
				if tokenize_kwargs is None:
							A_     :						Optional[Any]     =					{}

				if truncation is not None:
							if "truncation" in tokenize_kwargs:
										raise ValueError(
										    """truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)"""							)
							A_     :						str     =					truncation

				A_     :						List[str]     =					tokenize_kwargs

				A_     :						Dict     =					{}
				if return_tensors is not None:
							A_     :						List[Any]     =					return_tensors

				return preprocess_params, {}, postprocess_params


	def    _a     (self	,				lowercase	,				**lowercase							):
				A_     :						Optional[int]     =					self.framework
				A_     :						str     =					self.tokenizer(lowercase	,				return_tensors=lowercase	,				**lowercase							)
				return model_inputs


	def    _a     (self	,				lowercase							):
				A_     :						str     =					self.model(**lowercase							)
				return model_outputs


	def    _a     (self	,				lowercase	,				lowercase=False							):
				# [0] is the first available tensor, logits or last_hidden_state.
				if return_tensors:
							return model_outputs[0]
				if self.framework == "pt":
							return model_outputs[0].tolist()
				elif self.framework == "tf":
							return model_outputs[0].numpy().tolist()


	def __call__(self	,				*lowercase	,				**lowercase							):
				return super().__call__(*lowercase	,				**lowercase							)