




'''simple docstring'''

import numpy as np

a  :				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 						a				:


				def __init__(		self    :					Tuple					):
									snake_case_									=				np.array(lowercase_					)


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


				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					Tuple       ,				lowercase_    :					Optional[int]					):
									snake_case_									=				self.SQUARE[indexa - 1, indexa - 1]
									return letter


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					Any					):
									snake_case_									=				message.lower()
									snake_case_									=				message.replace(''' '''       ,				''''''					)
									snake_case_									=				message.replace('''j'''       ,				'''i'''					)

									snake_case_									=				np.empty((2, len(lowercase_					))					)
									for letter_index in range(len(lowercase_					)					):
														snake_case_									=				self.letter_to_numbers(message[letter_index]					)

														snake_case_									=				numbers[0]
														snake_case_									=				numbers[1]

									snake_case_									=				first_step.reshape(2 * len(lowercase_					)					)
									snake_case_									=				''
									for numbers_index in range(len(lowercase_					)					):
														snake_case_									=				int(second_step[numbers_index * 2]					)
														snake_case_									=				int(second_step[(numbers_index * 2) + 1]					)
														snake_case_									=				self.numbers_to_letter(lowercase_       ,				lowercase_					)
														snake_case_									=				encoded_message + letter

									return encoded_message


				def   A_ (		self    :					Dict       ,				lowercase_    :					Tuple					):
									snake_case_									=				message.lower()
									message.replace(''' '''       ,				''''''					)
									snake_case_									=				np.empty(2 * len(lowercase_					)					)
									for letter_index in range(len(lowercase_					)					):
														snake_case_									=				self.letter_to_numbers(message[letter_index]					)
														snake_case_									=				numbers[0]
														snake_case_									=				numbers[1]

									snake_case_									=				first_step.reshape((2, len(lowercase_					))					)
									snake_case_									=				''
									for numbers_index in range(len(lowercase_					)					):
														snake_case_									=				int(second_step[0, numbers_index]					)
														snake_case_									=				int(second_step[1, numbers_index]					)
														snake_case_									=				self.numbers_to_letter(lowercase_       ,				lowercase_					)
														snake_case_									=				decoded_message + letter

									return decoded_message







"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__     						=						{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase__     						=						[
			    """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """IBertForMaskedLM""",
			    """IBertForMultipleChoice""",
			    """IBertForQuestionAnswering""",
			    """IBertForSequenceClassification""",
			    """IBertForTokenClassification""",
			    """IBertModel""",
			    """IBertPreTrainedModel""",
			]

if TYPE_CHECKING:
			from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_ibert import (
						    IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    IBertForMaskedLM,
						    IBertForMultipleChoice,
						    IBertForQuestionAnswering,
						    IBertForSequenceClassification,
						    IBertForTokenClassification,
						    IBertModel,
						    IBertPreTrainedModel,
						)

else:
			import sys

			lowercase__     						=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)