






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					:





	"""simple docstring"""






	def __init__(				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     np.array(UpperCAmelCase_       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     np.where(letter == self.SQUARE       )
							_UpperCAmelCase							   =     np.concatenate([indexa + 1, indexa + 1]       )
							return indexes

	def 			UpperCamelCase       (				self			,       UpperCAmelCase			,       UpperCAmelCase       ):
							"""simple docstring"""







							return self.SQUARE[indexa - 1, indexa - 1]

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     message.lower()
							_UpperCAmelCase							   =     message.replace('j'			,       'i'       )

							_UpperCAmelCase							   =     ""
							for letter_index in range(len(UpperCAmelCase_       )       ):
													if message[letter_index] != " ":
																			_UpperCAmelCase							   =     self.letter_to_numbers(message[letter_index]       )
																			_UpperCAmelCase							   =     encoded_message + str(numbers[0]       ) + str(numbers[1]       )
													elif message[letter_index] == " ":
																			_UpperCAmelCase							   =     encoded_message + " "

							return encoded_message

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     message.replace(' '			,       '  '       )
							_UpperCAmelCase							   =     ""
							for numbers_index in range(int(len(UpperCAmelCase_       ) / 2       )       ):
													if message[numbers_index * 2] != " ":
																			_UpperCAmelCase							   =     message[numbers_index * 2]
																			_UpperCAmelCase							   =     message[numbers_index * 2 + 1]

																			_UpperCAmelCase							   =     self.numbers_to_letter(int(UpperCAmelCase_       )			,       int(UpperCAmelCase_       )       )
																			_UpperCAmelCase							   =     decoded_message + letter
													elif message[numbers_index * 2] == " ":
																			_UpperCAmelCase							   =     decoded_message + " "

							return decoded_message







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__      :   Any      	=			{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   Optional[int]      	=			["LlamaTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			["LlamaTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__      :   int      	=			[
			    "OpenLlamaForCausalLM",
			    "OpenLlamaModel",
			    "OpenLlamaPreTrainedModel",
			    "OpenLlamaForSequenceClassification",
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__      :   Dict      	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




