"""simple docstring"""
import numpy as np

SCREAMING_SNAKE_CASE__       =	[
    ["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"""

										snake_case             =      np.array(lowerCAmelCase     )

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

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

				def 		snake_case			(					self		,     lowerCAmelCase		,     lowerCAmelCase     ):






										"""simple docstring"""

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

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

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

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

										return encoded_message

				def 		snake_case			(					self		,     lowerCAmelCase     ):






										"""simple docstring"""

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

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


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

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

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

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "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

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


