
import numpy as np

lowercase__							:							Tuple									=			[
    ["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        SCREAMING_SNAKE_CASE__    :

							"""simple docstring"""






							def __init__(  self			)->   None:





													'''simple docstring'''






													__UpperCamelCase       =			np.array(lowerCAmelCase_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   np.ndarray:





													'''simple docstring'''






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



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			self.SQUARE[indexa - 1, indexa - 1]
													return letter



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






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

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

																			__UpperCamelCase       =			numbers[0]
																			__UpperCamelCase       =			numbers[1]

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

													return encoded_message



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													__UpperCamelCase       =			message.lower()
													message.replace(''' '''     ,	''''''			)
													__UpperCamelCase       =			np.empty(2 * len(lowerCAmelCase_			)			)
													for letter_index in range(len(lowerCAmelCase_			)			):
																			__UpperCamelCase       =			self.letter_to_numbers(message[letter_index]			)
																			__UpperCamelCase       =			numbers[0]
																			__UpperCamelCase       =			numbers[1]

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

													return decoded_message






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase__							:							Dict									=			{"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			["ViTFeatureExtractor"]
				lowercase__							:							str									=			["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[str]									=			[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[Any]									=			[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

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




