


import numpy as np

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      snake_case__	:
				def __init__(	self   ):




										"""simple docstring"""




										__magic_name__					:				str				=   np.array(_UpperCAmelCase   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__		,			__magic_name__					:				int				=   np.where(letter == self.SQUARE   )
										__magic_name__					:				Union[str, Any]				=   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"""




										__magic_name__					:				List[str]				=   message.lower()
										__magic_name__					:				int				=   message.replace('j'  ,   'i'   )

										__magic_name__					:				Union[str, Any]				=   ''
										for letter_index in range(len(_UpperCAmelCase   )   ):
																if message[letter_index] != " ":
																						__magic_name__					:				Optional[int]				=   self.letter_to_numbers(message[letter_index]   )
																						__magic_name__					:				List[Any]				=   encoded_message + str(numbers[0]   ) + str(numbers[1]   )
																elif message[letter_index] == " ":
																						__magic_name__					:				int				=   encoded_message + ' '

										return encoded_message





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




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

																						__magic_name__					:				Dict				=   self.numbers_to_letter(int(_UpperCAmelCase   )  ,   int(_UpperCAmelCase   )   )
																						__magic_name__					:				Tuple				=   decoded_message + letter
																elif message[numbers_index * 2] == " ":
																						__magic_name__					:				Union[str, Any]				=   decoded_message + ' '

										return decoded_message






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


snake_case__  				=			{
    'configuration_tvlt': ['TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TvltConfig'],
    'feature_extraction_tvlt': ['TvltFeatureExtractor'],
    'processing_tvlt': ['TvltProcessor'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			[
			    'TVLT_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'TvltModel',
			    'TvltForPreTraining',
			    'TvltForAudioVisualClassification',
			    'TvltPreTrainedModel',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__  				=			['TvltImageProcessor']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			snake_case__  				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


