code: 
  
  
  import numpy as np
  
  lowerCAmelCase__ 	=				[
      ['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)    ->       None:
              _A   : Dict       					=				np.array(__lowerCamelCase)
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       np.ndarray:
              _A					,    _A   : Tuple       					=				np.where(letter == self.SQUARE)
              _A   : List[str]       					=				np.concatenate([indexa + 1, indexa + 1])
              return indexes
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase				,		__lowerCamelCase)    ->       str:
              return self.SQUARE[indexa - 1, indexa - 1]
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       str:
              _A   : Tuple       					=				message.lower()
              _A   : int       					=				message.replace("j"				,		"i")
  
              _A   : Any       					=				""
              for letter_index in range(len(__lowerCamelCase)):
                     if message[letter_index] != " ":
                            _A   : Optional[Any]       					=				self.letter_to_numbers(message[letter_index])
                            _A   : Tuple       					=				encoded_message + str(numbers[0]) + str(numbers[1])
                     elif message[letter_index] == " ":
                            _A   : Optional[int]       					=				encoded_message + " "
  
              return encoded_message
  
  
       def 				_lowerCamelCase				(		self				,		__lowerCamelCase)    ->       str:
              _A   : Optional[Any]       					=				message.replace(" "				,		"  ")
              _A   : Optional[int]       					=				""
              for numbers_index in range(int(len(__lowerCamelCase) / 2)):
                     if message[numbers_index * 2] != " ":
                            _A   : Optional[Any]       					=				message[numbers_index * 2]
                            _A   : Optional[int]       					=				message[numbers_index * 2 + 1]
  
                            _A   : List[str]       					=				self.numbers_to_letter(int(__lowerCamelCase)				,		int(__lowerCamelCase))
                            _A   : Optional[int]       					=				decoded_message + letter
                     elif message[numbers_index * 2] == " ":
                            _A   : Union[str, Any]       					=				decoded_message + " "
  
              return decoded_message
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCAmelCase__   	=				{
      'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
  }
  
  try:
      if not is_vision_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				['SegformerFeatureExtractor']
      lowerCAmelCase__   	=				['SegformerImageProcessor']
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'SegformerDecodeHead',
          'SegformerForImageClassification',
          'SegformerForSemanticSegmentation',
          'SegformerLayer',
          'SegformerModel',
          'SegformerPreTrainedModel',
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      lowerCAmelCase__   	=				[
          'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSegformerDecodeHead',
          'TFSegformerForImageClassification',
          'TFSegformerForSemanticSegmentation',
          'TFSegformerModel',
          'TFSegformerPreTrainedModel',
      ]
  
  
  if TYPE_CHECKING:
      from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig
  
      try:
          if not is_vision_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .feature_extraction_segformer import SegformerFeatureExtractor
          from .image_processing_segformer import SegformerImageProcessor
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_segformer import (
              SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              SegformerDecodeHead,
              SegformerForImageClassification,
              SegformerForSemanticSegmentation,
              SegformerLayer,
              SegformerModel,
              SegformerPreTrainedModel,
          )
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_segformer import (
              TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFSegformerDecodeHead,
              TFSegformerForImageClassification,
              TFSegformerForSemanticSegmentation,
              TFSegformerModel,
              TFSegformerPreTrainedModel,
          )
  
  else:
      import sys
  
      lowerCAmelCase__   	=				_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  