




"""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       UpperCAmelCase_    :




    def __init__(       self		:			Optional[Any] ) ->				None:




     '''simple docstring'''

     A__			=							np.array(snake_case_ )




    def 				__magic_name__				(       self		:			Union[str, Any]      ,					snake_case_		:			str ) ->				np.ndarray:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			Dict      ,					snake_case_		:			int      ,					snake_case_		:			int ) ->				str:




     '''simple docstring'''

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




    def 				__magic_name__				(       self		:			Any      ,					snake_case_		:			str ) ->				str:




     '''simple docstring'''

     A__			=							message.lower()
     A__			=							message.replace("j"      ,					"i" )

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

     return encoded_message




    def 				__magic_name__				(       self		:			Any      ,					snake_case_		:			str ) ->				str:




     '''simple docstring'''

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

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

     return decoded_message











"""simple docstring"""



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							=			{
    "configuration_dpr": ["DPR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DPRConfig"],
    "tokenization_dpr": [
        "DPRContextEncoderTokenizer",
        "DPRQuestionEncoderTokenizer",
        "DPRReaderOutput",
        "DPRReaderTokenizer",
    ],
}


try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPRContextEncoderTokenizerFast",
          "DPRQuestionEncoderTokenizerFast",
          "DPRReaderTokenizerFast",
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "DPRContextEncoder",
          "DPRPretrainedContextEncoder",
          "DPRPreTrainedModel",
          "DPRPretrainedQuestionEncoder",
          "DPRPretrainedReader",
          "DPRQuestionEncoder",
          "DPRReader",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE							=			[
          "TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFDPRContextEncoder",
          "TFDPRPretrainedContextEncoder",
          "TFDPRPretrainedQuestionEncoder",
          "TFDPRPretrainedReader",
          "TFDPRQuestionEncoder",
          "TFDPRReader",
      ]


if TYPE_CHECKING:
      from .configuration_dpr import DPR_PRETRAINED_CONFIG_ARCHIVE_MAP, DPRConfig
      from .tokenization_dpr import (
          DPRContextEncoderTokenizer,
          DPRQuestionEncoderTokenizer,
          DPRReaderOutput,
          DPRReaderTokenizer,
      )

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_dpr_fast import (
                DPRContextEncoderTokenizerFast,
                DPRQuestionEncoderTokenizerFast,
                DPRReaderTokenizerFast,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dpr import (
                DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                DPRContextEncoder,
                DPRPretrainedContextEncoder,
                DPRPreTrainedModel,
                DPRPretrainedQuestionEncoder,
                DPRPretrainedReader,
                DPRQuestionEncoder,
                DPRReader,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_dpr import (
                TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFDPRContextEncoder,
                TFDPRPretrainedContextEncoder,
                TFDPRPretrainedQuestionEncoder,
                TFDPRPretrainedReader,
                TFDPRQuestionEncoder,
                TFDPRReader,
            )

else:
      import sys

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





