






"""simple docstring"""


from typing import TYPE_CHECKING

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


_UpperCamelCase :    Dict      =			{
    """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:
   _UpperCamelCase :    Tuple      =			[
       """DPRContextEncoderTokenizerFast""",
       """DPRQuestionEncoderTokenizerFast""",
       """DPRReaderTokenizerFast""",
   ]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase :    List[str]      =			[
       """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:
   _UpperCamelCase :    Union[str, Any]      =			[
       """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

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













"""simple docstring"""


import numpy as np

_UpperCamelCase :    int      =			[
    ["""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 __a :



  def __init__(self	:		Any )		->				None:
   __A						:							Any      =						np.array(lowercase_ )



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				np.ndarray:
   __A     ,       __A						:							Any      =						np.where(letter == self.SQUARE )
   __A						:							Tuple      =						np.concatenate([indexa + 1, indexa + 1] )
   return indexes



  def     A_					(self	:		Tuple					,      lowercase_	:		int					,      lowercase_	:		int )		->				str:
   return self.SQUARE[indexa - 1, indexa - 1]



  def     A_					(self	:		Union[str, Any]					,      lowercase_	:		str )		->				str:
   __A						:							Any      =						message.lower()
   __A						:							Optional[Any]      =						message.replace('j'					,      'i' )

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

   return encoded_message



  def     A_					(self	:		Dict					,      lowercase_	:		str )		->				str:
   __A						:							List[str]      =						message.replace(' '					,      '  ' )
   __A						:							List[Any]      =						''
   for numbers_index in range(int(len(lowercase_ ) / 2 ) ):
    if message[numbers_index * 2] != " ":
     __A						:							Any      =						message[numbers_index * 2]
     __A						:							Tuple      =						message[numbers_index * 2 + 1]

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

   return decoded_message





