






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__            =	{
    "configuration_flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertOnnxConfig"],
    "tokenization_flaubert": ["FlaubertTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__            =	[
         "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "FlaubertForMultipleChoice",
         "FlaubertForQuestionAnswering",
         "FlaubertForQuestionAnsweringSimple",
         "FlaubertForSequenceClassification",
         "FlaubertForTokenClassification",
         "FlaubertModel",
         "FlaubertWithLMHeadModel",
         "FlaubertPreTrainedModel",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCAmelCase__            =	[
         "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "TFFlaubertForMultipleChoice",
         "TFFlaubertForQuestionAnsweringSimple",
         "TFFlaubertForSequenceClassification",
         "TFFlaubertForTokenClassification",
         "TFFlaubertModel",
         "TFFlaubertPreTrainedModel",
         "TFFlaubertWithLMHeadModel",
     ]


if TYPE_CHECKING:
     from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
     from .tokenization_flaubert import FlaubertTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_flaubert import (
              FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              FlaubertForMultipleChoice,
              FlaubertForQuestionAnswering,
              FlaubertForQuestionAnsweringSimple,
              FlaubertForSequenceClassification,
              FlaubertForTokenClassification,
              FlaubertModel,
              FlaubertPreTrainedModel,
              FlaubertWithLMHeadModel,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_flaubert import (
              TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFFlaubertForMultipleChoice,
              TFFlaubertForQuestionAnsweringSimple,
              TFFlaubertForSequenceClassification,
              TFFlaubertForTokenClassification,
              TFFlaubertModel,
              TFFlaubertPreTrainedModel,
              TFFlaubertWithLMHeadModel,
          )

else:
     import sys

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














import numpy as np

UpperCAmelCase__            =	[
    ["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 	lowercase_      :






      '''simple docstring'''




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




       """simple docstring"""


       a							=   np.array(__UpperCAmelCase						)







      def    __lowerCAmelCase      (   self   :		List[str]				,      __UpperCAmelCase   :		str						)					->np.ndarray:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		int				,      __UpperCAmelCase   :		int				,      __UpperCAmelCase   :		int						)					->str:




       """simple docstring"""


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







      def    __lowerCAmelCase      (   self   :		Tuple				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


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

       a							=   ''''''
       for letter_index in range(len(__UpperCAmelCase						)						):
        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    __lowerCAmelCase      (   self   :		Any				,      __UpperCAmelCase   :		str						)					->str:




       """simple docstring"""


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

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

       return decoded_message


