



"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCAmelCase__      =    {
    '''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
    '''tokenization_canine''': ['''CanineTokenizer'''],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCAmelCase__      =    [
       '''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''CanineForMultipleChoice''',
       '''CanineForQuestionAnswering''',
       '''CanineForSequenceClassification''',
       '''CanineForTokenClassification''',
       '''CanineLayer''',
       '''CanineModel''',
       '''CaninePreTrainedModel''',
       '''load_tf_weights_in_canine''',
   ]


if TYPE_CHECKING:
   from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
   from .tokenization_canine import CanineTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_canine import (
          CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
          CanineForMultipleChoice,
          CanineForQuestionAnswering,
          CanineForSequenceClassification,
          CanineForTokenClassification,
          CanineLayer,
          CanineModel,
          CaninePreTrainedModel,
          load_tf_weights_in_canine,
      )


else:
   import sys

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







"""simple docstring"""






from torch import nn






class 	__snake_case	(     nn.Module):




  def __init__(			self					:						Optional[Any]						, __lowerCAmelCase					:						Dict						, __lowerCAmelCase					:						str		):






         """simple docstring"""






         super().__init__()
         _lowerCamelCase						:    List[str]         =			class_size
         _lowerCamelCase						:    List[str]         =			embed_size
         # self.mlp1 = nn.Linear(embed_size, embed_size)
         # self.mlp2 = (nn.Linear(embed_size, class_size))
         _lowerCamelCase						:    Optional[int]         =			nn.Linear(__lowerCAmelCase						, __lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						List[Any]						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			self.mlp(__lowerCAmelCase		)
         return logits


