from typing import TYPE_CHECKING

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


a_    					=       {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'XLMForMultipleChoice',
       'XLMForQuestionAnswering',
       'XLMForQuestionAnsweringSimple',
       'XLMForSequenceClassification',
       'XLMForTokenClassification',
       'XLMModel',
       'XLMPreTrainedModel',
       'XLMWithLMHeadModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_    					=       [
       'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFXLMForMultipleChoice',
       'TFXLMForQuestionAnsweringSimple',
       'TFXLMForSequenceClassification',
       'TFXLMForTokenClassification',
       'TFXLMMainLayer',
       'TFXLMModel',
       'TFXLMPreTrainedModel',
       'TFXLMWithLMHeadModel',
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

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




from torch import nn





class 		_lowercase						(					nn.Module       ):

 def __init__( self	:							Any   ,      snake_case	:							Dict   ,      snake_case	:							Union[str, Any]			)   ->						Dict:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_					:						List[Any] =					class_size
  UpperCamelCase_					:						List[Any] =					embed_size
  # self.mlp1 = nn.Linear(embed_size, embed_size)
  # self.mlp2 = (nn.Linear(embed_size, class_size))
  UpperCamelCase_					:						int =					nn.Linear(snake_case   ,      snake_case			)


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							Any			)   ->						str:

  """simple docstring"""





  UpperCamelCase_					:						Dict =					self.mlp(snake_case			)
  return logits



