






from typing import TYPE_CHECKING

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


lowerCamelCase     :							Union[str, Any]    ={
    '''configuration_xlm''': ['''XLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLMConfig''', '''XLMOnnxConfig'''],
    '''tokenization_xlm''': ['''XLMTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase     :							Any    =[
     '''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:
 lowerCamelCase     :							List[str]    =[
     '''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

 lowerCamelCase     :							List[str]    =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase     :							Dict    =logging.get_logger(__name__)





class       __a  (   A__	):
 _lowerCAmelCase	:	Optional[int]				=			'''timm_backbone'''







 def __init__(		self :       Dict							,       SCREAMING_SNAKE_CASE :       Dict=None							,       SCREAMING_SNAKE_CASE :       List[Any]=3							,       SCREAMING_SNAKE_CASE :       List[Any]=True							,       SCREAMING_SNAKE_CASE :       Dict=True							,       SCREAMING_SNAKE_CASE :       Union[str, Any]=None							,       **SCREAMING_SNAKE_CASE :       int							,       ):


      '''simple docstring'''






      super().__init__(**SCREAMING_SNAKE_CASE						)
      UpperCamelCase__      :       Union[str, Any]	    =  backbone
      UpperCamelCase__      :       Dict	    =  num_channels
      UpperCamelCase__      :       str	    =  features_only
      UpperCamelCase__      :       Dict	    =  use_pretrained_backbone
      UpperCamelCase__      :       Tuple	    =  True
      UpperCamelCase__      :       List[Any]	    =  out_indices if out_indices is not None else (-1,)