


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


snake_case__      			=    logging.get_logger(__name__)

snake_case__      			=    {
    "facebook/vit-mae-base": "https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json",
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}


class        lowerCAmelCase_			(							lowercase_):
						lowerCamelCase_    =  'vit_mae'




						def __init__(						self    : Union[str, Any] ,  __A    : Optional[int]=768 ,  __A    : Dict=12 ,  __A    : Optional[Any]=12 ,  __A    : Dict=3072 ,  __A    : int="gelu" ,  __A    : Any=0.0 ,  __A    : Any=0.0 ,  __A    : Optional[Any]=0.02 ,  __A    : Union[str, Any]=1E-12 ,  __A    : Optional[int]=224 ,  __A    : Dict=16 ,  __A    : Union[str, Any]=3 ,  __A    : List[str]=True ,  __A    : Union[str, Any]=16 ,  __A    : Tuple=512 ,  __A    : List[Any]=8 ,  __A    : str=2048 ,  __A    : List[str]=0.75 ,  __A    : List[str]=False ,  **__A    : Dict ,  )   ->Optional[Any]:






									"""simple docstring"""






									super().__init__(**UpperCamelCase_    )

									a__			:Optional[int]    	=     hidden_size
									a__			:List[Any]    	=     num_hidden_layers
									a__			:Tuple    	=     num_attention_heads
									a__			:List[Any]    	=     intermediate_size
									a__			:int    	=     hidden_act
									a__			:List[Any]    	=     hidden_dropout_prob
									a__			:Dict    	=     attention_probs_dropout_prob
									a__			:Union[str, Any]    	=     initializer_range
									a__			:List[Any]    	=     layer_norm_eps
									a__			:Any    	=     image_size
									a__			:Tuple    	=     patch_size
									a__			:Dict    	=     num_channels
									a__			:List[str]    	=     qkv_bias
									a__			:Optional[Any]    	=     decoder_num_attention_heads
									a__			:List[Any]    	=     decoder_hidden_size
									a__			:Tuple    	=     decoder_num_hidden_layers
									a__			:Union[str, Any]    	=     decoder_intermediate_size
									a__			:str    	=     mask_ratio
									a__			:List[str]    	=     norm_pix_loss










from typing import TYPE_CHECKING

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


__a   :							Optional[int]						= {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Tuple						= ["XLNetTokenizer"]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							List[str]						= ["XLNetTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Any						= [
      "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "XLNetForMultipleChoice",
      "XLNetForQuestionAnswering",
      "XLNetForQuestionAnsweringSimple",
      "XLNetForSequenceClassification",
      "XLNetForTokenClassification",
      "XLNetLMHeadModel",
      "XLNetModel",
      "XLNetPreTrainedModel",
      "load_tf_weights_in_xlnet",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFXLNetForMultipleChoice",
      "TFXLNetForQuestionAnsweringSimple",
      "TFXLNetForSequenceClassification",
      "TFXLNetForTokenClassification",
      "TFXLNetLMHeadModel",
      "TFXLNetMainLayer",
      "TFXLNetModel",
      "TFXLNetPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




