



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


SCREAMING_SNAKE_CASE_:Any             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:str             =						{
    """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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Dict					=      "vit_mae"






    def __init__(       self,		lowerCamelCase__=768,		lowerCamelCase__=12,		lowerCamelCase__=12,		lowerCamelCase__=3072,		lowerCamelCase__="gelu",		lowerCamelCase__=0.0,		lowerCamelCase__=0.0,		lowerCamelCase__=0.02,		lowerCamelCase__=1e-12,		lowerCamelCase__=224,		lowerCamelCase__=16,		lowerCamelCase__=3,		lowerCamelCase__=True,		lowerCamelCase__=16,		lowerCamelCase__=512,		lowerCamelCase__=8,		lowerCamelCase__=2048,		lowerCamelCase__=0.75,		lowerCamelCase__=False,		**lowerCamelCase__,		):
        super().__init__(**lowerCamelCase__		)

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







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_:str             =						{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Union[str, Any]             =						[
         """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """AdaptiveEmbedding""",
         """TransfoXLForSequenceClassification""",
         """TransfoXLLMHeadModel""",
         """TransfoXLModel""",
         """TransfoXLPreTrainedModel""",
         """load_tf_weights_in_transfo_xl""",
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_:Any             =						[
         """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """TFAdaptiveEmbedding""",
         """TFTransfoXLForSequenceClassification""",
         """TFTransfoXLLMHeadModel""",
         """TFTransfoXLMainLayer""",
         """TFTransfoXLModel""",
         """TFTransfoXLPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
     from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_transfo_xl import (
              TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              AdaptiveEmbedding,
              TransfoXLForSequenceClassification,
              TransfoXLLMHeadModel,
              TransfoXLModel,
              TransfoXLPreTrainedModel,
              load_tf_weights_in_transfo_xl,
          )

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_transfo_xl import (
              TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFAdaptiveEmbedding,
              TFTransfoXLForSequenceClassification,
              TFTransfoXLLMHeadModel,
              TFTransfoXLMainLayer,
              TFTransfoXLModel,
              TFTransfoXLPreTrainedModel,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_:Dict             =						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


