






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


__a   :							Union[str, Any]						= logging.get_logger(__name__)

__a   :							Dict						= {
    "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    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"vit_mae"




     def __init__(					self							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]=768			,						UpperCamelCase_							:      Dict=12			,						UpperCamelCase_							:      Optional[Any]=12			,						UpperCamelCase_							:      Dict=3_072			,						UpperCamelCase_							:      int="gelu"			,						UpperCamelCase_							:      Any=0.0			,						UpperCamelCase_							:      Any=0.0			,						UpperCamelCase_							:      Optional[Any]=0.02			,						UpperCamelCase_							:      Union[str, Any]=1e-12			,						UpperCamelCase_							:      Optional[int]=224			,						UpperCamelCase_							:      Dict=16			,						UpperCamelCase_							:      Union[str, Any]=3			,						UpperCamelCase_							:      List[str]=True			,						UpperCamelCase_							:      Union[str, Any]=16			,						UpperCamelCase_							:      Tuple=512			,						UpperCamelCase_							:      List[Any]=8			,						UpperCamelCase_							:      str=2_048			,						UpperCamelCase_							:      List[str]=0.75			,						UpperCamelCase_							:      List[str]=False			,						**UpperCamelCase_							:      Dict			,						):


         """simple docstring"""



         super().__init__(**UpperCamelCase_					)

         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						initializer_range
         __A       =						layer_norm_eps
         __A       =						image_size
         __A       =						patch_size
         __A       =						num_channels
         __A       =						qkv_bias
         __A       =						decoder_num_attention_heads
         __A       =						decoder_hidden_size
         __A       =						decoder_num_hidden_layers
         __A       =						decoder_intermediate_size
         __A       =						mask_ratio
         __A       =						norm_pix_loss












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

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




