





'''simple docstring'''

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


a   	=    logging.get_logger(__name__)

a   	=    {
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}







class __a			( lowercase_      ):
   __UpperCamelCase :			List[Any]						   =						'swinv2'

   __UpperCamelCase :			int						   =						{
       'num_attention_heads': 'num_heads',
       'num_hidden_layers': 'num_layers',
   }





   def __init__(       self					:      Optional[Any]					,lowerCamelCase					:      Dict=224					,lowerCamelCase					:      str=4					,lowerCamelCase					:      Optional[int]=3					,lowerCamelCase					:      Any=96					,lowerCamelCase					:      List[Any]=[2, 2, 6, 2]					,lowerCamelCase					:      int=[3, 6, 12, 24]					,lowerCamelCase					:      Optional[int]=7					,lowerCamelCase					:      int=4.0					,lowerCamelCase					:      List[Any]=True					,lowerCamelCase					:      List[str]=0.0					,lowerCamelCase					:      List[str]=0.0					,lowerCamelCase					:      str=0.1					,lowerCamelCase					:      Any="gelu"					,lowerCamelCase					:      Dict=False					,lowerCamelCase					:      Union[str, Any]=0.02					,lowerCamelCase					:      Optional[Any]=1E-5					,lowerCamelCase					:      Tuple=32					,**lowerCamelCase					:      Union[str, Any]					,):

      '''simple docstring'''

      super().__init__(**UpperCamelCase_     )

      __SCREAMING_SNAKE_CASE   				=							image_size
      __SCREAMING_SNAKE_CASE   				=							patch_size
      __SCREAMING_SNAKE_CASE   				=							num_channels
      __SCREAMING_SNAKE_CASE   				=							embed_dim
      __SCREAMING_SNAKE_CASE   				=							depths
      __SCREAMING_SNAKE_CASE   				=							len(UpperCamelCase_     )
      __SCREAMING_SNAKE_CASE   				=							num_heads
      __SCREAMING_SNAKE_CASE   				=							window_size
      __SCREAMING_SNAKE_CASE   				=							mlp_ratio
      __SCREAMING_SNAKE_CASE   				=							qkv_bias
      __SCREAMING_SNAKE_CASE   				=							hidden_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE   				=							drop_path_rate
      __SCREAMING_SNAKE_CASE   				=							hidden_act
      __SCREAMING_SNAKE_CASE   				=							use_absolute_embeddings
      __SCREAMING_SNAKE_CASE   				=							layer_norm_eps
      __SCREAMING_SNAKE_CASE   				=							initializer_range
      __SCREAMING_SNAKE_CASE   				=							encoder_stride
      # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      __SCREAMING_SNAKE_CASE   				=							int(embed_dim * 2 ** (len(UpperCamelCase_     ) - 1)     )
      __SCREAMING_SNAKE_CASE   				=							(0, 0, 0, 0)













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__)




