






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__a   :							Optional[Any]						= logging.get_logger(__name__)

__a   :							Any						= {
    "facebook/convnextv2-tiny-1k-224": "https://huggingface.co/facebook/convnextv2-tiny-1k-224/resolve/main/config.json",
}


class    __lowercase   (						lowercase_      ,   lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					"convnextv2"




     def __init__(					self							:      Tuple			,						UpperCamelCase_							:      Dict=3			,						UpperCamelCase_							:      Optional[int]=4			,						UpperCamelCase_							:      Tuple=4			,						UpperCamelCase_							:      Any=None			,						UpperCamelCase_							:      Any=None			,						UpperCamelCase_							:      List[str]="gelu"			,						UpperCamelCase_							:      Tuple=0.02			,						UpperCamelCase_							:      Any=1e-12			,						UpperCamelCase_							:      Optional[int]=0.0			,						UpperCamelCase_							:      Optional[int]=224			,						UpperCamelCase_							:      Optional[int]=None			,						UpperCamelCase_							:      Optional[int]=None			,						**UpperCamelCase_							:      List[Any]			,						):


         """simple docstring"""



         super().__init__(**UpperCamelCase_					)

         __A       =						num_channels
         __A       =						patch_size
         __A       =						num_stages
         __A       =						[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
         __A       =						[3, 3, 9, 3] if depths is None else depths
         __A       =						hidden_act
         __A       =						initializer_range
         __A       =						layer_norm_eps
         __A       =						drop_path_rate
         __A       =						image_size
         __A       =						["""stem"""] + [F"stage{idx}" for idx in range(1			,						len(self.depths					) + 1					)]
         __A     ,						__A       =						get_aligned_output_features_output_indices(
             out_features=UpperCamelCase_			,						out_indices=UpperCamelCase_			,						stage_names=self.stage_names					)












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__)




