





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


lowercase      :       List[Any]									=  logging.get_logger(__name__)

lowercase      :       Optional[int]									=  {
    """microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}





class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  '''cvt'''

   def __init__(					self					,  lowercase=3					,  lowercase=[7, 3, 3]					,  lowercase=[4, 2, 2]					,  lowercase=[2, 1, 1]					,  lowercase=[64, 192, 384]					,  lowercase=[1, 3, 6]					,  lowercase=[1, 2, 10]					,  lowercase=[4.0, 4.0, 4.0]					,  lowercase=[0.0, 0.0, 0.0]					,  lowercase=[0.0, 0.0, 0.0]					,  lowercase=[0.0, 0.0, 0.1]					,  lowercase=[True, True, True]					,  lowercase=[False, False, True]					,  lowercase=["dw_bn", "dw_bn", "dw_bn"]					,  lowercase=[3, 3, 3]					,  lowercase=[1, 1, 1]					,  lowercase=[2, 2, 2]					,  lowercase=[1, 1, 1]					,  lowercase=[1, 1, 1]					,  lowercase=0.02					,  lowercase=1e-12					,  **lowercase					,  )			->						int:







          '''simple docstring'''

          super().__init__(**lowercase)
          a__						:	str =				num_channels
          a__						:	Dict =				patch_sizes
          a__						:	Union[str, Any] =				patch_stride
          a__						:	Any =				patch_padding
          a__						:	Tuple =				embed_dim
          a__						:	Union[str, Any] =				num_heads
          a__						:	Any =				depth
          a__						:	List[str] =				mlp_ratio
          a__						:	Any =				attention_drop_rate
          a__						:	Any =				drop_rate
          a__						:	str =				drop_path_rate
          a__						:	Dict =				qkv_bias
          a__						:	Tuple =				cls_token
          a__						:	List[Any] =				qkv_projection_method
          a__						:	Dict =				kernel_qkv
          a__						:	Dict =				padding_kv
          a__						:	Optional[int] =				stride_kv
          a__						:	Dict =				padding_q
          a__						:	Optional[int] =				stride_q
          a__						:	Any =				initializer_range
          a__						:	Union[str, Any] =				layer_norm_eps









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase      :       List[Any]									=  {
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowercase      :       Dict									=  [
        """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """NllbMoeForConditionalGeneration""",
        """NllbMoeModel""",
        """NllbMoePreTrainedModel""",
        """NllbMoeTop2Router""",
        """NllbMoeSparseMLP""",
    ]


if TYPE_CHECKING:
    from .configuration_nllb_moe import (
        NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
        NllbMoeConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_nllb_moe import (
            NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
            NllbMoeForConditionalGeneration,
            NllbMoeModel,
            NllbMoePreTrainedModel,
            NllbMoeSparseMLP,
            NllbMoeTopaRouter,
        )


else:
    import sys

    lowercase      :       List[Any]									=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


