



"""simple docstring"""





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


__lowerCAmelCase							:       int          =       logging.get_logger(__name__)

__lowerCAmelCase							:       str          =       {
    'Visual-Attention-Network/van-base': (
        'https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json'
    ),
}



class  _lowerCAmelCase     (					UpperCamelCase__		):





		"""simple docstring"""






		_lowerCamelCase      	=							'''van'''





		def __init__(       self				,     _lowercase=2_2_4				,     _lowercase=3				,     _lowercase=[7, 3, 3, 3]				,     _lowercase=[4, 2, 2, 2]				,     _lowercase=[6_4, 1_2_8, 3_2_0, 5_1_2]				,     _lowercase=[3, 3, 1_2, 3]				,     _lowercase=[8, 8, 4, 4]				,     _lowercase="gelu"				,     _lowercase=0.02				,     _lowercase=1E-6				,     _lowercase=1E-2				,     _lowercase=0.0				,     _lowercase=0.0				,     **_lowercase				,     )			->    Dict:







								'''simple docstring'''

								super().__init__(**__lowerCamelCase )
								snake_case_ :			Tuple            =      image_size
								snake_case_ :			List[Any]            =      num_channels
								snake_case_ :			Any            =      patch_sizes
								snake_case_ :			List[Any]            =      strides
								snake_case_ :			List[str]            =      hidden_sizes
								snake_case_ :			str            =      depths
								snake_case_ :			str            =      mlp_ratios
								snake_case_ :			Any            =      hidden_act
								snake_case_ :			str            =      initializer_range
								snake_case_ :			Dict            =      layer_norm_eps
								snake_case_ :			Optional[int]            =      layer_scale_init_value
								snake_case_ :			Optional[Any]            =      drop_path_rate
								snake_case_ :			str            =      dropout_rate








a__: str							   =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
       from .utils import rich





