




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase	:	List[str]				=   {
    '''configuration_convnext''': ['''CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ConvNextConfig''', '''ConvNextOnnxConfig''']
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	str				=   ['''ConvNextFeatureExtractor''']
       lowerCamelCase	:	int				=   ['''ConvNextImageProcessor''']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Dict				=   [
           '''CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''ConvNextForImageClassification''',
           '''ConvNextModel''',
           '''ConvNextPreTrainedModel''',
           '''ConvNextBackbone''',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase	:	Optional[int]				=   [
           '''TFConvNextForImageClassification''',
           '''TFConvNextModel''',
           '''TFConvNextPreTrainedModel''',
       ]

if TYPE_CHECKING:
       from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_convnext import ConvNextFeatureExtractor
              from .image_processing_convnext import ConvNextImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnext import (
                  CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextBackbone,
                  ConvNextForImageClassification,
                  ConvNextModel,
                  ConvNextPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
       import sys

       lowerCamelCase	:	Dict				=   _LazyModule(__name__, globals()['__file__'], _import_structure)











from typing import List

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


lowerCamelCase	:	Optional[int]				=   logging.get_logger(__name__)

lowerCamelCase	:	Tuple				=   {
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 						__lowercase		(UpperCamelCase__							):




      """simple docstring"""





      _snake_case												=						"""efficientformer"""

      def __init__(				self    ,				A = [3, 2, 6, 4]    ,				A = [4_8, 9_6, 2_2_4, 4_4_8]    ,				A = [True, True, True, True]    ,				A = 4_4_8    ,				A = 3_2    ,				A = 4    ,				A = 7    ,				A = 5    ,				A = 8    ,				A = 4    ,				A = 0.0    ,				A = 1_6    ,				A = 3    ,				A = 3    ,				A = 3    ,				A = 2    ,				A = 1    ,				A = 0.0    ,				A = 1    ,				A = True    ,				A = True    ,				A = 1e-5    ,				A = "gelu"    ,				A = 0.02    ,				A = 1e-1_2    ,				A = 2_2_4    ,				A = 1e-0_5    ,				**A    ,				)		->    None:
         super().__init__(**A     )

         snake_case				:    Dict           =	hidden_act
         snake_case				:    int           =	hidden_dropout_prob
         snake_case				:    Any           =	hidden_sizes
         snake_case				:    Optional[Any]           =	num_hidden_layers
         snake_case				:    List[Any]           =	num_attention_heads
         snake_case				:    List[Any]           =	initializer_range
         snake_case				:    str           =	layer_norm_eps
         snake_case				:    Dict           =	patch_size
         snake_case				:    Optional[int]           =	num_channels
         snake_case				:    int           =	depths
         snake_case				:    Optional[int]           =	mlp_expansion_ratio
         snake_case				:    Any           =	downsamples
         snake_case				:    Dict           =	dim
         snake_case				:    Optional[int]           =	key_dim
         snake_case				:    Union[str, Any]           =	attention_ratio
         snake_case				:    Any           =	resolution
         snake_case				:    Dict           =	pool_size
         snake_case				:    Any           =	downsample_patch_size
         snake_case				:    Tuple           =	downsample_stride
         snake_case				:    Any           =	downsample_pad
         snake_case				:    Union[str, Any]           =	drop_path_rate
         snake_case				:    List[str]           =	num_metaad_blocks
         snake_case				:    Union[str, Any]           =	distillation
         snake_case				:    List[str]           =	use_layer_scale
         snake_case				:    int           =	layer_scale_init_value
         snake_case				:    Union[str, Any]           =	image_size
         snake_case				:    Dict           =	batch_norm_eps





