




from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A        =    logging.get_logger(__name__)

__A        =    {
    "google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       '''efficientnet'''


			def __init__(      self			,						lowerCamelCase__ = 3			,						lowerCamelCase__ = 600			,						lowerCamelCase__ = 2.0			,						lowerCamelCase__ = 3.1			,						lowerCamelCase__ = 8			,						lowerCamelCase__ = [3, 3, 5, 3, 5, 5, 3]			,						lowerCamelCase__ = [32, 16, 24, 40, 80, 112, 192]			,						lowerCamelCase__ = [16, 24, 40, 80, 112, 192, 320]			,						lowerCamelCase__ = []			,						lowerCamelCase__ = [1, 2, 2, 2, 1, 2, 1]			,						lowerCamelCase__ = [1, 2, 2, 3, 3, 4, 1]			,						lowerCamelCase__ = [1, 6, 6, 6, 6, 6, 6]			,						lowerCamelCase__ = 0.25			,						lowerCamelCase__ = "swish"			,						lowerCamelCase__ = 2_560			,						lowerCamelCase__ = "mean"			,						lowerCamelCase__ = 0.02			,						lowerCamelCase__ = 0.0_01			,						lowerCamelCase__ = 0.99			,						lowerCamelCase__ = 0.5			,						lowerCamelCase__ = 0.2			,						**lowerCamelCase__			,						)    ->      Optional[int]:



							'''simple docstring'''
							super().__init__(**__a							)

							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    image_size
							__lowerCamelCase		       =    width_coefficient
							__lowerCamelCase		       =    depth_coefficient
							__lowerCamelCase		       =    depth_divisor
							__lowerCamelCase		       =    kernel_sizes
							__lowerCamelCase		       =    in_channels
							__lowerCamelCase		       =    out_channels
							__lowerCamelCase		       =    depthwise_padding
							__lowerCamelCase		       =    strides
							__lowerCamelCase		       =    num_block_repeats
							__lowerCamelCase		       =    expand_ratios
							__lowerCamelCase		       =    squeeze_expansion_ratio
							__lowerCamelCase		       =    hidden_act
							__lowerCamelCase		       =    hidden_dim
							__lowerCamelCase		       =    pooling_type
							__lowerCamelCase		       =    initializer_range
							__lowerCamelCase		       =    batch_norm_eps
							__lowerCamelCase		       =    batch_norm_momentum
							__lowerCamelCase		       =    dropout_rate
							__lowerCamelCase		       =    drop_connect_rate
							__lowerCamelCase		       =    sum(__a							) * 4







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       version.parse('''1.11''' )


			@property
			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]							)






			@property
			def       lowercase_    (      self							)    ->      Any:



							'''simple docstring'''
							return 1e-5







from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case		=  logging.get_logger(__name__)

_snake_case		=  {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}



class   UpperCAmelCase_    (       a   ,					a):
							lowerCamelCase__   			=		'swin'

							lowerCamelCase__   			=		{
							    'num_attention_heads': 'num_heads',
							    'num_hidden_layers': 'num_layers',
							}







							def __init__(   self,			__a=224,			__a=4,			__a=3,			__a=96,			__a=[2, 2, 6, 2],			__a=[3, 6, 12, 24],			__a=7,			__a=4.0,			__a=True,			__a=0.0,			__a=0.0,			__a=0.1,			__a="gelu",			__a=False,			__a=0.02,			__a=1E-5,			__a=32,			__a=None,			__a=None,			**__a,			):

														'''simple docstring'''

														super().__init__(**__a)

														_lowerCAmelCase     :							Any    =    image_size
														_lowerCAmelCase     :							Union[str, Any]    =    patch_size
														_lowerCAmelCase     :							Tuple    =    num_channels
														_lowerCAmelCase     :							List[Any]    =    embed_dim
														_lowerCAmelCase     :							Tuple    =    depths
														_lowerCAmelCase     :							Optional[Any]    =    len(__a)
														_lowerCAmelCase     :							int    =    num_heads
														_lowerCAmelCase     :							int    =    window_size
														_lowerCAmelCase     :							int    =    mlp_ratio
														_lowerCAmelCase     :							List[Any]    =    qkv_bias
														_lowerCAmelCase     :							str    =    hidden_dropout_prob
														_lowerCAmelCase     :							Union[str, Any]    =    attention_probs_dropout_prob
														_lowerCAmelCase     :							Any    =    drop_path_rate
														_lowerCAmelCase     :							int    =    hidden_act
														_lowerCAmelCase     :							Tuple    =    use_absolute_embeddings
														_lowerCAmelCase     :							Optional[int]    =    layer_norm_eps
														_lowerCAmelCase     :							Tuple    =    initializer_range
														_lowerCAmelCase     :							Tuple    =    encoder_stride
														# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
														# this indicates the channel dimension after the last stage of the model
														_lowerCAmelCase     :							List[str]    =    int(embed_dim * 2 ** (len(__a) - 1))
														_lowerCAmelCase     :							List[Any]    =    ["stem"] + [f"stage{idx}" for idx in range(1,			len(__a) + 1)]
														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[int]    =    get_aligned_output_features_output_indices(
														    out_features=__a,			out_indices=__a,			stage_names=self.stage_names)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict(
														    [
														        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
														    ])







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4
