
'''simple docstring'''







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


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

lowercase					:							Tuple   =      {
    "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   (      _lowerCamelCase		,     _lowerCamelCase		):
							__lowercase   	= """swin"""

							__lowercase   	= {
							    """num_attention_heads""": """num_heads""",
							    """num_hidden_layers""": """num_layers""",
							}


							def __init__( self					,     lowerCAmelCase_=2_24					,     lowerCAmelCase_=4					,     lowerCAmelCase_=3					,     lowerCAmelCase_=96					,     lowerCAmelCase_=[2, 2, 6, 2]					,     lowerCAmelCase_=[3, 6, 12, 24]					,     lowerCAmelCase_=7					,     lowerCAmelCase_=4.0					,     lowerCAmelCase_=True					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=0.0					,     lowerCAmelCase_=0.1					,     lowerCAmelCase_="gelu"					,     lowerCAmelCase_=False					,     lowerCAmelCase_=0.02					,     lowerCAmelCase_=1E-5					,     lowerCAmelCase_=32					,     lowerCAmelCase_=None					,     lowerCAmelCase_=None					,     **lowerCAmelCase_					,     ):





								"""simple docstring"""






								super().__init__(**lowerCAmelCase_						)

								_snake_case     		=	image_size
								_snake_case     		=	patch_size
								_snake_case     		=	num_channels
								_snake_case     		=	embed_dim
								_snake_case     		=	depths
								_snake_case     		=	len(lowerCAmelCase_						)
								_snake_case     		=	num_heads
								_snake_case     		=	window_size
								_snake_case     		=	mlp_ratio
								_snake_case     		=	qkv_bias
								_snake_case     		=	hidden_dropout_prob
								_snake_case     		=	attention_probs_dropout_prob
								_snake_case     		=	drop_path_rate
								_snake_case     		=	hidden_act
								_snake_case     		=	use_absolute_embeddings
								_snake_case     		=	layer_norm_eps
								_snake_case     		=	initializer_range
								_snake_case     		=	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
								_snake_case     		=	int(embed_dim * 2 ** (len(lowerCAmelCase_						) - 1)						)
								_snake_case     		=	['stem'] + [F'stage{idx}' for idx in range(1					,     len(lowerCAmelCase_						) + 1						)]
								_snake_case		,				_snake_case     		=	get_aligned_output_features_output_indices(
								    out_features=lowerCAmelCase_					,     out_indices=lowerCAmelCase_					,     stage_names=self.stage_names						)


class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= version.parse("""1.11"""		)


							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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




							@property
							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								return 1E-4



'''simple docstring'''







from __future__ import annotations







def        SCREAMING_SNAKE_CASE__			(     __A			) ->			int:
	if not nums:
		return 0
	_snake_case     		=	nums[0]
	_snake_case     		=	0
	for num in nums[1:]:
		_snake_case		,				_snake_case     		=	(
		    max_excluding + num,
		    max(__A    ,       __A			),
		)
	return max(__A    ,       __A			)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

