

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


lowerCAmelCase__								=		logging.get_logger(__name__)

lowerCAmelCase__								=		{
    "microsoft/swinv2-tiny-patch4-window8-256": (
        "https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json"
    ),
}







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "swinv2"

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


		def __init__(				self	,       __snake_case=2_24	,       __snake_case=4	,       __snake_case=3	,       __snake_case=96	,       __snake_case=[2, 2, 6, 2]	,       __snake_case=[3, 6, 12, 24]	,       __snake_case=7	,       __snake_case=4.0	,       __snake_case=True	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.1	,       __snake_case="gelu"	,       __snake_case=False	,       __snake_case=0.0_2	,       __snake_case=1e-5	,       __snake_case=32	,       **__snake_case	,       ):
			super().__init__(**UpperCAmelCase__)

			_UpperCamelCase   :		Union[str, Any]			=						image_size
			_UpperCamelCase   :		int			=						patch_size
			_UpperCamelCase   :		List[Any]			=						num_channels
			_UpperCamelCase   :		int			=						embed_dim
			_UpperCamelCase   :		List[str]			=						depths
			_UpperCamelCase   :		Union[str, Any]			=						len(UpperCAmelCase__)
			_UpperCamelCase   :		Tuple			=						num_heads
			_UpperCamelCase   :		Any			=						window_size
			_UpperCamelCase   :		Dict			=						mlp_ratio
			_UpperCamelCase   :		Optional[Any]			=						qkv_bias
			_UpperCamelCase   :		List[str]			=						hidden_dropout_prob
			_UpperCamelCase   :		Tuple			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Dict			=						drop_path_rate
			_UpperCamelCase   :		Optional[Any]			=						hidden_act
			_UpperCamelCase   :		List[str]			=						use_absolute_embeddings
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Dict			=						initializer_range
			_UpperCamelCase   :		int			=						encoder_stride
			# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
			# this indicates the channel dimension after the last stage of the model
			_UpperCamelCase   :		List[str]			=						int(embed_dim * 2 ** (len(UpperCAmelCase__) - 1))
			_UpperCamelCase   :		Tuple			=						(0, 0, 0, 0)







from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels




