"""simple docstring"""





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


_UpperCamelCase   :						Any      =    logging.get_logger(__name__)

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


class      UpperCAmelCase_	(    _a):
      lowerCamelCase__		:    Tuple   =  "swinv2"

      lowerCamelCase__		:    Tuple   =  {
          "num_attention_heads": "num_heads",
          "num_hidden_layers": "num_layers",
      }


      def __init__(   self		,			a=2_2_4		,			a=4		,			a=3		,			a=9_6		,			a=[2, 2, 6, 2]		,			a=[3, 6, 1_2, 2_4]		,			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=3_2		,			**a		,			)       ->					Tuple:
             super().__init__(**snake_case_							)

             lowercase__			:		Optional[int]       =	image_size
             lowercase__			:		Union[str, Any]       =	patch_size
             lowercase__			:		Optional[int]       =	num_channels
             lowercase__			:		str       =	embed_dim
             lowercase__			:		List[str]       =	depths
             lowercase__			:		int       =	len(snake_case_							)
             lowercase__			:		Union[str, Any]       =	num_heads
             lowercase__			:		Tuple       =	window_size
             lowercase__			:		str       =	mlp_ratio
             lowercase__			:		Optional[Any]       =	qkv_bias
             lowercase__			:		Union[str, Any]       =	hidden_dropout_prob
             lowercase__			:		Dict       =	attention_probs_dropout_prob
             lowercase__			:		Optional[Any]       =	drop_path_rate
             lowercase__			:		Tuple       =	hidden_act
             lowercase__			:		str       =	use_absolute_embeddings
             lowercase__			:		List[str]       =	layer_norm_eps
             lowercase__			:		Optional[int]       =	initializer_range
             lowercase__			:		Dict       =	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
             lowercase__			:		List[str]       =	int(embed_dim * 2 ** (len(snake_case_							) - 1)							)
             lowercase__			:		Tuple       =	(0, 0, 0, 0)






'''simple docstring'''





import numpy as np

from transformers import Pipeline





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[int]:
      snake_case__  :    Optional[Any]    						=      np.max(_lowerCAmelCase     ,   axis=-1     ,   keepdims=_lowerCAmelCase		)
      snake_case__  :    List[str]    						=      np.exp(outputs - maxes		)
      return shifted_exp / shifted_exp.sum(axis=-1     ,   keepdims=_lowerCAmelCase		)







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    def lowerCamelCase       (    self					:     Optional[Any] ,     **snake_case_					:     int						):
          snake_case__  :    Optional[int]    						=      {}
          if "second_text" in kwargs:
                snake_case__  :    Union[str, Any]    						=      kwargs["""second_text"""]
          return preprocess_kwargs, {}, {}




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     Tuple ,     snake_case_					:     Union[str, Any]=None						):
          return self.tokenizer(snake_case_ ,     text_pair=snake_case_ ,     return_tensors=self.framework						)




    def lowerCamelCase       (    self					:     List[Any] ,     snake_case_					:     Dict						):
          return self.model(**snake_case_						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     List[Any]						):
          snake_case__  :    Union[str, Any]    						=      model_outputs.logits[0].numpy()
          snake_case__  :    List[str]    						=      softmax(snake_case_						)

          snake_case__  :    List[str]    						=      np.argmax(snake_case_						)
          snake_case__  :    List[str]    						=      self.model.config.idalabel[best_class]
          snake_case__  :    Optional[int]    						=      probabilities[best_class].item()
          snake_case__  :    str    						=      logits.tolist()
          return {"label": label, "score": score, "logits": logits}
