





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_tf_available():
     import tensorflow as tf

     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
     from ..tf_utils import stable_softmax

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

UpperCAmelCase     :     Union[str, Any]							=	logging.get_logger(__name__)







@add_end_docstrings(UpperCamelCase__)
class     __lowerCAmelCase	(   UpperCamelCase__):







      def __init__(   self		,				*lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        super().__init__(*lowerCAmelCase__		,				**lowerCAmelCase__							)
        requires_backends(self		,				"vision"							)
        self.check_model_type(
            TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
            if self.framework == "tf"
            else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING							)







      def        _lowercase  (   self		,				lowerCAmelCase__=None							)       ->						str:



        '''simple docstring'''




        a__     : int  ={}
        if top_k is not None:
          a__     : Optional[int]  =top_k
        return {}, {}, postprocess_params







      def __call__(   self		,				lowerCAmelCase__		,				**lowerCAmelCase__							)       ->						Tuple:



        '''simple docstring'''




        return super().__call__(lowerCAmelCase__		,				**lowerCAmelCase__							)







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : int  =load_image(lowerCAmelCase__							)
        a__     : Union[str, Any]  =self.image_processor(images=lowerCAmelCase__		,				return_tensors=self.framework							)
        return model_inputs







      def        _lowercase  (   self		,				lowerCAmelCase__							)       ->						Union[str, Any]:



        '''simple docstring'''




        a__     : Union[str, Any]  =self.model(**lowerCAmelCase__							)
        return model_outputs







      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__=5							)       ->						Tuple:



        '''simple docstring'''




        if top_k > self.model.config.num_labels:
          a__     : int  =self.model.config.num_labels

        if self.framework == "pt":
          a__     : List[Any]  =model_outputs.logits.softmax(-1							)[0]
          a__ ,							a__     : Tuple  =probs.topk(lowerCAmelCase__							)
        elif self.framework == "tf":
          a__     : Any  =stable_softmax(model_outputs.logits		,				axis=-1							)[0]
          a__     : Optional[int]  =tf.math.top_k(lowerCAmelCase__		,				k=lowerCAmelCase__							)
          a__ ,							a__     : str  =topk.values.numpy(), topk.indices.numpy()
        else:
          raise ValueError(F'''Unsupported framework: {self.framework}'''							)

        a__     : Optional[Any]  =scores.tolist()
        a__     : int  =ids.tolist()
        return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase__		,				lowerCAmelCase__							)]













import os
def 					_A	(		):





  """simple docstring"""



  a__     : Optional[int]  =os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE						)	,	"num.txt"						)
  with open(SCREAMING_SNAKE_CASE						) as file_hand:
    return str(sum(int(SCREAMING_SNAKE_CASE						) for line in file_hand						)						)[:10]


if __name__ == "__main__":
     print(solution())






