





"""simple docstring"""




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

SCREAMING_SNAKE_CASE_						       =	logging.get_logger(__name__)

@add_end_docstrings(lowerCAmelCase_       )
class 		UpperCamelCase__			(				lowerCAmelCase_       ):



     '''simple docstring'''

     def __init__(							self						:   List[str]						,*lowerCamelCase__						:   List[str]						,**lowerCamelCase__						:   Optional[Any]	)					->		Union[str, Any]:




           '''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     SCREAMING_SNAKE_CASE__  (							self						:   Any						,lowerCamelCase__						:   Optional[Any]=None	)					->		Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						{}
           if top_k is not None:
                 SCREAMING_SNAKE_CASE		=						top_k
           return {}, {}, postprocess_params






     def __call__(							self						:   List[Any]						,lowerCamelCase__						:   Union[str, List[str], "Image.Image", List["Image.Image"]]						,**lowerCamelCase__						:   Union[str, Any]	)					->		Dict:




           '''simple docstring'''


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






     def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   Tuple	)					->		int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						load_image(lowerCamelCase__	)
           SCREAMING_SNAKE_CASE		=						self.image_processor(images=lowerCamelCase__						,return_tensors=self.framework	)
           return model_inputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   int	)					->		Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE		=						self.model(**lowerCamelCase__	)
           return model_outputs






     def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]						,lowerCamelCase__						:   Union[str, Any]=5	)					->		Union[str, Any]:




           '''simple docstring'''


           if top_k > self.model.config.num_labels:
                 SCREAMING_SNAKE_CASE		=						self.model.config.num_labels

           if self.framework == "pt":
                 SCREAMING_SNAKE_CASE		=						model_outputs.logits.softmax(-1	)[0]
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						probs.topk(lowerCamelCase__	)
           elif self.framework == "tf":
                 SCREAMING_SNAKE_CASE		=						stable_softmax(model_outputs.logits						,axis=-1	)[0]
                 SCREAMING_SNAKE_CASE		=						tf.math.top_k(lowerCamelCase__						,k=lowerCamelCase__	)
                 SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE		=						topk.values.numpy(), topk.indices.numpy()
           else:
                 raise ValueError(F"""Unsupported framework: {self.framework}"""	)

           SCREAMING_SNAKE_CASE		=						scores.tolist()
           SCREAMING_SNAKE_CASE		=						ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase__						,lowerCamelCase__	)]









def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       int:


      '''simple docstring'''


      if index == number_of_items:
            return 0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						0
      SCREAMING_SNAKE_CASE		=						knapsack(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						index + 1     )
      if weights[index] <= max_weight:
            SCREAMING_SNAKE_CASE		=						values[index] + knapsack(
                _SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						max_weight - weights[index]			,						index + 1     )
      return max(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      import doctest

      doctest.testmod()


