






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

__snake_case												=  logging.get_logger(__name__)







@add_end_docstrings(lowercase_ )
class 			UpperCAmelCase_						(					lowercase_ ):



      """simple docstring"""
      def __init__(				self    ,				*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          super().__init__(*SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )
          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     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_=None )				->      List[str]:
          UpperCamelCase       :Optional[int]								=   {}
          if top_k is not None:
              UpperCamelCase       :Optional[Any]								=   top_k
          return {}, {}, postprocess_params
      def __call__(				self    ,				SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )				->      Optional[int]:
          return super().__call__(SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          UpperCamelCase       :Optional[int]								=   load_image(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :List[Any]								=   self.image_processor(images=SCREAMING_SNAKE_CASE_    ,				return_tensors=self.framework )
          return model_inputs
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_ )				->      Union[str, Any]:
          UpperCamelCase       :Optional[int]								=   self.model(**SCREAMING_SNAKE_CASE_ )
          return model_outputs




      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=5 )				->      Optional[Any]:
          if top_k > self.model.config.num_labels:
              UpperCamelCase       :str								=   self.model.config.num_labels

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

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










"""simple docstring"""







def 				UpperCamelCase_    (    lowerCAmelCase__      :				int = 100						)						->   int:

  """simple docstring"""




  lowerCAmelCase_   :				Any								= (n * (n + 1) // 2) ** 2
  lowerCAmelCase_   :				Optional[int]								= n * (n + 1) * (2 * n + 1) // 6
  return sum_cubes - sum_squares


if __name__ == "__main__":
     print(f'{solution() = }')





