





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

A__:    Any  	=	logging.get_logger(__name__)




@add_end_docstrings(UpperCamelCase__)
class        _a    (    UpperCamelCase__):

      """simple docstring"""


      def __init__(		self:			int	,    *__lowerCamelCase:			Tuple	,    **__lowerCamelCase:			List[str]					):
          '''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 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			Union[str, Any]=None					):
          '''simple docstring'''


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






      def __call__(		self:			Union[str, Any]	,    __lowerCamelCase:			Union[str, List[str], "Image.Image", List["Image.Image"]]	,    **__lowerCamelCase:			Dict					):
          '''simple docstring'''


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






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			List[Any]					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     load_image(__lowerCamelCase					)
          UpperCamelCase__:	str					=     self.image_processor(images=__lowerCamelCase	,    return_tensors=self.framework					)
          return model_inputs






      def 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			str					):
          '''simple docstring'''


          UpperCamelCase__:	int					=     self.model(**__lowerCamelCase					)
          return model_outputs






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			Any	,    __lowerCamelCase:			Optional[Any]=5					):
          '''simple docstring'''


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

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

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








import math



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	Dict					=     len(A_)
    UpperCamelCase__:	Optional[Any]					=     int(math.floor(math.sqrt(A_)))
    UpperCamelCase__:	Union[str, Any]					=     0
    while arr[min(A_    ,A_) - 1] < x:
        UpperCamelCase__:	Any					=     step
        step += int(math.floor(math.sqrt(A_)))
        if prev >= n:
            return -1

    while arr[prev] < x:
        UpperCamelCase__:	Dict					=     prev + 1
        if prev == min(A_    ,A_):
            return -1
    if arr[prev] == x:
        return prev
    return -1


if __name__ == "__main__":
   A__:    Tuple  	=	input('''Enter numbers separated by a comma:\n''').strip()
   A__:    List[Any]  	=	[int(item) for item in user_input.split(''',''')]
   A__:    int  	=	int(input('''Enter the number to be searched:\n'''))
   A__:    Any  	=	jump_search(arr, x)
   if res == -1:
      print('''Number not found!''')
   else:
      print(f"Number {x} is at index {res}")

