




'''simple docstring'''



from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase_			=						logging.get_logger(__name__)







@add_end_docstrings(A       )
class 		lowercase_   (			A       ):


   """simple docstring"""

   def __init__(   self :     int  ,				**__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      super().__init__(**__lowerCamelCase   )

      if self.framework == "tf":
         raise ValueError(F"""The {self.__class__} is only available in PyTorch."""   )

      requires_backends(self  ,				"vision"   )
      self.check_model_type(__lowerCamelCase   )



   def __call__(   self :     List[str]  ,				__lowerCamelCase :     Union[str, "Image.Image", List[Dict[str, Any]]]  ,				__lowerCamelCase :     Union[str, List[str]] = None  ,				**__lowerCamelCase :     List[str]  ,				):

      """simple docstring"""
      if "text_queries" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs.pop("text_queries"   )

      if isinstance(__lowerCamelCase  ,				(str, Image.Image)   ):
         _SCREAMING_SNAKE_CASE	=				{"image": image, "candidate_labels": candidate_labels}
      else:
         _SCREAMING_SNAKE_CASE	=				image
      _SCREAMING_SNAKE_CASE	=				super().__call__(__lowerCamelCase  ,				**__lowerCamelCase   )
      return results



   def 							lowerCAmelCase_      (   self :     Any  ,				**__lowerCamelCase :     Dict   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				{}
      if "threshold" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["threshold"]
      if "top_k" in kwargs:
         _SCREAMING_SNAKE_CASE	=				kwargs["top_k"]
      return {}, {}, postprocess_params



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     Union[str, Any]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_image(inputs["image"]   )
      _SCREAMING_SNAKE_CASE	=				inputs["candidate_labels"]
      if isinstance(__lowerCamelCase  ,				__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				candidate_labels.split(","   )

      _SCREAMING_SNAKE_CASE	=				torch.tensor([[image.height, image.width]]  ,				dtype=torch.intaa   )
      for i, candidate_label in enumerate(__lowerCamelCase   ):
         _SCREAMING_SNAKE_CASE	=				self.tokenizer(__lowerCamelCase  ,				return_tensors=self.framework   )
         _SCREAMING_SNAKE_CASE	=				self.image_processor(__lowerCamelCase  ,				return_tensors=self.framework   )
         yield {
             "is_last": i == len(__lowerCamelCase   ) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }



   def 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("target_size"   )
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("candidate_label"   )
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("is_last"   )

      _SCREAMING_SNAKE_CASE	=				self.model(**__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				{"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
      return model_outputs



   def 							lowerCAmelCase_      (   self :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]  ,				__lowerCamelCase :     Tuple=0.1  ,				__lowerCamelCase :     Tuple=None   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				[]
      for model_output in model_outputs:
         _SCREAMING_SNAKE_CASE	=				model_output["candidate_label"]
         _SCREAMING_SNAKE_CASE	=				BaseModelOutput(__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				self.image_processor.post_process_object_detection(
             outputs=__lowerCamelCase  ,				threshold=__lowerCamelCase  ,				target_sizes=model_output["target_size"]   )[0]

         for index in outputs["scores"].nonzero():
            _SCREAMING_SNAKE_CASE	=				outputs["scores"][index].item()
            _SCREAMING_SNAKE_CASE	=				self._get_bounding_box(outputs["boxes"][index][0]   )

            _SCREAMING_SNAKE_CASE	=				{"score": score, "label": label, "box": box}
            results.append(__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				sorted(__lowerCamelCase  ,				key=lambda __lowerCamelCase   : x["score"]  ,				reverse=__lowerCamelCase   )
      if top_k:
         _SCREAMING_SNAKE_CASE	=				results[:top_k]

      return results



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     "torch.Tensor"   ):

      """simple docstring"""
      if self.framework != "pt":
         raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."   )
      _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				box.int().tolist()
      _SCREAMING_SNAKE_CASE	=				{
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox










'''simple docstring'''



def 			SCREAMING_SNAKE_CASE_			(							__A    :			list  )			->			list:
   if len(__A  ) <= 1:
      return lst

   _SCREAMING_SNAKE_CASE	=				1

   while i < len(__A  ):
      if lst[i - 1] <= lst[i]:
         i += 1
      else:
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				lst[i], lst[i - 1]
         i -= 1
         if i == 0:
            _SCREAMING_SNAKE_CASE	=				1

   return lst


if __name__ == "__main__":
 lowerCamelCase_			=						input('Enter numbers separated by a comma:\n').strip()
 lowerCamelCase_			=						[int(item) for item in user_input.split(',')]
 print(gnome_sort(unsorted))

