code: 
  
  
  
  
  
  
  """simple docstring"""
  from typing import Any, Dict, List, Union
  
  import numpy as np
  
  from ..utils import add_end_docstrings, 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_torch_available():
   from ..models.auto.modeling_auto import (
       MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
       MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
       MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
       MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
   )
  
  
  snake_case   :   Optional[int]    		=		logging.get_logger(__name__)
  
  
  snake_case   :   Optional[int]    		=		Dict[str, Any]
  snake_case   :   Union[str, Any]    		=		List[Prediction]
  
  
  
  @add_end_docstrings(a_)
  class 					UpperCamelCase__   (							a_):
  
  
  
  
  
  
  
      """simple docstring"""
  
  
  
  
  
  
  
      def __init__(		self   :  Tuple							,						*UpperCamelCase_   :  Any							,						**UpperCamelCase_   :  Dict   ):
  
  
  
  
  
             '''simple docstring'''
  
             super().__init__(*UpperCamelCase_							,						**UpperCamelCase_   )
  
             if self.framework == "tf":
                    raise ValueError(f"""The {self.__class__} is only available in PyTorch."""   )
  
             requires_backends(self							,						'vision'   )
             self.check_model_type(
                 dict(
                     MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
                     + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
                     + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
                     + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()   )   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  int							,						**UpperCamelCase_   :  str   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						{}
             __magic_name__										=						{}
             if "subtask" in kwargs:
                    __magic_name__										=						kwargs['subtask']
                    __magic_name__										=						kwargs['subtask']
             if "threshold" in kwargs:
                    __magic_name__										=						kwargs['threshold']
             if "mask_threshold" in kwargs:
                    __magic_name__										=						kwargs['mask_threshold']
             if "overlap_mask_area_threshold" in kwargs:
                    __magic_name__										=						kwargs['overlap_mask_area_threshold']
  
             return preprocess_kwargs, {}, postprocess_kwargs
  
  
  
  
  
  
  
      def __call__(		self   :  List[Any]							,						UpperCamelCase_   :  List[str]							,						**UpperCamelCase_   :  Optional[int]   ):
  
  
  
  
  
             '''simple docstring'''
  
             return super().__call__(UpperCamelCase_							,						**UpperCamelCase_   )
  
  
  
  
  
  
  
      def 							a__ (		self   :  Any							,						UpperCamelCase_   :  Tuple							,						UpperCamelCase_   :  Optional[Any]=None   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						load_image(UpperCamelCase_   )
             __magic_name__										=						[(image.height, image.width)]
             if self.model.config.__class__.__name__ == "OneFormerConfig":
                    if subtask is None:
                           __magic_name__										=						{}
                    else:
                           __magic_name__										=						{'task_inputs': [subtask]}
                    __magic_name__										=						self.image_processor(images=[image]							,						return_tensors='pt'							,						**UpperCamelCase_   )
                    __magic_name__										=						self.tokenizer(
                        inputs['task_inputs']							,						padding='max_length'							,						max_length=self.model.config.task_seq_len							,						return_tensors=self.framework							,						)['input_ids']
             else:
                    __magic_name__										=						self.image_processor(images=[image]							,						return_tensors='pt'   )
             __magic_name__										=						target_size
             return inputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  Tuple   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						model_inputs.pop('target_size'   )
             __magic_name__										=						self.model(**UpperCamelCase_   )
             __magic_name__										=						target_size
             return model_outputs
  
  
  
  
  
  
  
      def 							a__ (		self   :  List[Any]							,						UpperCamelCase_   :  int							,						UpperCamelCase_   :  Any=None							,						UpperCamelCase_   :  Tuple=0.9							,						UpperCamelCase_   :  List[str]=0.5							,						UpperCamelCase_   :  Optional[Any]=0.5   ):
  
  
  
  
  
             '''simple docstring'''
  
             __magic_name__										=						None
             if subtask in {"panoptic", None} and hasattr(self.image_processor							,						'post_process_panoptic_segmentation'   ):
                    __magic_name__										=						self.image_processor.post_process_panoptic_segmentation
             elif subtask in {"instance", None} and hasattr(self.image_processor							,						'post_process_instance_segmentation'   ):
                    __magic_name__										=						self.image_processor.post_process_instance_segmentation
  
             if fn is not None:
                    __magic_name__										=						fn(
                        UpperCamelCase_							,						threshold=UpperCamelCase_							,						mask_threshold=UpperCamelCase_							,						overlap_mask_area_threshold=UpperCamelCase_							,						target_sizes=model_outputs['target_size']							,						)[0]
  
                    __magic_name__										=						[]
                    __magic_name__										=						outputs['segmentation']
  
                    for segment in outputs["segments_info"]:
                           __magic_name__										=						(segmentation == segment['id']) * 2_5_5
                           __magic_name__										=						Image.fromarray(mask.numpy().astype(np.uinta   )							,						mode='L'   )
                           __magic_name__										=						self.model.config.idalabel[segment['label_id']]
                           __magic_name__										=						segment['score']
                           annotation.append({'score': score, 'label': label, 'mask': mask}   )
  
             elif subtask in {"semantic", None} and hasattr(self.image_processor							,						'post_process_semantic_segmentation'   ):
                    __magic_name__										=						self.image_processor.post_process_semantic_segmentation(
                        UpperCamelCase_							,						target_sizes=model_outputs['target_size']   )[0]
  
                    __magic_name__										=						[]
                    __magic_name__										=						outputs.numpy()
                    __magic_name__										=						np.unique(UpperCamelCase_   )
  
                    for label in labels:
                           __magic_name__										=						(segmentation == label) * 2_5_5
                           __magic_name__										=						Image.fromarray(mask.astype(np.uinta   )							,						mode='L'   )
                           __magic_name__										=						self.model.config.idalabel[label]
                           annotation.append({'score': None, 'label': label, 'mask': mask}   )
             else:
                    raise ValueError(f"""Subtask {subtask} is not supported for model {type(self.model   )}"""   )
             return annotation
style_context: 
  
  
  
  
  
  
  """simple docstring"""
  def        A	(							__snake_case:   Dict   ,    __snake_case:   Tuple     )						->		int:
  
         """simple docstring"""
  
  
  
  
  
         sequence.append(__snake_case     )
  
         __magic_name__										=						0
         while sequence[index] != target:
                index += 1
  
         sequence.pop()
  
         if index == len(__snake_case     ):
                return None
  
         return index
  
  
  if __name__ == "__main__":
   snake_case   :   Dict    		=		input("""Enter numbers separated by comma:\n""").strip()
   snake_case   :   List[str]    		=		[int(item) for item in user_input.split(""",""")]
  
   snake_case   :   Union[str, Any]    		=		input("""Enter a single number to be found in the list:\n""")
   snake_case   :   str    		=		int(target_input)
   snake_case   :   Any    		=		sentinel_linear_search(sequence, target)
   if result is not None:
    print(f"""{target} found at positions: {result}""")
   else:
    print("""Not found""")