




'''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, Pipeline


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCamelCase_			=						logging.get_logger(__name__)


lowerCamelCase_			=						Dict[str, Any]
lowerCamelCase_			=						List[Prediction]







@add_end_docstrings(A       )
class 		lowercase_   (			A       ):


   """simple docstring"""

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

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

      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_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()   )   )



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

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



   def __call__(   self :     Dict  ,				*__lowerCamelCase :     List[Any]  ,				**__lowerCamelCase :     Tuple   ):

      """simple docstring"""
      return super().__call__(*__lowerCamelCase  ,				**__lowerCamelCase   )



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

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_image(__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				torch.IntTensor([[image.height, image.width]]   )
      _SCREAMING_SNAKE_CASE	=				self.image_processor(images=[image]  ,				return_tensors="pt"   )
      if self.tokenizer is not None:
         _SCREAMING_SNAKE_CASE	=				self.tokenizer(text=inputs["words"]  ,				boxes=inputs["boxes"]  ,				return_tensors="pt"   )
      _SCREAMING_SNAKE_CASE	=				target_size
      return inputs



   def 							lowerCAmelCase_      (   self :     int  ,				__lowerCamelCase :     Optional[int]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				model_inputs.pop("target_size"   )
      _SCREAMING_SNAKE_CASE	=				self.model(**__lowerCamelCase   )
      _SCREAMING_SNAKE_CASE	=				outputs.__class__({"target_size": target_size, **outputs}   )
      if self.tokenizer is not None:
         _SCREAMING_SNAKE_CASE	=				model_inputs["bbox"]
      return model_outputs



   def 							lowerCAmelCase_      (   self :     Dict  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Union[str, Any]=0.9   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				model_outputs["target_size"]
      if self.tokenizer is not None:
         # This is a LayoutLMForTokenClassification variant.
         # The OCR got the boxes and the model classified the words.
         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				target_size[0].tolist()

         def unnormalize(__lowerCamelCase :     Any   ):
            return self._get_bounding_box(
                torch.Tensor(
                    [
                        (width * bbox[0] / 1_0_0_0),
                        (height * bbox[1] / 1_0_0_0),
                        (width * bbox[2] / 1_0_0_0),
                        (height * bbox[3] / 1_0_0_0),
                    ]   )   )

         _SCREAMING_SNAKE_CASE,	_SCREAMING_SNAKE_CASE	=				model_outputs["logits"].squeeze(0   ).softmax(dim=-1   ).max(dim=-1   )
         _SCREAMING_SNAKE_CASE	=				[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
         _SCREAMING_SNAKE_CASE	=				[unnormalize(__lowerCamelCase   ) for bbox in model_outputs["bbox"].squeeze(0   )]
         _SCREAMING_SNAKE_CASE	=				["score", "label", "box"]
         _SCREAMING_SNAKE_CASE	=				[dict(zip(__lowerCamelCase  ,				__lowerCamelCase   )   ) for vals in zip(scores.tolist()  ,				__lowerCamelCase  ,				__lowerCamelCase   ) if vals[0] > threshold]
      else:
         # This is a regular ForObjectDetectionModel
         _SCREAMING_SNAKE_CASE	=				self.image_processor.post_process_object_detection(__lowerCamelCase  ,				__lowerCamelCase  ,				__lowerCamelCase   )
         _SCREAMING_SNAKE_CASE	=				raw_annotations[0]
         _SCREAMING_SNAKE_CASE	=				raw_annotation["scores"]
         _SCREAMING_SNAKE_CASE	=				raw_annotation["labels"]
         _SCREAMING_SNAKE_CASE	=				raw_annotation["boxes"]

         _SCREAMING_SNAKE_CASE	=				scores.tolist()
         _SCREAMING_SNAKE_CASE	=				[self.model.config.idalabel[label.item()] for label in labels]
         _SCREAMING_SNAKE_CASE	=				[self._get_bounding_box(__lowerCamelCase   ) for box in boxes]

         # {"scores": [...], ...} --> [{"score":x, ...}, ...]
         _SCREAMING_SNAKE_CASE	=				["score", "label", "box"]
         _SCREAMING_SNAKE_CASE	=				[
             dict(zip(__lowerCamelCase  ,				__lowerCamelCase   )   )
             for vals in zip(raw_annotation["scores"]  ,				raw_annotation["labels"]  ,				raw_annotation["boxes"]   )
         ]

      return annotation



   def 							lowerCAmelCase_      (   self :     Union[str, Any]  ,				__lowerCamelCase :     "torch.Tensor"   ):

      """simple docstring"""
      if self.framework != "pt":
         raise ValueError("The ObjectDetectionPipeline 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))

