



"""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(_lowercase)
class 	__snake_case	(     _lowercase):




  def __init__(			self					:						Optional[int]						, *__lowerCAmelCase					:						List[Any]						, **__lowerCAmelCase					:						Optional[Any]		):






         """simple docstring"""






         super().__init__(*__a						, **__a		)

         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 						SCREAMING_SNAKE_CASE    (			self					:						Tuple						, **__lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         _lowerCamelCase						:    Dict         =			{}
         if "threshold" in kwargs:
                _lowerCamelCase						:    Optional[Any]         =			kwargs['''threshold''']
         return {}, {}, postprocess_kwargs




  def __call__(			self					:						int						, *__lowerCAmelCase					:						int						, **__lowerCAmelCase					:						str		):






         """simple docstring"""






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




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						Union[str, Any]		):






         """simple docstring"""






         _lowerCamelCase						:    List[str]         =			load_image(__a		)
         _lowerCamelCase						:    Tuple         =			torch.IntTensor([[image.height, image.width]]		)
         _lowerCamelCase						:    Tuple         =			self.image_processor(images=[image]						, return_tensors='''pt'''		)
         if self.tokenizer is not None:
                _lowerCamelCase						:    Optional[int]         =			self.tokenizer(text=inputs['''words''']						, boxes=inputs['''boxes''']						, return_tensors='''pt'''		)
         _lowerCamelCase						:    Dict         =			target_size
         return inputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						Any						, __lowerCAmelCase					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			model_inputs.pop('''target_size'''		)
         _lowerCamelCase						:    Tuple         =			self.model(**__a		)
         _lowerCamelCase						:    Optional[int]         =			outputs.__class__({'''target_size''': target_size, **outputs}		)
         if self.tokenizer is not None:
                _lowerCamelCase						:    Optional[int]         =			model_inputs['''bbox''']
         return model_outputs




  def 						SCREAMING_SNAKE_CASE    (			self					:						str						, __lowerCAmelCase					:						int						, __lowerCAmelCase					:						Optional[int]=0.9		):






         """simple docstring"""






         _lowerCamelCase						:    str         =			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.
                _lowerCamelCase      ,   _lowerCamelCase						:    Optional[int]         =			target_size[0].tolist()

                def unnormalize(__lowerCAmelCase					:						Tuple		):
                       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),
                               ]		)		)

                _lowerCamelCase      ,   _lowerCamelCase						:    List[Any]         =			model_outputs['''logits'''].squeeze(0		).softmax(dim=-1		).max(dim=-1		)
                _lowerCamelCase						:    Tuple         =			[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
                _lowerCamelCase						:    Tuple         =			[unnormalize(__a		) for bbox in model_outputs['''bbox'''].squeeze(0		)]
                _lowerCamelCase						:    int         =			['''score''', '''label''', '''box''']
                _lowerCamelCase						:    int         =			[dict(zip(__a						, __a		)		) for vals in zip(scores.tolist()						, __a						, __a		) if vals[0] > threshold]
         else:
                # This is a regular ForObjectDetectionModel
                _lowerCamelCase						:    Dict         =			self.image_processor.post_process_object_detection(__a						, __a						, __a		)
                _lowerCamelCase						:    str         =			raw_annotations[0]
                _lowerCamelCase						:    Optional[int]         =			raw_annotation['''scores''']
                _lowerCamelCase						:    List[Any]         =			raw_annotation['''labels''']
                _lowerCamelCase						:    Optional[int]         =			raw_annotation['''boxes''']

                _lowerCamelCase						:    List[str]         =			scores.tolist()
                _lowerCamelCase						:    str         =			[self.model.config.idalabel[label.item()] for label in labels]
                _lowerCamelCase						:    int         =			[self._get_bounding_box(__a		) for box in boxes]

                # {"scores": [...], ...} --> [{"score":x, ...}, ...]
                _lowerCamelCase						:    Dict         =			['''score''', '''label''', '''box''']
                _lowerCamelCase						:    Union[str, Any]         =			[
                    dict(zip(__a						, __a		)		)
                    for vals in zip(raw_annotation['''scores''']						, raw_annotation['''labels''']						, raw_annotation['''boxes''']		)
                ]

         return annotation




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict						, __lowerCAmelCase					:						Tuple		):






         """simple docstring"""






         if self.framework != "pt":
                raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''		)
         _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase      ,   _lowerCamelCase						:    int         =			box.int().tolist()
         _lowerCamelCase						:    Dict         =			{
             '''xmin''': xmin,
             '''ymin''': ymin,
             '''xmax''': xmax,
             '''ymax''': ymax,
         }
         return bbox







"""simple docstring"""






def 	snake_case_  (				A_ :		list						):


       '''simple docstring'''





       _lowerCamelCase						:    Union[str, Any]         =			len(A_						)
       for i in range(1,    A_						):
              _lowerCamelCase						:    Tuple         =			collection[i]
              _lowerCamelCase						:    Dict         =			0
              _lowerCamelCase						:    Any         =			i - 1

              while low <= high:
                     _lowerCamelCase						:    Optional[int]         =			(low + high) // 2
                     if val < collection[mid]:
                            _lowerCamelCase						:    List[str]         =			mid - 1
                     else:
                            _lowerCamelCase						:    Dict         =			mid + 1
              for j in range(A_,    A_,    -1						):
                     _lowerCamelCase						:    Optional[int]         =			collection[j - 1]
              _lowerCamelCase						:    Tuple         =			val
       return collection


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


