

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

lowercase_       		=  logging.get_logger(__name__)


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


@add_end_docstrings(_UpperCAmelCase )
class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def __init__(      self :       Optional[int],*lowercase_ :       Union[str, Any],**lowercase_ :       Tuple		)->    int:





        '''simple docstring'''







        super().__init__(*lowercase_,**lowercase_		)

        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 						snake_case__     (      self :       Union[str, Any],**lowercase_ :       int		)->    Any:





        '''simple docstring'''







        A__          =   {}
        if "threshold" in kwargs:
            A__          =   kwargs['threshold']
        return {}, {}, postprocess_kwargs


    def __call__(      self :       List[str],*lowercase_ :       int,**lowercase_ :       Union[str, Any]		)->    Union[Predictions, List[Prediction]]:





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   load_image(lowercase_		)
        A__          =   torch.IntTensor([[image.height, image.width]]		)
        A__          =   self.image_processor(images=[image],return_tensors='pt'		)
        if self.tokenizer is not None:
            A__          =   self.tokenizer(text=inputs['words'],boxes=inputs['boxes'],return_tensors='pt'		)
        A__          =   target_size
        return inputs


    def 						snake_case__     (      self :       Dict,lowercase_ :       int		)->    str:





        '''simple docstring'''







        A__          =   model_inputs.pop('target_size'		)
        A__          =   self.model(**lowercase_		)
        A__          =   outputs.__class__({'target_size': target_size, **outputs}		)
        if self.tokenizer is not None:
            A__          =   model_inputs['bbox']
        return model_outputs


    def 						snake_case__     (      self :       List[str],lowercase_ :       Tuple,lowercase_ :       Union[str, Any]=0.9		)->    List[str]:





        '''simple docstring'''







        A__          =   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.
            A__      ,						A__          =   target_size[0].tolist()

            def unnormalize(lowercase_ :       List[str]		):
                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),
                        ]		)		)

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

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

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

        return annotation


    def 						snake_case__     (      self :       Optional[int],lowercase_ :       "torch.Tensor"		)->    Dict[str, int]:





        '''simple docstring'''







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




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      Tuple:





    '''simple docstring'''






    A__          =   0
    A__          =   len(SCREAMING_SNAKE_CASE__      ) - 1

    while left <= right:
        # avoid divided by 0 during interpolation
        if sorted_collection[left] == sorted_collection[right]:
            if sorted_collection[left] == item:
                return left
            else:
                return None

        A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
            sorted_collection[right] - sorted_collection[left]
        )

        # out of range check
        if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
            return None

        A__          =   sorted_collection[point]
        if current_item == item:
            return point
        else:
            if point < left:
                A__          =   left
                A__          =   point
            elif point > right:
                A__          =   right
                A__          =   point
            else:
                if item < current_item:
                    A__          =   point - 1
                else:
                    A__          =   point + 1
    return None
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	List[str]      )							->      str:





    '''simple docstring'''






    if sorted_collection[left] == sorted_collection[right]:
        if sorted_collection[left] == item:
            return left
        else:
            return None

    A__          =   left + ((item - sorted_collection[left]) * (right - left)) // (
        sorted_collection[right] - sorted_collection[left]
    )

    # out of range check
    if point < 0 or point >= len(SCREAMING_SNAKE_CASE__      ):
        return None

    if sorted_collection[point] == item:
        return point
    elif point < left:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    elif point > right:
        return interpolation_search_by_recursion(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )
    else:
        if sorted_collection[point] > item:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point - 1      )
        else:
            return interpolation_search_by_recursion(
                SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__    ,			point + 1    ,			SCREAMING_SNAKE_CASE__      )
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Tuple      )							->      Tuple:





    '''simple docstring'''






    if collection != sorted(SCREAMING_SNAKE_CASE__      ):
        raise ValueError('Collection must be ascending sorted'      )
    return True


if __name__ == "__main__":
     import sys
     lowercase_       		=  0
     if debug == 1:
          lowercase_       		=  [10, 30, 40, 45, 50, 66, 77, 93]
          try:
               __assert_sorted(collection)
          except ValueError:
               sys.exit("Sequence must be ascending sorted to apply interpolation search")
          lowercase_       		=  67

     lowercase_       		=  interpolation_search(collection, target)
     if result is not None:
          print(f"""{target} found at positions: {result}""")
     else:
          print("Not found")

