





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

__A							:			Optional[int]         =			logging.get_logger(__name__)


__A							:			Dict         =			Dict[str, Any]
__A							:			Optional[int]         =			List[Prediction]







@add_end_docstrings(__snake_case	)
class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     *snake_case_				,     **snake_case_							):
         super().__init__(*snake_case_				,     **snake_case_							)

         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				,     **snake_case_							):
         _A     =							{}
         if "threshold" in kwargs:
             _A     =							kwargs['threshold']
         return {}, {}, postprocess_kwargs




     def __call__(    self				,     *snake_case_				,     **snake_case_							):
         return super().__call__(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							load_image(snake_case_							)
         _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        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							model_inputs.pop('target_size'							)
         _A     =							self.model(**snake_case_							)
         _A     =							outputs.__class__({'target_size': target_size, **outputs}							)
         if self.tokenizer is not None:
             _A     =							model_inputs['bbox']
         return model_outputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0.9							):
         _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(snake_case_							):
                 return self._get_bounding_box(
                     torch.Tensor(
                         [
                             (width * bbox[0] / 1000),
                             (height * bbox[1] / 1000),
                             (width * bbox[2] / 1000),
                             (height * bbox[3] / 1000),
                         ]							)							)

             _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(snake_case_							) for bbox in model_outputs['bbox'].squeeze(0							)]
             _A     =							['score', 'label', 'box']
             _A     =							[dict(zip(snake_case_				,     snake_case_							)							) for vals in zip(scores.tolist()				,     snake_case_				,     snake_case_							) if vals[0] > threshold]
         else:
             # This is a regular ForObjectDetectionModel
             _A     =							self.image_processor.post_process_object_detection(snake_case_				,     snake_case_				,     snake_case_							)
             _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(snake_case_							) for box in boxes]

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

         return annotation




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         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 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE				)							-> bool:




    """simple docstring"""




    if not isinstance(_SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE				):
        raise ValueError('check_bouncy() accepts only integer arguments'				)
    _A     =							str(_SCREAMING_SNAKE_CASE				)
    _A     =							''.join(sorted(_SCREAMING_SNAKE_CASE				)				)
    return sorted_str_n != str_n and sorted_str_n[::-1] != str_n





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE = 99				)							-> int:




    """simple docstring"""




    if not 0 < percent < 100:
        raise ValueError('solution() only accepts values from 0 to 100'				)
    _A     =							0
    _A     =							1

    while True:
        if check_bouncy(_SCREAMING_SNAKE_CASE				):
            bouncy_num += 1
        if (bouncy_num / num) * 100 >= percent:
            return num
        num += 1


if __name__ == "__main__":
 from doctest import testmod

 testmod()
 print(f"{solution(99)}")





