





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







from math import isqrt, loga
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int				)			->						list[int]:
       SCREAMING_SNAKE_CASE_							:				int		=[True] * max_number
       for i in range(2			,							isqrt(max_number - 1				) + 1				):
              if is_prime[i]:
                     for j in range(i**2			,							UpperCAmelCase_			,							UpperCAmelCase_				):
                            SCREAMING_SNAKE_CASE_							:				Dict		=False

       return [i for i in range(2			,							UpperCAmelCase_				) if is_prime[i]]
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int = 8_0_0_8_0_0			,							UpperCAmelCase_	:     int = 8_0_0_8_0_0				)			->						int:
       SCREAMING_SNAKE_CASE_							:				str		=degree * loga(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				List[Any]		=int(UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				int		=calculate_prime_numbers(UpperCAmelCase_				)

       SCREAMING_SNAKE_CASE_							:				int		=0
       SCREAMING_SNAKE_CASE_							:				List[Any]		=0
       SCREAMING_SNAKE_CASE_							:				Any		=len(UpperCAmelCase_				) - 1
       while left < right:
              while (
                  prime_numbers[right] * loga(prime_numbers[left]				)
                  + prime_numbers[left] * loga(prime_numbers[right]				)
                  > upper_bound
              ):
                     right -= 1
              hybrid_integers_count += right - left
              left += 1

       return hybrid_integers_count


if __name__ == "__main__":
 print(F"{solution() = }")
