
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

__magic_name__  							=	logging.get_logger(__name__)


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




@add_end_docstrings(__UpperCamelCase			)
class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      def __init__(      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          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 A_      (      self		,						**lowerCamelCase			):
          snake_case__    =					{}
          if "threshold" in kwargs:
              snake_case__    =					kwargs["threshold"]
          return {}, {}, postprocess_kwargs
      def __call__(      self		,						*lowerCamelCase		,						**lowerCamelCase			):
          return super().__call__(*lowerCamelCase		,						**lowerCamelCase			)
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					load_image(lowerCamelCase			)
          snake_case__    =					torch.IntTensor([[image.height, image.width]]			)
          snake_case__    =					self.image_processor(images=[image]		,						return_tensors="pt"			)
          if self.tokenizer is not None:
              snake_case__    =					self.tokenizer(text=inputs["words"]		,						boxes=inputs["boxes"]		,						return_tensors="pt"			)
          snake_case__    =					target_size
          return inputs
      def A_      (      self		,						lowerCamelCase			):
          snake_case__    =					model_inputs.pop("target_size"			)
          snake_case__    =					self.model(**lowerCamelCase			)
          snake_case__    =					outputs.__class__({"target_size": target_size, **outputs}			)
          if self.tokenizer is not None:
              snake_case__    =					model_inputs["bbox"]
          return model_outputs
      def A_      (      self		,						lowerCamelCase		,						lowerCamelCase=0.9			):
          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.
              snake_case__    ,       snake_case__    =					target_size[0].tolist()

              def unnormalize(lowerCamelCase			):
                  return self._get_bounding_box(
                      torch.Tensor(
                          [
                              (width * bbox[0] / 10_00),
                              (height * bbox[1] / 10_00),
                              (width * bbox[2] / 10_00),
                              (height * bbox[3] / 10_00),
                          ]			)			)

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

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

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

          return annotation



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





from __future__ import annotations

import math





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):

    if 1 < number < 4:
        # 2 and 3 are primes
        return True
    elif number < 2 or number % 2 == 0 or number % 3 == 0:
        # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
        return False

    # All primes number are in format of 6k +/- 1
    for i in range(5     ,		int(math.sqrt(__lowerCAmelCase				) + 1				)     ,		6				):
        if number % i == 0 or number % (i + 2) == 0:
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    snake_case__    =					str(__lowerCAmelCase				)
    snake_case__    =					[n]
    for i in range(1     ,		len(__lowerCAmelCase				)				):
        list_nums.append(int(str_num[i:]				)				)
        list_nums.append(int(str_num[:-i]				)				)
    return list_nums





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase				):
    if len(str(__lowerCAmelCase				)				) > 3:
        if not is_prime(int(str(__lowerCAmelCase				)[-3:]				)				) or not is_prime(int(str(__lowerCAmelCase				)[:3]				)				):
            return False
    return True





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase = 11				):
    snake_case__    =					[]
    snake_case__    =					13
    while len(__lowerCAmelCase				) != count:
        if validate(__lowerCAmelCase				):
            snake_case__    =					list_truncated_nums(__lowerCAmelCase				)
            if all(is_prime(__lowerCAmelCase				) for i in list_nums				):
                list_truncated_primes.append(__lowerCAmelCase				)
        num += 2
    return list_truncated_primes





def 						SCREAMING_SNAKE_CASE__     (   ):
    return sum(compute_truncated_primes(11				)				)


if __name__ == "__main__":
     print(F'''{sum(compute_truncated_primes(11)) = }''')



