




from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
  import torch

if is_tf_available():
  import tensorflow as tf
class 	_UpperCamelCase					( UpperCamelCase__						):
      UpperCAmelCase_						=						["""image_processor"""]
      UpperCAmelCase_						=						"""SamImageProcessor"""
      def __init__(	self      :Tuple       ,					lowerCamelCase      :int					)   ->	str:
       super().__init__(__lowerCamelCase					)
       UpperCAmelCase__							       =  self.image_processor
       UpperCAmelCase__							       =  -10
       UpperCAmelCase__							       =  self.image_processor.size["longest_edge"]
      def __call__(	self      :int       ,					lowerCamelCase      :Union[str, Any]=None       ,					lowerCamelCase      :Dict=None       ,					lowerCamelCase      :Tuple=None       ,					lowerCamelCase      :Tuple=None       ,					lowerCamelCase      :Union[str, Any] = None       ,					**lowerCamelCase      :Any       ,					)   ->	List[str]:
       UpperCAmelCase__							       =  self.image_processor(
           __lowerCamelCase       ,					return_tensors=__lowerCamelCase       ,					**__lowerCamelCase       ,					)

       # pop arguments that are not used in the foward but used nevertheless
       UpperCAmelCase__							       =  encoding_image_processor["original_sizes"]

       if hasattr(__lowerCamelCase       ,					"numpy"					):  # Checks if Torch or TF tensor
        UpperCAmelCase__							       =  original_sizes.numpy()

       UpperCAmelCase__       ,       UpperCAmelCase__       ,       UpperCAmelCase__							       =  self._check_and_preprocess_points(
           input_points=__lowerCamelCase       ,					input_labels=__lowerCamelCase       ,					input_boxes=__lowerCamelCase       ,					)

       UpperCAmelCase__							       =  self._normalize_and_convert(
           __lowerCamelCase       ,					__lowerCamelCase       ,					input_points=__lowerCamelCase       ,					input_labels=__lowerCamelCase       ,					input_boxes=__lowerCamelCase       ,					return_tensors=__lowerCamelCase       ,					)

       return encoding_image_processor
      def 		UpperCAmelCase_  (	self      :List[Any]       ,					lowerCamelCase      :List[Any]       ,					lowerCamelCase      :Any       ,					lowerCamelCase      :Any=None       ,					lowerCamelCase      :List[Any]=None       ,					lowerCamelCase      :Optional[Any]=None       ,					lowerCamelCase      :Dict="pt"       ,					)   ->	Dict:
       if input_points is not None:
        if len(__lowerCamelCase					) != len(__lowerCamelCase					):
         UpperCAmelCase__							       =  [
             self._normalize_coordinates(self.target_size       ,					__lowerCamelCase       ,					original_sizes[0]					) for point in input_points
         ]
        else:
         UpperCAmelCase__							       =  [
             self._normalize_coordinates(self.target_size       ,					__lowerCamelCase       ,					__lowerCamelCase					)
             for point, original_size in zip(__lowerCamelCase       ,					__lowerCamelCase					)
         ]
        # check that all arrays have the same shape
        if not all(point.shape == input_points[0].shape for point in input_points					):
         if input_labels is not None:
          UpperCAmelCase__       ,       UpperCAmelCase__							       =  self._pad_points_and_labels(__lowerCamelCase       ,					__lowerCamelCase					)

        UpperCAmelCase__							       =  np.array(__lowerCamelCase					)

       if input_labels is not None:
        UpperCAmelCase__							       =  np.array(__lowerCamelCase					)

       if input_boxes is not None:
        if len(__lowerCamelCase					) != len(__lowerCamelCase					):
         UpperCAmelCase__							       =  [
             self._normalize_coordinates(self.target_size       ,					__lowerCamelCase       ,					original_sizes[0]       ,					is_bounding_box=__lowerCamelCase					)
             for box in input_boxes
         ]
        else:
         UpperCAmelCase__							       =  [
             self._normalize_coordinates(self.target_size       ,					__lowerCamelCase       ,					__lowerCamelCase       ,					is_bounding_box=__lowerCamelCase					)
             for box, original_size in zip(__lowerCamelCase       ,					__lowerCamelCase					)
         ]
        UpperCAmelCase__							       =  np.array(__lowerCamelCase					)

       if input_boxes is not None:
        if return_tensors == "pt":
         UpperCAmelCase__							       =  torch.from_numpy(__lowerCamelCase					)
         # boxes batch size of 1 by default
         UpperCAmelCase__							       =  input_boxes.unsqueeze(1					) if len(input_boxes.shape					) != 3 else input_boxes
        elif return_tensors == "tf":
         UpperCAmelCase__							       =  tf.convert_to_tensor(__lowerCamelCase					)
         # boxes batch size of 1 by default
         UpperCAmelCase__							       =  tf.expand_dims(__lowerCamelCase       ,					1					) if len(input_boxes.shape					) != 3 else input_boxes
        encoding_image_processor.update({"input_boxes": input_boxes}					)
       if input_points is not None:
        if return_tensors == "pt":
         UpperCAmelCase__							       =  torch.from_numpy(__lowerCamelCase					)
         # point batch size of 1 by default
         UpperCAmelCase__							       =  input_points.unsqueeze(1					) if len(input_points.shape					) != 4 else input_points
        elif return_tensors == "tf":
         UpperCAmelCase__							       =  tf.convert_to_tensor(__lowerCamelCase					)
         # point batch size of 1 by default
         UpperCAmelCase__							       =  tf.expand_dims(__lowerCamelCase       ,					1					) if len(input_points.shape					) != 4 else input_points
        encoding_image_processor.update({"input_points": input_points}					)
       if input_labels is not None:
        if return_tensors == "pt":
         UpperCAmelCase__							       =  torch.from_numpy(__lowerCamelCase					)
         # point batch size of 1 by default
         UpperCAmelCase__							       =  input_labels.unsqueeze(1					) if len(input_labels.shape					) != 3 else input_labels
        elif return_tensors == "tf":
         UpperCAmelCase__							       =  tf.convert_to_tensor(__lowerCamelCase					)
         # point batch size of 1 by default
         UpperCAmelCase__							       =  tf.expand_dims(__lowerCamelCase       ,					1					) if len(input_labels.shape					) != 3 else input_labels
        encoding_image_processor.update({"input_labels": input_labels}					)

       return encoding_image_processor
      def 		UpperCAmelCase_  (	self      :Dict       ,					lowerCamelCase      :Optional[int]       ,					lowerCamelCase      :Dict					)   ->	List[str]:
       UpperCAmelCase__							       =  max([point.shape[0] for point in input_points]					)
       UpperCAmelCase__							       =  []
       for i, point in enumerate(__lowerCamelCase					):
        if point.shape[0] != expected_nb_points:
         UpperCAmelCase__							       =  np.concatenate(
             [point, np.zeros((expected_nb_points - point.shape[0], 2)					) + self.point_pad_value]       ,					axis=0					)
         UpperCAmelCase__							       =  np.append(input_labels[i]       ,					[self.point_pad_value]					)
        processed_input_points.append(__lowerCamelCase					)
       UpperCAmelCase__							       =  processed_input_points
       return input_points, input_labels
      def 		UpperCAmelCase_  (	self      :int       ,					lowerCamelCase      :Tuple       ,					lowerCamelCase      :Optional[Any]       ,					lowerCamelCase      :List[str]       ,					lowerCamelCase      :Any=False					)   ->	str:
       UpperCAmelCase__       ,       UpperCAmelCase__							       =  original_size
       UpperCAmelCase__       ,       UpperCAmelCase__							       =  self.image_processor._get_preprocess_shape(__lowerCamelCase       ,					longest_edge=__lowerCamelCase					)
       UpperCAmelCase__							       =  deepcopy(__lowerCamelCase					).astype(__lowerCamelCase					)

       if is_bounding_box:
        UpperCAmelCase__							       =  coords.reshape(-1       ,					2       ,					2					)

       UpperCAmelCase__							       =  coords[..., 0] * (new_w / old_w)
       UpperCAmelCase__							       =  coords[..., 1] * (new_h / old_h)

       if is_bounding_box:
        UpperCAmelCase__							       =  coords.reshape(-1       ,					4					)

       return coords
      def 		UpperCAmelCase_  (	self      :List[str]       ,					lowerCamelCase      :int=None       ,					lowerCamelCase      :Dict=None       ,					lowerCamelCase      :Tuple=None       ,					)   ->	Tuple:
       if input_points is not None:
        if hasattr(__lowerCamelCase       ,					"numpy"					):  # Checks for TF or Torch tensor
         UpperCAmelCase__							       =  input_points.numpy().tolist()

        if not isinstance(__lowerCamelCase       ,					__lowerCamelCase					) or not isinstance(input_points[0]       ,					__lowerCamelCase					):
         raise ValueError("Input points must be a list of list of floating points."					)
        UpperCAmelCase__							       =  [np.array(__lowerCamelCase					) for input_point in input_points]
       else:
        UpperCAmelCase__							       =  None

       if input_labels is not None:
        if hasattr(__lowerCamelCase       ,					"numpy"					):
         UpperCAmelCase__							       =  input_labels.numpy().tolist()

        if not isinstance(__lowerCamelCase       ,					__lowerCamelCase					) or not isinstance(input_labels[0]       ,					__lowerCamelCase					):
         raise ValueError("Input labels must be a list of list integers."					)
        UpperCAmelCase__							       =  [np.array(__lowerCamelCase					) for label in input_labels]
       else:
        UpperCAmelCase__							       =  None

       if input_boxes is not None:
        if hasattr(__lowerCamelCase       ,					"numpy"					):
         UpperCAmelCase__							       =  input_boxes.numpy().tolist()

        if (
            not isinstance(__lowerCamelCase       ,					__lowerCamelCase					)
            or not isinstance(input_boxes[0]       ,					__lowerCamelCase					)
            or not isinstance(input_boxes[0][0]       ,					__lowerCamelCase					)
        ):
         raise ValueError("Input boxes must be a list of list of list of floating points."					)
        UpperCAmelCase__							       =  [np.array(__lowerCamelCase					).astype(np.floataa					) for box in input_boxes]
       else:
        UpperCAmelCase__							       =  None

       return input_points, input_labels, input_boxes
      @property
      def 		UpperCAmelCase_  (	self      :Any					)   ->	Optional[Any]:
       UpperCAmelCase__							       =  self.image_processor.model_input_names
       return list(dict.fromkeys(__lowerCamelCase					)					)







      def 		UpperCAmelCase_  (	self      :Union[str, Any]       ,					*lowerCamelCase      :Dict       ,					**lowerCamelCase      :Dict					)   ->	int:
       return self.image_processor.post_process_masks(*__lowerCamelCase       ,					**__lowerCamelCase					)












from __future__ import annotations

import math


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->bool:

       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(UpperCamelCase__  ) + 1  ) ,     6  ):
              if number % i == 0 or number % (i + 2) == 0:
                     return False
       return True


def 	UpperCamelCase__(       UpperCamelCase__			:       int  )->list[int]:
       A__											=						str(UpperCamelCase__  )
       A__											=						[n]
       for i in range(1 ,     len(UpperCamelCase__  )  ):
              list_nums.append(int(str_num[i:]  )  )
              list_nums.append(int(str_num[:-i]  )  )
       return list_nums


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


def 	UpperCamelCase__(       UpperCamelCase__			:       int = 11  )->list[int]:
       A__											=						[]
       A__											=						13
       while len(UpperCamelCase__  ) != count:
              if validate(UpperCamelCase__  ):
                     A__											=						list_truncated_nums(UpperCamelCase__  )
                     if all(is_prime(UpperCamelCase__  ) for i in list_nums  ):
                            list_truncated_primes.append(UpperCamelCase__  )
              num += 2
       return list_truncated_primes


def 	UpperCamelCase__(       )->int:
       return sum(compute_truncated_primes(11  )  )


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





