'''simple docstring'''

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 							lowercase						(						__lowerCamelCase ):
   lowerCamelCase_    					=['image_processor']
   lowerCamelCase_    					='SamImageProcessor'


   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	str)      ->					List[str]:
         super().__init__(__lowerCAmelCase)
         lowercase_     =      self.image_processor
         lowercase_     =      -10
         lowercase_     =      self.image_processor.size["longest_edge"]


   def __call__(   self				:	int   ,		__lowerCAmelCase				:	Dict=None   ,		__lowerCAmelCase				:	Any=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	int=None   ,		__lowerCAmelCase				:	Optional[Union[str, TensorType]] = None   ,		**__lowerCAmelCase				:	List[Any]   ,		)      ->					BatchEncoding:
         lowercase_     =      self.image_processor(
             __lowerCAmelCase   ,		return_tensors=__lowerCAmelCase   ,		**__lowerCAmelCase   ,		)

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

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

         lowercase_ ,							lowercase_ ,							lowercase_     =      self._check_and_preprocess_points(
             input_points=__lowerCAmelCase   ,		input_labels=__lowerCAmelCase   ,		input_boxes=__lowerCAmelCase   ,		)

         lowercase_     =      self._normalize_and_convert(
             __lowerCAmelCase   ,		__lowerCAmelCase   ,		input_points=__lowerCAmelCase   ,		input_labels=__lowerCAmelCase   ,		input_boxes=__lowerCAmelCase   ,		return_tensors=__lowerCAmelCase   ,		)

         return encoding_image_processor


   def lowerCamelCase__       (   self				:	List[str]   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Optional[Any]=None   ,		__lowerCAmelCase				:	List[str]=None   ,		__lowerCAmelCase				:	Union[str, Any]=None   ,		__lowerCAmelCase				:	List[str]="pt"   ,		)      ->					str:
         if input_points is not None:
               if len(__lowerCAmelCase) != len(__lowerCAmelCase):
                     lowercase_     =      [
                         self._normalize_coordinates(self.target_size   ,		__lowerCAmelCase   ,		original_sizes[0]) for point in input_points
                     ]
               else:
                     lowercase_     =      [
                         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:
                           lowercase_ ,							lowercase_     =      self._pad_points_and_labels(__lowerCAmelCase   ,		__lowerCAmelCase)

               lowercase_     =      np.array(__lowerCAmelCase)

         if input_labels is not None:
               lowercase_     =      np.array(__lowerCAmelCase)

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

         if input_boxes is not None:
               if return_tensors == "pt":
                     lowercase_     =      torch.from_numpy(__lowerCAmelCase)
                     # boxes batch size of 1 by default
                     lowercase_     =      input_boxes.unsqueeze(1) if len(input_boxes.shape) != 3 else input_boxes
               elif return_tensors == "tf":
                     lowercase_     =      tf.convert_to_tensor(__lowerCAmelCase)
                     # boxes batch size of 1 by default
                     lowercase_     =      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":
                     lowercase_     =      torch.from_numpy(__lowerCAmelCase)
                     # point batch size of 1 by default
                     lowercase_     =      input_points.unsqueeze(1) if len(input_points.shape) != 4 else input_points
               elif return_tensors == "tf":
                     lowercase_     =      tf.convert_to_tensor(__lowerCAmelCase)
                     # point batch size of 1 by default
                     lowercase_     =      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":
                     lowercase_     =      torch.from_numpy(__lowerCAmelCase)
                     # point batch size of 1 by default
                     lowercase_     =      input_labels.unsqueeze(1) if len(input_labels.shape) != 3 else input_labels
               elif return_tensors == "tf":
                     lowercase_     =      tf.convert_to_tensor(__lowerCAmelCase)
                     # point batch size of 1 by default
                     lowercase_     =      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 lowerCamelCase__       (   self				:	str   ,		__lowerCAmelCase				:	Optional[Any]   ,		__lowerCAmelCase				:	Union[str, Any])      ->					Dict:
         lowercase_     =      max([point.shape[0] for point in input_points])
         lowercase_     =      []
         for i, point in enumerate(__lowerCAmelCase):
               if point.shape[0] != expected_nb_points:
                     lowercase_     =      np.concatenate(
                         [point, np.zeros((expected_nb_points - point.shape[0], 2)) + self.point_pad_value]   ,		axis=0)
                     lowercase_     =      np.append(input_labels[i]   ,		[self.point_pad_value])
               processed_input_points.append(__lowerCAmelCase)
         lowercase_     =      processed_input_points
         return input_points, input_labels


   def lowerCamelCase__       (   self				:	Union[str, Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	np.ndarray   ,		__lowerCAmelCase				:	Dict   ,		__lowerCAmelCase				:	Tuple=False)      ->					np.ndarray:
         lowercase_ ,							lowercase_     =      original_size
         lowercase_ ,							lowercase_     =      self.image_processor._get_preprocess_shape(__lowerCAmelCase   ,		longest_edge=__lowerCAmelCase)
         lowercase_     =      deepcopy(__lowerCAmelCase).astype(__lowerCAmelCase)

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

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

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

         return coords


   def lowerCamelCase__       (   self				:	Dict   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Tuple=None   ,		__lowerCAmelCase				:	int=None   ,		)      ->					int:
         if input_points is not None:
               if hasattr(__lowerCAmelCase   ,		"numpy"):  # Checks for TF or Torch tensor
                     lowercase_     =      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.")
               lowercase_     =      [np.array(__lowerCAmelCase) for input_point in input_points]
         else:
               lowercase_     =      None

         if input_labels is not None:
               if hasattr(__lowerCAmelCase   ,		"numpy"):
                     lowercase_     =      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.")
               lowercase_     =      [np.array(__lowerCAmelCase) for label in input_labels]
         else:
               lowercase_     =      None

         if input_boxes is not None:
               if hasattr(__lowerCAmelCase   ,		"numpy"):
                     lowercase_     =      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.")
               lowercase_     =      [np.array(__lowerCAmelCase).astype(np.floataa) for box in input_boxes]
         else:
               lowercase_     =      None

         return input_points, input_labels, input_boxes


   @property
   def lowerCamelCase__       (   self				:	Optional[Any])      ->					List[str]:
         lowercase_     =      self.image_processor.model_input_names
         return list(dict.fromkeys(__lowerCAmelCase))


   def lowerCamelCase__       (   self				:	Any   ,		*__lowerCAmelCase				:	List[Any]   ,		**__lowerCAmelCase				:	int)      ->					Union[str, Any]:
         return self.image_processor.post_process_masks(*__lowerCAmelCase   ,		**__lowerCAmelCase)







'''simple docstring'''

from __future__ import annotations


def __a (	__lowerCamelCase			:						int				)	->							list[int]:
      '''simple docstring'''


      lowercase_     =      [True] * limit
      lowercase_     =      False
      lowercase_     =      False
      lowercase_     =      True

      for i in range(3 ,		int(limit**0.5 + 1				) ,		2				):
            lowercase_     =      i * 2
            while index < limit:
                  lowercase_     =      False
                  lowercase_     =      index + i

      lowercase_     =      [2]

      for i in range(3 ,		__lowerCamelCase ,		2				):
            if is_prime[i]:
                  primes.append(__lowerCamelCase				)

      return primes


def __a (	__lowerCamelCase			:						int = 1_000_000				)	->							int:
      '''simple docstring'''


      lowercase_     =      prime_sieve(__lowerCamelCase				)
      lowercase_     =      0
      lowercase_     =      0

      for i in range(len(__lowerCamelCase				)				):
            for j in range(i + length ,		len(__lowerCamelCase				)				):
                  lowercase_     =      sum(primes[i:j]				)
                  if sol >= ceiling:
                        break

                  if sol in primes:
                        lowercase_     =      j - i
                        lowercase_     =      sol

      return largest


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






