





import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import DPTImageProcessor



class       A__     ( unittest.TestCase    ):





      def __init__(				self ,							A_ ,							A_=7 ,							A_=3 ,							A_=18 ,							A_=30 ,							A_=400 ,							A_=True ,							A_=None ,							A_=True ,							A_=[0.5, 0.5, 0.5] ,							A_=[0.5, 0.5, 0.5] ,							):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     size if size is not None else {"height": 18, "width": 18}
       UpperCamelCase			:      Tuple 			=     parent
       UpperCamelCase			:      List[str] 			=     batch_size
       UpperCamelCase			:      List[Any] 			=     num_channels
       UpperCamelCase			:      List[str] 			=     image_size
       UpperCamelCase			:      Any 			=     min_resolution
       UpperCamelCase			:      Tuple 			=     max_resolution
       UpperCamelCase			:      Dict 			=     do_resize
       UpperCamelCase			:      Optional[int] 			=     size
       UpperCamelCase			:      Union[str, Any] 			=     do_normalize
       UpperCamelCase			:      Union[str, Any] 			=     image_mean
       UpperCamelCase			:      List[str] 			=     image_std





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return {
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_normalize": self.do_normalize,
           "do_resize": self.do_resize,
           "size": self.size,
       }



@require_torch
@require_vision
class       A__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:Optional[Any]							=     DPTImageProcessor if is_vision_available() else None





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     DPTImageProcessingTester(self     )





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       return self.image_processor_tester.prepare_image_processor_dict()





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Tuple 			=     self.image_processing_class(**self.image_processor_dict     )
       self.assertTrue(hasattr(A_ ,							"image_mean"     )     )
       self.assertTrue(hasattr(A_ ,							"image_std"     )     )
       self.assertTrue(hasattr(A_ ,							"do_normalize"     )     )
       self.assertTrue(hasattr(A_ ,							"do_resize"     )     )
       self.assertTrue(hasattr(A_ ,							"size"     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.image_processing_class.from_dict(self.image_processor_dict     )
       self.assertEqual(image_processor.size ,							{"height": 18, "width": 18}     )

       UpperCamelCase			:      str 			=     self.image_processing_class.from_dict(self.image_processor_dict ,							size=42     )
       self.assertEqual(image_processor.size ,							{"height": 42, "width": 42}     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PIL images
       UpperCamelCase			:      Any 			=     prepare_image_inputs(self.image_processor_tester ,							equal_resolution=A_     )
       for image in image_inputs:
        self.assertIsInstance(A_ ,							Image.Image     )

       # Test not batched input
       UpperCamelCase			:      List[str] 			=     image_processing(image_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_images.shape ,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      Union[str, Any] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_images.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random numpy tensors
       UpperCamelCase			:      Optional[Any] 			=     prepare_image_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							numpify=A_     )
       for image in image_inputs:
        self.assertIsInstance(A_ ,							np.ndarray     )

       # Test not batched input
       UpperCamelCase			:      Tuple 			=     image_processing(image_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_images.shape ,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      List[str] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_images.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           ) ,							)





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.image_processing_class(**self.image_processor_dict     )
       # create random PyTorch tensors
       UpperCamelCase			:      Any 			=     prepare_image_inputs(self.image_processor_tester ,							equal_resolution=A_ ,							torchify=A_     )
       for image in image_inputs:
        self.assertIsInstance(A_ ,							torch.Tensor     )

       # Test not batched input
       UpperCamelCase			:      List[Any] 			=     image_processing(image_inputs[0] ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_images.shape ,							(
               1,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           ) ,							)

       # Test batched
       UpperCamelCase			:      List[str] 			=     image_processing(A_ ,							return_tensors="pt"     ).pixel_values
       self.assertEqual(
           encoded_images.shape ,							(
               self.image_processor_tester.batch_size,
               self.image_processor_tester.num_channels,
               self.image_processor_tester.size["height"],
               self.image_processor_tester.size["width"],
           ) ,							)







from __future__ import annotations



def 	A_       (  _lowerCAmelCase  )		->				list[int]:
 UpperCamelCase			:      Optional[Any] 			=     [True] * limit
 UpperCamelCase			:      Optional[Any] 			=     False
 UpperCamelCase			:      List[str] 			=     False
 UpperCamelCase			:      Tuple 			=     True

 for i in range(3							,     int(limit**0.5 + 1  )							,     2  ):
  UpperCamelCase			:      Optional[Any] 			=     i * 2
  while index < limit:
   UpperCamelCase			:      int 			=     False
   UpperCamelCase			:      Optional[int] 			=     index + i

 UpperCamelCase			:      Any 			=     [2]

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

 return primes



def 	A_       (  _lowerCAmelCase = 100_0000  )		->				int:
 UpperCamelCase			:      Union[str, Any] 			=     prime_sieve(_lowerCAmelCase  )
 UpperCamelCase			:      List[str] 			=     0
 UpperCamelCase			:      Union[str, Any] 			=     0

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

   if sol in primes:
    UpperCamelCase			:      int 			=     j - i
    UpperCamelCase			:      Dict 			=     sol

 return largest


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