'''simple docstring'''




import unittest

import numpy as np

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

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 ViTImageProcessor




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       def __init__(      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE=7    ,     _SCREAMING_SNAKE_CASE=3    ,     _SCREAMING_SNAKE_CASE=18    ,     _SCREAMING_SNAKE_CASE=30    ,     _SCREAMING_SNAKE_CASE=400    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=None    ,     _SCREAMING_SNAKE_CASE=True    ,     _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,     _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]    ,     )		->     Optional[int]:
           A_     =   size if size is not None else {'height': 18, 'width': 18}
           A_     =   parent
           A_     =   batch_size
           A_     =   num_channels
           A_     =   image_size
           A_     =   min_resolution
           A_     =   max_resolution
           A_     =   do_resize
           A_     =   size
           A_     =   do_normalize
           A_     =   image_mean
           A_     =   image_std

       def 					__A       (      self )		->     int:
           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 	__UpperCAmelCase				(				snake_case_						,      unittest.TestCase     ):





       '''simple docstring'''



       __lowercase					:					Any    =				ViTImageProcessor if is_vision_available() else None

       def 					__A       (      self )		->     Tuple:
           A_     =   ViTImageProcessingTester(self )

       @property
       def 					__A       (      self )		->     Optional[int]:
           return self.image_processor_tester.prepare_image_processor_dict()

       def 					__A       (      self )		->     Union[str, Any]:
           A_     =   self.image_processing_class(**self.image_processor_dict )
           self.assertTrue(hasattr(lowerCamelCase_    ,     '''image_mean''' ) )
           self.assertTrue(hasattr(lowerCamelCase_    ,     '''image_std''' ) )
           self.assertTrue(hasattr(lowerCamelCase_    ,     '''do_normalize''' ) )
           self.assertTrue(hasattr(lowerCamelCase_    ,     '''do_resize''' ) )
           self.assertTrue(hasattr(lowerCamelCase_    ,     '''size''' ) )

       def 					__A       (      self )		->     str:
           A_     =   self.image_processing_class.from_dict(self.image_processor_dict )
           self.assertEqual(image_processor.size    ,     {'''height''': 18, '''width''': 18} )

           A_     =   self.image_processing_class.from_dict(self.image_processor_dict    ,     size=42 )
           self.assertEqual(image_processor.size    ,     {'''height''': 42, '''width''': 42} )

       def 					__A       (      self )		->     Optional[Any]:
           pass

       def 					__A       (      self )		->     Tuple:
           A_     =   self.image_processing_class(**self.image_processor_dict )
           # create random PIL images
           A_     =   prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowerCamelCase_ )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase_    ,     Image.Image )

           # Test not batched input
           A_     =   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
           A_     =   image_processing(lowerCamelCase_    ,     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 					__A       (      self )		->     Union[str, Any]:
           A_     =   self.image_processing_class(**self.image_processor_dict )
           # create random numpy tensors
           A_     =   prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowerCamelCase_    ,     numpify=lowerCamelCase_ )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase_    ,     np.ndarray )

           # Test not batched input
           A_     =   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
           A_     =   image_processing(lowerCamelCase_    ,     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 					__A       (      self )		->     Union[str, Any]:
           A_     =   self.image_processing_class(**self.image_processor_dict )
           # create random PyTorch tensors
           A_     =   prepare_image_inputs(self.image_processor_tester    ,     equal_resolution=lowerCamelCase_    ,     torchify=lowerCamelCase_ )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase_    ,     torch.Tensor )

           # Test not batched input
           A_     =   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
           A_     =   image_processing(lowerCamelCase_    ,     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'''],
               )    ,     )














"""simple docstring"""
import os






def        lowerCamelCase_  (						):
 lowerCamelCase__     :			List[Any]     				=						os.path.dirname(os.path.realpath(_lowerCamelCase				)				)
 lowerCamelCase__     :			Any     				=						os.path.join(_lowerCamelCase			,   'triangle.txt'				)

 with open(_lowerCamelCase				) as in_file:
  lowerCamelCase__     :			Dict     				=						[[int(_lowerCamelCase				) for i in line.split()] for line in in_file]

 while len(_lowerCamelCase				) != 1:
  lowerCamelCase__     :			Dict     				=						triangle.pop()
  lowerCamelCase__     :			Optional[Any]     				=						triangle[-1]
  for j in range(len(_lowerCamelCase				) - 1				):
   curr_row[j] += max(last_row[j]			,   last_row[j + 1]				)
 return triangle[0][0]


if __name__ == "__main__":
      print(solution())

