




'''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 DeiTImageProcessor






class      UpperCamelCase_  (unittest.TestCase  ):







						"""simple docstring"""


						def __init__(  self   :							int				,			_lowerCamelCase   :							Any				,			_lowerCamelCase   :							Union[str, Any]=7				,			_lowerCamelCase   :							int=3				,			_lowerCamelCase   :							Dict=18				,			_lowerCamelCase   :							str=30				,			_lowerCamelCase   :							Tuple=400				,			_lowerCamelCase   :							str=True				,			_lowerCamelCase   :							Dict=None				,			_lowerCamelCase   :							Dict=True				,			_lowerCamelCase   :							int=None				,			_lowerCamelCase   :							Optional[int]=True				,			_lowerCamelCase   :							Optional[Any]=[0.5, 0.5, 0.5]				,			_lowerCamelCase   :							int=[0.5, 0.5, 0.5]				,			):





											"""simple docstring"""







											A_			:  int			=				size if size is not None else {'''height''': 20, '''width''': 20}
											A_			:  Dict			=				crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

											A_			:  Optional[int]			=				parent
											A_			:  str			=				batch_size
											A_			:  Dict			=				num_channels
											A_			:  List[Any]			=				image_size
											A_			:  int			=				min_resolution
											A_			:  Optional[Any]			=				max_resolution
											A_			:  List[Any]			=				do_resize
											A_			:  List[str]			=				size
											A_			:  Dict			=				do_center_crop
											A_			:  List[Any]			=				crop_size
											A_			:  Optional[Any]			=				do_normalize
											A_			:  List[Any]			=				image_mean
											A_			:  Optional[Any]			=				image_std







						def    _a     (  self   :							Optional[Any]     ):





											"""simple docstring"""







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






@require_torch
@require_vision
class      UpperCamelCase_  (a__,   unittest.TestCase  ):







						"""simple docstring"""


						_lowerCAmelCase         =       DeiTImageProcessor if is_vision_available() else None
						_lowerCAmelCase         =       True







						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											A_			:  int			=				DeiTImageProcessingTester(self     )







						@property
						def    _a     (  self   :							Tuple     ):





											"""simple docstring"""







											return self.image_processor_tester.prepare_image_processor_dict()







						def    _a     (  self   :							int     ):





											"""simple docstring"""







											A_			:  Tuple			=				self.image_processing_class(**self.image_processor_dict     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''do_resize'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''size'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''do_center_crop'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''center_crop'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''do_normalize'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''image_mean'''     )     )
											self.assertTrue(hasattr(_lowerCamelCase				,			'''image_std'''     )     )







						def    _a     (  self   :							List[Any]     ):





											"""simple docstring"""







											A_			:  Optional[int]			=				self.image_processing_class.from_dict(self.image_processor_dict     )
											self.assertEqual(image_processor.size				,			{'''height''': 20, '''width''': 20}     )
											self.assertEqual(image_processor.crop_size				,			{'''height''': 18, '''width''': 18}     )

											A_			:  int			=				self.image_processing_class.from_dict(self.image_processor_dict				,			size=42				,			crop_size=84     )
											self.assertEqual(image_processor.size				,			{'''height''': 42, '''width''': 42}     )
											self.assertEqual(image_processor.crop_size				,			{'''height''': 84, '''width''': 84}     )







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											pass







						def    _a     (  self   :							Optional[int]     ):





											"""simple docstring"""







											A_			:  Tuple			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PIL images
											A_			:  Any			=				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_			:  int			=				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.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,			)

											# Test batched
											A_			:  Optional[Any]			=				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.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,			)







						def    _a     (  self   :							Union[str, Any]     ):





											"""simple docstring"""







											A_			:  str			=				self.image_processing_class(**self.image_processor_dict     )
											# create random numpy tensors
											A_			:  List[str]			=				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_			:  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.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,			)

											# Test batched
											A_			:  List[Any]			=				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.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,			)







						def    _a     (  self   :							Any     ):





											"""simple docstring"""







											A_			:  int			=				self.image_processing_class(**self.image_processor_dict     )
											# create random PyTorch tensors
											A_			:  List[Any]			=				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_			:  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.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,			)

											# Test batched
											A_			:  Any			=				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.crop_size['''height'''],
											        self.image_processor_tester.crop_size['''width'''],
											    )				,			)









'''simple docstring'''




import os




def     snake_case__     (			) -> int:
					A_			:  List[str]			=				os.path.dirname(os.path.realpath(lowerCamelCase__      )      )
					A_			:  str			=				os.path.join(lowerCamelCase__      ,		'''triangle.txt'''      )

					with open(lowerCamelCase__      ) as in_file:
										A_			:  List[str]			=				[[int(lowerCamelCase__      ) for i in line.split()] for line in in_file]

					while len(lowerCamelCase__      ) != 1:
										A_			:  List[str]			=				triangle.pop()
										A_			:  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())



