



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    SCREAMING_SNAKE_CASE			(							unittest.TestCase					):





							def __init__(   self		:		Optional[int]      ,				__lowercase		:		Tuple      ,				__lowercase		:		List[Any]=13      ,				__lowercase		:		Dict=3      ,				__lowercase		:		Any=224      ,				__lowercase		:		Union[str, Any]=30      ,				__lowercase		:		Any=400      ,				__lowercase		:		List[Any]=True      ,				__lowercase		:		Dict=None      ,				__lowercase		:		Dict=True      ,				__lowercase		:		Union[str, Any]=[0.5, 0.5, 0.5]      ,				__lowercase		:		List[Any]=[0.5, 0.5, 0.5]      ,				):

											'''simple docstring'''
											__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 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''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    SCREAMING_SNAKE_CASE			(							_a			,      unittest.TestCase					):
							__lowerCamelCase	:	int    =ViTImageProcessor if is_vision_available() else None





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

											'''simple docstring'''
											__a       =		EfficientFormerImageProcessorTester(self	)





							@property
							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											return self.image_proc_tester.prepare_image_processor_dict()





							def 						UpperCamelCase_					(   self		:		Any	):

											'''simple docstring'''
											__a       =		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		:		Union[str, Any]	):

											'''simple docstring'''
											pass





							def 						UpperCamelCase_					(   self		:		Tuple	):

											'''simple docstring'''
											# Initialize image_processor
											__a       =		self.image_processing_class(**self.image_processor_dict	)
											# create random PIL images
											__a       =		prepare_image_inputs(self.image_proc_tester      ,				equal_resolution=_A	)
											for image in image_inputs:
															self.assertIsInstance(_A      ,				Image.Image	)

											# Test not batched input
											__a       =		image_processor(image_inputs[0]      ,				return_tensors="""pt"""	).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,				(
											        1,
											        self.image_proc_tester.num_channels,
											        self.image_proc_tester.size["""height"""],
											        self.image_proc_tester.size["""width"""],
											    )      ,				)

											# Test batched
											__a       =		image_processor(_A      ,				return_tensors="""pt"""	).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,				(
											        self.image_proc_tester.batch_size,
											        self.image_proc_tester.num_channels,
											        self.image_proc_tester.size["""height"""],
											        self.image_proc_tester.size["""width"""],
											    )      ,				)





							def 						UpperCamelCase_					(   self		:		Optional[Any]	):

											'''simple docstring'''
											# Initialize image_processor
											__a       =		self.image_processing_class(**self.image_processor_dict	)
											# create random numpy tensors
											__a       =		prepare_image_inputs(self.image_proc_tester      ,				equal_resolution=_A      ,				numpify=_A	)
											for image in image_inputs:
															self.assertIsInstance(_A      ,				np.ndarray	)

											# Test not batched input
											__a       =		image_processor(image_inputs[0]      ,				return_tensors="""pt"""	).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,				(
											        1,
											        self.image_proc_tester.num_channels,
											        self.image_proc_tester.size["""height"""],
											        self.image_proc_tester.size["""width"""],
											    )      ,				)

											# Test batched
											__a       =		image_processor(_A      ,				return_tensors="""pt"""	).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,				(
											        self.image_proc_tester.batch_size,
											        self.image_proc_tester.num_channels,
											        self.image_proc_tester.size["""height"""],
											        self.image_proc_tester.size["""width"""],
											    )      ,				)







							def 						UpperCamelCase_					(   self		:		List[Any]	):

											'''simple docstring'''
											# Initialize image_processor
											__a       =		self.image_processing_class(**self.image_processor_dict	)
											# create random PyTorch tensors
											__a       =		prepare_image_inputs(self.image_proc_tester      ,				equal_resolution=_A      ,				torchify=_A	)
											for image in image_inputs:
															self.assertIsInstance(_A      ,				torch.Tensor	)

											# Test not batched input
											__a       =		image_processor(image_inputs[0]      ,				return_tensors="""pt"""	).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,				(
											        1,
											        self.image_proc_tester.num_channels,
											        self.image_proc_tester.size["""height"""],
											        self.image_proc_tester.size["""width"""],
											    )      ,				)

											# Test batched
											__a       =		image_processor(_A      ,				return_tensors="""pt"""	).pixel_values
											self.assertEqual(
											    encoded_images.shape      ,				(
											        self.image_proc_tester.batch_size,
											        self.image_proc_tester.num_channels,
											        self.image_proc_tester.size["""height"""],
											        self.image_proc_tester.size["""width"""],
											    )      ,				)





import os

def   lowerCAmelCase__						(							):
				"""simple docstring"""







				__a       =		os.path.dirname(os.path.realpath(_SCREAMING_SNAKE_CASE    )    )
				__a       =		os.path.join(_SCREAMING_SNAKE_CASE				,     """triangle.txt"""    )

				with open(_SCREAMING_SNAKE_CASE    ) as f:
								__a       =		f.readlines()

				__a       =		[]
				for line in triangle:
								__a       =		[]
								for number in line.strip().split(""" """    ):
												numbers_from_line.append(int(_SCREAMING_SNAKE_CASE    )    )
								a.append(_SCREAMING_SNAKE_CASE    )

				for i in range(1				,     len(_SCREAMING_SNAKE_CASE    )    ):
								for j in range(len(a[i]    )    ):
												__a       =		a[i - 1][j] if j != len(a[i - 1]    ) else 0
												__a       =		a[i - 1][j - 1] if j > 0 else 0
												a[i][j] += max(_SCREAMING_SNAKE_CASE				,     _SCREAMING_SNAKE_CASE    )
				return max(a[-1]    )


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