

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)


if is_vision_available():
					import PIL

class  a     (		snake_case_ ):
						_lowercase			  =	['pixel_values']


						def __init__(				self  ,    A_ = True  ,    A_ = None  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = True  ,    A_ = None  ,    A_ = True  ,    A_ = 1 / 255  ,    A_ = True  ,    A_ = None  ,    A_ = None  ,    A_ = True  ,    **A_  ,    ):







										'''simple docstring'''


										super().__init__(**lowerCamelCase_		)
										_UpperCAmelCase				:						Dict       						=			size if size is not None else {'shortest_edge': 224}
										_UpperCAmelCase				:						Any       						=			get_size_dict(lowerCamelCase_  ,    default_to_square=lowerCamelCase_		)
										_UpperCAmelCase				:						List[Any]       						=			crop_size if crop_size is not None else {'height': 224, 'width': 224}
										_UpperCAmelCase				:						int       						=			get_size_dict(lowerCamelCase_		)

										_UpperCAmelCase				:						int       						=			do_resize
										_UpperCAmelCase				:						int       						=			size
										_UpperCAmelCase				:						Union[str, Any]       						=			resample
										_UpperCAmelCase				:						Optional[Any]       						=			do_center_crop
										_UpperCAmelCase				:						Tuple       						=			crop_size
										_UpperCAmelCase				:						str       						=			do_rescale
										_UpperCAmelCase				:						List[Any]       						=			rescale_factor
										_UpperCAmelCase				:						Optional[Any]       						=			do_normalize
										_UpperCAmelCase				:						Dict       						=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										_UpperCAmelCase				:						Union[str, Any]       						=			image_std if image_std is not None else OPENAI_CLIP_STD
										_UpperCAmelCase				:						int       						=			do_convert_rgb


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = PILImageResampling.BICUBIC  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			get_size_dict(lowerCamelCase_  ,    default_to_square=lowerCamelCase_		)
										_UpperCAmelCase				:						Any       						=			get_resize_output_image_size(
										    lowerCamelCase_  ,    size=(size["height"], size["width"])  ,    default_to_square=lowerCamelCase_		)
										return resize(lowerCamelCase_  ,    size=lowerCamelCase_  ,    resample=lowerCamelCase_  ,    data_format=lowerCamelCase_  ,    **lowerCamelCase_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						List[Any]       						=			get_size_dict(lowerCamelCase_		)
										return center_crop(lowerCamelCase_  ,    size=(size["height"], size["width"])  ,    data_format=lowerCamelCase_  ,    **lowerCamelCase_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										return rescale(lowerCamelCase_  ,    scale=lowerCamelCase_  ,    data_format=lowerCamelCase_  ,    **lowerCamelCase_		)


						def 				_UpperCAmelCase (				self  ,    A_  ,    A_  ,    A_  ,    A_ = None  ,    **A_  ,    ):







										'''simple docstring'''


										return normalize(lowerCamelCase_  ,    mean=lowerCamelCase_  ,    std=lowerCamelCase_  ,    data_format=lowerCamelCase_  ,    **lowerCamelCase_		)





						def 				_UpperCAmelCase (				self  ,    A_  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = None  ,    A_ = ChannelDimension.FIRST  ,    **A_  ,    ):







										'''simple docstring'''


										_UpperCAmelCase				:						Any       						=			do_resize if do_resize is not None else self.do_resize
										_UpperCAmelCase				:						Optional[int]       						=			size if size is not None else self.size
										_UpperCAmelCase				:						List[str]       						=			get_size_dict(lowerCamelCase_  ,    default_to_square=lowerCamelCase_		)
										_UpperCAmelCase				:						Any       						=			resample if resample is not None else self.resample
										_UpperCAmelCase				:						Tuple       						=			do_center_crop if do_center_crop is not None else self.do_center_crop
										_UpperCAmelCase				:						Dict       						=			crop_size if crop_size is not None else self.crop_size
										_UpperCAmelCase				:						List[Any]       						=			get_size_dict(lowerCamelCase_		)
										_UpperCAmelCase				:						Tuple       						=			do_rescale if do_rescale is not None else self.do_rescale
										_UpperCAmelCase				:						Union[str, Any]       						=			rescale_factor if rescale_factor is not None else self.rescale_factor
										_UpperCAmelCase				:						Tuple       						=			do_normalize if do_normalize is not None else self.do_normalize
										_UpperCAmelCase				:						Dict       						=			image_mean if image_mean is not None else self.image_mean
										_UpperCAmelCase				:						Optional[Any]       						=			image_std if image_std is not None else self.image_std
										_UpperCAmelCase				:						List[str]       						=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										_UpperCAmelCase				:						Optional[Any]       						=			make_list_of_images(lowerCamelCase_		)

										if not valid_images(lowerCamelCase_		):
														raise ValueError(
														    "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
														    "torch.Tensor, tf.Tensor or jax.ndarray."		)

										if do_resize and size is None:
														raise ValueError("Size must be specified if do_resize is True."		)

										if do_center_crop and crop_size is None:
														raise ValueError("Crop size must be specified if do_center_crop is True."		)

										if do_rescale and rescale_factor is None:
														raise ValueError("Rescale factor must be specified if do_rescale is True."		)

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("Image mean and std must be specified if do_normalize is True."		)

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
														_UpperCAmelCase				:						Any       						=			[convert_to_rgb(lowerCamelCase_		) for image in images]

										# All transformations expect numpy arrays.
										_UpperCAmelCase				:						Dict       						=			[to_numpy_array(lowerCamelCase_		) for image in images]

										if do_resize:
														_UpperCAmelCase				:						Union[str, Any]       						=			[self.resize(image=lowerCamelCase_  ,    size=lowerCamelCase_  ,    resample=lowerCamelCase_		) for image in images]

										if do_center_crop:
														_UpperCAmelCase				:						int       						=			[self.center_crop(image=lowerCamelCase_  ,    size=lowerCamelCase_		) for image in images]

										if do_rescale:
														_UpperCAmelCase				:						List[str]       						=			[self.rescale(image=lowerCamelCase_  ,    scale=lowerCamelCase_		) for image in images]

										if do_normalize:
														_UpperCAmelCase				:						Optional[Any]       						=			[self.normalize(image=lowerCamelCase_  ,    mean=lowerCamelCase_  ,    std=lowerCamelCase_		) for image in images]

										_UpperCAmelCase				:						str       						=			[to_channel_dimension_format(lowerCamelCase_  ,    lowerCamelCase_		) for image in images]

										_UpperCAmelCase				:						Optional[Any]       						=			{'pixel_values': images}
										return BatchFeature(data=lowerCamelCase_  ,    tensor_type=lowerCamelCase_		)










"""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())

