






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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
     import PIL


lowerCamelCase__							=     logging.get_logger(__name__)




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				int 				= ["pixel_values"]



       def __init__(							self			,						a__ = True			,						a__ = None			,						a__ = None			,						a__ = PILImageResampling.BILINEAR			,						a__ = True			,						a__ = 1 / 2_55			,						a__ = True			,						a__ = None			,						a__ = None			,						**a__			,						) ->      None:






            '''simple docstring'''





            super().__init__(**a__    )
            __snake_case			:List[str]        =						size if size is not None else {"""shortest_edge""": 3_84}
            __snake_case			:Tuple        =						get_size_dict(a__			,						default_to_square=a__    )

            __snake_case			:List[Any]        =						do_resize
            __snake_case			:str        =						size
            # Default value set here for backwards compatibility where the value in config is None
            __snake_case			:Any        =						crop_pct if crop_pct is not None else 2_24 / 2_56
            __snake_case			:Any        =						resample
            __snake_case			:Any        =						do_rescale
            __snake_case			:Optional[int]        =						rescale_factor
            __snake_case			:List[Any]        =						do_normalize
            __snake_case			:Optional[Any]        =						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
            __snake_case			:Dict        =						image_std if image_std is not None else IMAGENET_STANDARD_STD



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__ = PILImageResampling.BICUBIC			,						a__ = None			,						**a__			,						) ->      np.ndarray:






            '''simple docstring'''





            __snake_case			:List[str]        =						get_size_dict(a__			,						default_to_square=a__    )
            if "shortest_edge" not in size:
                 raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''    )
            __snake_case			:int        =						size["""shortest_edge"""]

            if shortest_edge < 3_84:
                 # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                 __snake_case			:Dict        =						int(shortest_edge / crop_pct    )
                 __snake_case			:Any        =						get_resize_output_image_size(a__			,						size=a__			,						default_to_square=a__    )
                 __snake_case			:Any        =						resize(image=a__			,						size=a__			,						resample=a__			,						data_format=a__			,						**a__    )
                 # then crop to (shortest_edge, shortest_edge)
                 return center_crop(image=a__			,						size=(shortest_edge, shortest_edge)			,						data_format=a__			,						**a__    )
            else:
                 # warping (no cropping) when evaluated at 384 or larger
                 return resize(
                     a__			,						size=(shortest_edge, shortest_edge)			,						resample=a__			,						data_format=a__			,						**a__    )



       def 					__lowercase						(							self			,						a__			,						a__			,						a__ = None			,						**a__			,						) ->      List[str]:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						a__			,						a__			,						a__			,						a__ = None			,						**a__			,						) ->      np.ndarray:






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						a__			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = None			,						a__ = ChannelDimension.FIRST			,						**a__			,						) ->      PIL.Image.Image:






            '''simple docstring'''





            __snake_case			:List[str]        =						do_resize if do_resize is not None else self.do_resize
            __snake_case			:Optional[Any]        =						crop_pct if crop_pct is not None else self.crop_pct
            __snake_case			:List[str]        =						resample if resample is not None else self.resample
            __snake_case			:Tuple        =						do_rescale if do_rescale is not None else self.do_rescale
            __snake_case			:int        =						rescale_factor if rescale_factor is not None else self.rescale_factor
            __snake_case			:Tuple        =						do_normalize if do_normalize is not None else self.do_normalize
            __snake_case			:List[str]        =						image_mean if image_mean is not None else self.image_mean
            __snake_case			:Optional[Any]        =						image_std if image_std is not None else self.image_std

            __snake_case			:Union[str, Any]        =						size if size is not None else self.size
            __snake_case			:Dict        =						get_size_dict(a__			,						default_to_square=a__    )

            __snake_case			:Tuple        =						make_list_of_images(a__    )

            if not valid_images(a__    ):
                 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 or resample is None:
                 raise ValueError("""Size and resample must be specified if do_resize is True."""    )

            if do_resize and size["shortest_edge"] < 3_84 and crop_pct is None:
                 raise ValueError("""crop_pct must be specified if size < 384."""    )

            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."""    )

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

            if do_resize:
                 __snake_case			:List[str]        =						[self.resize(image=a__			,						size=a__			,						crop_pct=a__			,						resample=a__    ) for image in images]

            if do_rescale:
                 __snake_case			:Any        =						[self.rescale(image=a__			,						scale=a__    ) for image in images]

            if do_normalize:
                 __snake_case			:int        =						[self.normalize(image=a__			,						mean=a__			,						std=a__    ) for image in images]

            __snake_case			:Optional[Any]        =						[to_channel_dimension_format(a__			,						a__    ) for image in images]

            __snake_case			:Optional[int]        =						{"""pixel_values""": images}
            return BatchFeature(data=a__			,						tensor_type=a__    )










import os






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						os.path.dirname(os.path.realpath(snake_case__  )  )
     __snake_case			:Union[str, Any]          =						os.path.join(snake_case__	,"""triangle.txt"""  )

     with open(snake_case__  ) as f:
          __snake_case			:int          =						f.readlines()

     __snake_case			:int          =						[]
     for line in triangle:
          __snake_case			:List[Any]          =						[]
          for number in line.strip().split(""" """  ):
               numbers_from_line.append(int(snake_case__  )  )
          a.append(snake_case__  )

     for i in range(1	,len(snake_case__  )  ):
          for j in range(len(a[i]  )  ):
               __snake_case			:Union[str, Any]          =						a[i - 1][j] if j != len(a[i - 1]  ) else 0
               __snake_case			:Dict          =						a[i - 1][j - 1] if j > 0 else 0
               a[i][j] += max(snake_case__	,snake_case__  )
     return max(a[-1]  )


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


