
'''simple docstring'''





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


UpperCAmelCase            = logging.get_logger(__name__)


if is_vision_available():
						import PIL

class     a (					__magic_name__     ):
				_snake_case		     =			['''pixel_values''']




				def __init__(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					Dict[str, int] = None,     SCREAMING_SNAKE_CASE_						:					PILImageResampling = PILImageResampling.BILINEAR,     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					Union[int, float] = 1 / 2_55,     SCREAMING_SNAKE_CASE_						:					bool = True,     SCREAMING_SNAKE_CASE_						:					Optional[Union[float, List[float]]] = None,     SCREAMING_SNAKE_CASE_						:					Optional[Union[float, List[float]]] = None,     **SCREAMING_SNAKE_CASE_						:					Any,     ):
										super().__init__(**SCREAMING_SNAKE_CASE_   )

										snake_case							:  List[str]			=     size if size is not None else {'''height''': 25_60, '''width''': 19_20}
										if isinstance(SCREAMING_SNAKE_CASE_,     (tuple, list)   ):
																# The previous feature extractor size parameter was in (width, height) format
																snake_case							:  str			=     size[::-1]
										snake_case							:  Tuple			=     get_size_dict(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Optional[Any]			=     do_resize
										snake_case							:  Union[str, Any]			=     size
										snake_case							:  str			=     resample
										snake_case							:  Any			=     do_thumbnail
										snake_case							:  Tuple			=     do_align_long_axis
										snake_case							:  str			=     do_pad
										snake_case							:  Tuple			=     do_rescale
										snake_case							:  Union[str, Any]			=     rescale_factor
										snake_case							:  str			=     do_normalize
										snake_case							:  Optional[Any]			=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										snake_case							:  Optional[int]			=     image_std if image_std is not None else IMAGENET_STANDARD_STD




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					np.ndarray,     SCREAMING_SNAKE_CASE_						:					Dict[str, int],     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, ChannelDimension]] = None   ):
										snake_case,       snake_case							:  Dict			=     get_image_size(SCREAMING_SNAKE_CASE_   )
										snake_case,       snake_case							:  List[str]			=     size['''height'''], size['''width''']

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																snake_case							:  Any			=     np.rotaa(SCREAMING_SNAKE_CASE_,     3   )

										if data_format is not None:
																snake_case							:  List[Any]			=     to_channel_dimension_format(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

										return image




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					np.ndarray,     SCREAMING_SNAKE_CASE_						:					Dict[str, int],     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, ChannelDimension]] = None,     ):
										snake_case,       snake_case							:  List[str]			=     size['''height'''], size['''width''']
										snake_case,       snake_case							:  Union[str, Any]			=     get_image_size(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     output_width - input_width
										snake_case							:  List[str]			=     output_height - input_height

										if random_padding:
																snake_case							:  Any			=     np.random.randint(low=0,     high=delta_height + 1   )
																snake_case							:  Dict			=     np.random.randint(low=0,     high=delta_width + 1   )
										else:
																snake_case							:  Optional[Any]			=     delta_height // 2
																snake_case							:  Optional[int]			=     delta_width // 2

										snake_case							:  Union[str, Any]			=     delta_height - pad_top
										snake_case							:  str			=     delta_width - pad_left

										snake_case							:  str			=     ((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     *SCREAMING_SNAKE_CASE_						:					Optional[Any],     **SCREAMING_SNAKE_CASE_						:					Dict   ):
										logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''   )
										return self.pad_image(*SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					np.ndarray,     SCREAMING_SNAKE_CASE_						:					Dict[str, int],     SCREAMING_SNAKE_CASE_						:					PILImageResampling = PILImageResampling.BICUBIC,     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, ChannelDimension]] = None,     **SCREAMING_SNAKE_CASE_						:					Any,     ):
										snake_case,       snake_case							:  Optional[Any]			=     get_image_size(SCREAMING_SNAKE_CASE_   )
										snake_case,       snake_case							:  Optional[Any]			=     size['''height'''], size['''width''']

										# We always resize to the smallest of either the input or output size.
										snake_case							:  int			=     min(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     min(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

										if height == input_height and width == input_width:
																return image

										if input_height > input_width:
																snake_case							:  int			=     int(input_width * height / input_height   )
										elif input_width > input_height:
																snake_case							:  Dict			=     int(input_height * width / input_width   )

										return resize(
										    SCREAMING_SNAKE_CASE_,     size=(height, width),     resample=SCREAMING_SNAKE_CASE_,     reducing_gap=2.0,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					np.ndarray,     SCREAMING_SNAKE_CASE_						:					Dict[str, int],     SCREAMING_SNAKE_CASE_						:					PILImageResampling = PILImageResampling.BICUBIC,     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, ChannelDimension]] = None,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										snake_case							:  List[Any]			=     get_size_dict(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Tuple			=     min(size['''height'''],     size['''width''']   )
										snake_case							:  Optional[Any]			=     get_resize_output_image_size(SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     default_to_square=SCREAMING_SNAKE_CASE_   )
										snake_case							:  Dict			=     resize(SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )
										return resized_image




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					np.ndarray,     SCREAMING_SNAKE_CASE_						:					Union[int, float],     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, ChannelDimension]] = None,     **SCREAMING_SNAKE_CASE_						:					Union[str, Any],     ):
										return rescale(SCREAMING_SNAKE_CASE_,     scale=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					np.ndarray,     SCREAMING_SNAKE_CASE_						:					Union[float, List[float]],     SCREAMING_SNAKE_CASE_						:					Union[float, List[float]],     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, ChannelDimension]] = None,     **SCREAMING_SNAKE_CASE_						:					Union[str, Any],     ):
										return normalize(SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_,     data_format=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					ImageInput,     SCREAMING_SNAKE_CASE_						:					bool = None,     SCREAMING_SNAKE_CASE_						:					Dict[str, int] = None,     SCREAMING_SNAKE_CASE_						:					PILImageResampling = None,     SCREAMING_SNAKE_CASE_						:					bool = None,     SCREAMING_SNAKE_CASE_						:					bool = None,     SCREAMING_SNAKE_CASE_						:					bool = None,     SCREAMING_SNAKE_CASE_						:					bool = False,     SCREAMING_SNAKE_CASE_						:					bool = None,     SCREAMING_SNAKE_CASE_						:					float = None,     SCREAMING_SNAKE_CASE_						:					bool = None,     SCREAMING_SNAKE_CASE_						:					Optional[Union[float, List[float]]] = None,     SCREAMING_SNAKE_CASE_						:					Optional[Union[float, List[float]]] = None,     SCREAMING_SNAKE_CASE_						:					Optional[Union[str, TensorType]] = None,     SCREAMING_SNAKE_CASE_						:					Optional[ChannelDimension] = ChannelDimension.FIRST,     **SCREAMING_SNAKE_CASE_						:					List[str],     ):
										snake_case							:  Optional[int]			=     do_resize if do_resize is not None else self.do_resize
										snake_case							:  List[str]			=     size if size is not None else self.size
										if isinstance(SCREAMING_SNAKE_CASE_,     (tuple, list)   ):
																# Previous feature extractor had size in (width, height) format
																snake_case							:  Union[str, Any]			=     size[::-1]
										snake_case							:  Tuple			=     get_size_dict(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     resample if resample is not None else self.resample
										snake_case							:  Union[str, Any]			=     do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										snake_case							:  Any			=     do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										snake_case							:  List[str]			=     do_pad if do_pad is not None else self.do_pad
										snake_case							:  Optional[Any]			=     do_rescale if do_rescale is not None else self.do_rescale
										snake_case							:  Optional[int]			=     rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case							:  Any			=     do_normalize if do_normalize is not None else self.do_normalize
										snake_case							:  Optional[Any]			=     image_mean if image_mean is not None else self.image_mean
										snake_case							:  str			=     image_std if image_std is not None else self.image_std

										snake_case							:  List[str]			=     make_list_of_images(SCREAMING_SNAKE_CASE_   )

										if not valid_images(SCREAMING_SNAKE_CASE_   ):
																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_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''   )

										if do_pad and size is None:
																raise ValueError('''Size must be specified if do_pad 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							:  List[Any]			=     [to_numpy_array(SCREAMING_SNAKE_CASE_   ) for image in images]

										if do_align_long_axis:
																snake_case							:  List[Any]			=     [self.align_long_axis(SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_   ) for image in images]

										if do_resize:
																snake_case							:  Tuple			=     [self.resize(image=SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     resample=SCREAMING_SNAKE_CASE_   ) for image in images]

										if do_thumbnail:
																snake_case							:  int			=     [self.thumbnail(image=SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_   ) for image in images]

										if do_pad:
																snake_case							:  Dict			=     [self.pad_image(image=SCREAMING_SNAKE_CASE_,     size=SCREAMING_SNAKE_CASE_,     random_padding=SCREAMING_SNAKE_CASE_   ) for image in images]

										if do_rescale:
																snake_case							:  List[Any]			=     [self.rescale(image=SCREAMING_SNAKE_CASE_,     scale=SCREAMING_SNAKE_CASE_   ) for image in images]

										if do_normalize:
																snake_case							:  str			=     [self.normalize(image=SCREAMING_SNAKE_CASE_,     mean=SCREAMING_SNAKE_CASE_,     std=SCREAMING_SNAKE_CASE_   ) for image in images]

										snake_case							:  int			=     [to_channel_dimension_format(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) for image in images]

										snake_case							:  Tuple			=     {'''pixel_values''': images}
										return BatchFeature(data=SCREAMING_SNAKE_CASE_,     tensor_type=SCREAMING_SNAKE_CASE_   )





'''simple docstring'''




import os





def 				a_							(    )      ->int:






				'''simple docstring'''

				__lowerCamelCase		:   Dict           =       os.path.dirname(os.path.realpath(lowercase     )     )
				__lowerCamelCase		:   Any           =       os.path.join(lowercase				,    "triangle.txt"     )

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

				while len(lowercase     ) != 1:
								__lowerCamelCase		:   List[str]           =       triangle.pop()
								__lowerCamelCase		:   Union[str, Any]           =       triangle[-1]
								for j in range(len(lowercase     ) - 1     ):
												curr_row[j] += max(last_row[j]				,    last_row[j + 1]     )
				return triangle[0][0]


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





