


'''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


__lowercase:		List[Any]    						=  logging.get_logger(__name__)


if is_vision_available():
       import PIL







class 			UpperCAmelCase			(							lowerCAmelCase_):
 _lowerCamelCase			:       List[str]            =       ["pixel_values"]

 def __init__(			self			:						Tuple,       a_			:						bool = True,       a_			:						Dict[str, int] = None,       a_			:						PILImageResampling = PILImageResampling.BILINEAR,       a_			:						bool = True,       a_			:						bool = False,       a_			:						bool = True,       a_			:						bool = True,       a_			:						Union[int, float] = 1 / 255,       a_			:						bool = True,       a_			:						Optional[Union[float, List[float]]] = None,       a_			:						Optional[Union[float, List[float]]] = None,       **a_			:						str,       ):





    """simple docstring"""



    super().__init__(**lowerCamelCase__       )

    UpperCamelCase__			    =    size if size is not None else {"height": 2560, "width": 1920}
    if isinstance(lowerCamelCase__,       (tuple, list)       ):
       # The previous feature extractor size parameter was in (width, height) format
       UpperCamelCase__			    =    size[::-1]
    UpperCamelCase__			    =    get_size_dict(lowerCamelCase__       )

    UpperCamelCase__			    =    do_resize
    UpperCamelCase__			    =    size
    UpperCamelCase__			    =    resample
    UpperCamelCase__			    =    do_thumbnail
    UpperCamelCase__			    =    do_align_long_axis
    UpperCamelCase__			    =    do_pad
    UpperCamelCase__			    =    do_rescale
    UpperCamelCase__			    =    rescale_factor
    UpperCamelCase__			    =    do_normalize
    UpperCamelCase__			    =    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    UpperCamelCase__			    =    image_std if image_std is not None else IMAGENET_STANDARD_STD

 def 		lowercase_    (			self			:						Optional[int],       a_			:						np.ndarray,       a_			:						Dict[str, int],       a_			:						Optional[Union[str, ChannelDimension]] = None       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    get_image_size(lowerCamelCase__       )
    UpperCamelCase__    ,		UpperCamelCase__			    =    size["height"], size["width"]

    if (output_width < output_height and input_width > input_height) or (
        output_width > output_height and input_width < input_height
    ):
       UpperCamelCase__			    =    np.rotaa(lowerCamelCase__,       3       )

    if data_format is not None:
       UpperCamelCase__			    =    to_channel_dimension_format(lowerCamelCase__,       lowerCamelCase__       )

    return image

 def 		lowercase_    (			self			:						List[Any],       a_			:						np.ndarray,       a_			:						Dict[str, int],       a_			:						bool = False,       a_			:						Optional[Union[str, ChannelDimension]] = None,       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    size["height"], size["width"]
    UpperCamelCase__    ,		UpperCamelCase__			    =    get_image_size(lowerCamelCase__       )

    UpperCamelCase__			    =    output_width - input_width
    UpperCamelCase__			    =    output_height - input_height

    if random_padding:
       UpperCamelCase__			    =    np.random.randint(low=0,       high=delta_height + 1       )
       UpperCamelCase__			    =    np.random.randint(low=0,       high=delta_width + 1       )
    else:
       UpperCamelCase__			    =    delta_height // 2
       UpperCamelCase__			    =    delta_width // 2

    UpperCamelCase__			    =    delta_height - pad_top
    UpperCamelCase__			    =    delta_width - pad_left

    UpperCamelCase__			    =    ((pad_top, pad_bottom), (pad_left, pad_right))
    return pad(lowerCamelCase__,       lowerCamelCase__,       data_format=lowerCamelCase__       )

 def 		lowercase_    (			self			:						Tuple,       *a_			:						Tuple,       **a_			:						Optional[int]       ):





    """simple docstring"""



    logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."       )
    return self.pad_image(*lowerCamelCase__,       **lowerCamelCase__       )

 def 		lowercase_    (			self			:						Any,       a_			:						np.ndarray,       a_			:						Dict[str, int],       a_			:						PILImageResampling = PILImageResampling.BICUBIC,       a_			:						Optional[Union[str, ChannelDimension]] = None,       **a_			:						str,       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    get_image_size(lowerCamelCase__       )
    UpperCamelCase__    ,		UpperCamelCase__			    =    size["height"], size["width"]

    # We always resize to the smallest of either the input or output size.
    UpperCamelCase__			    =    min(lowerCamelCase__,       lowerCamelCase__       )
    UpperCamelCase__			    =    min(lowerCamelCase__,       lowerCamelCase__       )

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

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

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

 def 		lowercase_    (			self			:						int,       a_			:						np.ndarray,       a_			:						Dict[str, int],       a_			:						PILImageResampling = PILImageResampling.BICUBIC,       a_			:						Optional[Union[str, ChannelDimension]] = None,       **a_			:						Optional[int],       ):





    """simple docstring"""



    UpperCamelCase__			    =    get_size_dict(lowerCamelCase__       )
    UpperCamelCase__			    =    min(size["height"],       size["width"]       )
    UpperCamelCase__			    =    get_resize_output_image_size(lowerCamelCase__,       size=lowerCamelCase__,       default_to_square=lowerCamelCase__       )
    UpperCamelCase__			    =    resize(lowerCamelCase__,       size=lowerCamelCase__,       resample=lowerCamelCase__,       data_format=lowerCamelCase__,       **lowerCamelCase__       )
    return resized_image

 def 		lowercase_    (			self			:						int,       a_			:						np.ndarray,       a_			:						Union[int, float],       a_			:						Optional[Union[str, ChannelDimension]] = None,       **a_			:						Optional[int],       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						int,       a_			:						np.ndarray,       a_			:						Union[float, List[float]],       a_			:						Union[float, List[float]],       a_			:						Optional[Union[str, ChannelDimension]] = None,       **a_			:						List[Any],       ):





    """simple docstring"""



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

 def 		lowercase_    (			self			:						Tuple,       a_			:						ImageInput,       a_			:						bool = None,       a_			:						Dict[str, int] = None,       a_			:						PILImageResampling = None,       a_			:						bool = None,       a_			:						bool = None,       a_			:						bool = None,       a_			:						bool = False,       a_			:						bool = None,       a_			:						float = None,       a_			:						bool = None,       a_			:						Optional[Union[float, List[float]]] = None,       a_			:						Optional[Union[float, List[float]]] = None,       a_			:						Optional[Union[str, TensorType]] = None,       a_			:						Optional[ChannelDimension] = ChannelDimension.FIRST,       **a_			:						Optional[int],       ):





    """simple docstring"""



    UpperCamelCase__			    =    do_resize if do_resize is not None else self.do_resize
    UpperCamelCase__			    =    size if size is not None else self.size
    if isinstance(lowerCamelCase__,       (tuple, list)       ):
       # Previous feature extractor had size in (width, height) format
       UpperCamelCase__			    =    size[::-1]
    UpperCamelCase__			    =    get_size_dict(lowerCamelCase__       )
    UpperCamelCase__			    =    resample if resample is not None else self.resample
    UpperCamelCase__			    =    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
    UpperCamelCase__			    =    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
    UpperCamelCase__			    =    do_pad if do_pad is not None else self.do_pad
    UpperCamelCase__			    =    do_rescale if do_rescale is not None else self.do_rescale
    UpperCamelCase__			    =    rescale_factor if rescale_factor is not None else self.rescale_factor
    UpperCamelCase__			    =    do_normalize if do_normalize is not None else self.do_normalize
    UpperCamelCase__			    =    image_mean if image_mean is not None else self.image_mean
    UpperCamelCase__			    =    image_std if image_std is not None else self.image_std

    UpperCamelCase__			    =    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_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.
    UpperCamelCase__			    =    [to_numpy_array(lowerCamelCase__       ) for image in images]

    if do_align_long_axis:
       UpperCamelCase__			    =    [self.align_long_axis(lowerCamelCase__,       size=lowerCamelCase__       ) for image in images]

    if do_resize:
       UpperCamelCase__			    =    [self.resize(image=lowerCamelCase__,       size=lowerCamelCase__,       resample=lowerCamelCase__       ) for image in images]

    if do_thumbnail:
       UpperCamelCase__			    =    [self.thumbnail(image=lowerCamelCase__,       size=lowerCamelCase__       ) for image in images]

    if do_pad:
       UpperCamelCase__			    =    [self.pad_image(image=lowerCamelCase__,       size=lowerCamelCase__,       random_padding=lowerCamelCase__       ) for image in images]

    if do_rescale:
       UpperCamelCase__			    =    [self.rescale(image=lowerCamelCase__,       scale=lowerCamelCase__       ) for image in images]

    if do_normalize:
       UpperCamelCase__			    =    [self.normalize(image=lowerCamelCase__,       mean=lowerCamelCase__,       std=lowerCamelCase__       ) for image in images]

    UpperCamelCase__			    =    [to_channel_dimension_format(lowerCamelCase__,       lowerCamelCase__       ) for image in images]

    UpperCamelCase__			    =    {"pixel_values": images}
    return BatchFeature(data=lowerCamelCase__,       tensor_type=lowerCamelCase__       )






import os



def   __lowercase  ( )					->  int:


      '''simple docstring'''


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

      with open(_SCREAMING_SNAKE_CASE      ) as in_file:
            SCREAMING_SNAKE_CASE						=		[[int(_SCREAMING_SNAKE_CASE      ) for i in line.split()] for line in in_file]

      while len(_SCREAMING_SNAKE_CASE      ) != 1:
            SCREAMING_SNAKE_CASE						=		triangle.pop()
            SCREAMING_SNAKE_CASE						=		triangle[-1]
            for j in range(len(_SCREAMING_SNAKE_CASE      ) - 1      ):
                  curr_row[j] += max(last_row[j]					,		last_row[j + 1]      )
      return triangle[0][0]


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


