

import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


lowercase_									=   logging.get_logger(__name__)
def      a      (			A__	:   np.ndarray      ,   A__	:   Union[int, Iterable[int]]      ,   A__	:   bool      ,   A__	:   int     )    ->      Tuple[int, int]:





      """simple docstring"""



      def constraint_to_multiple_of(A__	:   Tuple      ,   A__	:   Dict      ,   A__	:   str=0      ,   A__	:   List[Any]=None     ):
            _lowercase	       =round(val / multiple     ) * multiple

            if max_val is not None and x > max_val:
                  _lowercase	       =math.floor(val / multiple     ) * multiple

            if x < min_val:
                  _lowercase	       =math.ceil(val / multiple     ) * multiple

            return x

      _lowercase	       =(output_size, output_size) if isinstance(A__      ,   A__     ) else output_size

      _lowercase       ,       _lowercase	       =get_image_size(A__     )
      _lowercase       ,       _lowercase	       =output_size

      # determine new height and width
      _lowercase	       =output_height / input_height
      _lowercase	       =output_width / input_width

      if keep_aspect_ratio:
            # scale as little as possible
            if abs(1 - scale_width     ) < abs(1 - scale_height     ):
                  # fit width
                  _lowercase	       =scale_width
            else:
                  # fit height
                  _lowercase	       =scale_height

      _lowercase	       =constraint_to_multiple_of(scale_height * input_height      ,   multiple=A__     )
      _lowercase	       =constraint_to_multiple_of(scale_width * input_width      ,   multiple=A__     )

      return (new_height, new_width)







class  __lowerCAmelCase     (					SCREAMING_SNAKE_CASE							):
  _a									=  ["""pixel_values"""]




  def __init__(				self							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = PILImageResampling.BILINEAR							,   lowerCAmelCase = False							,   lowerCAmelCase = 1							,   lowerCAmelCase = True							,   lowerCAmelCase = 1 / 255							,   lowerCAmelCase = True							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> None:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase					)
        _lowercase	       =size if size is not None else {'height': 384, 'width': 384}
        _lowercase	       =get_size_dict(lowerCAmelCase					)
        _lowercase	       =do_resize
        _lowercase	       =size
        _lowercase	       =keep_aspect_ratio
        _lowercase	       =ensure_multiple_of
        _lowercase	       =resample
        _lowercase	       =do_rescale
        _lowercase	       =rescale_factor
        _lowercase	       =do_normalize
        _lowercase	       =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        _lowercase	       =image_std if image_std is not None else IMAGENET_STANDARD_STD




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = False							,   lowerCAmelCase = 1							,   lowerCAmelCase = PILImageResampling.BICUBIC							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> np.ndarray:
        '''simple docstring'''






        _lowercase	       =get_size_dict(lowerCAmelCase					)
        if "height" not in size or "width" not in size:
              raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''					)
        _lowercase	       =get_resize_output_image_size(
            lowerCAmelCase							,   output_size=(size['height'], size['width'])							,   keep_aspect_ratio=lowerCAmelCase							,   multiple=lowerCAmelCase							,   )
        return resize(lowerCAmelCase							,   size=lowerCAmelCase							,   resample=lowerCAmelCase							,   data_format=lowerCAmelCase							,   **lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> str:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> np.ndarray:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = ChannelDimension.FIRST							,   **lowerCAmelCase							,   )  -> PIL.Image.Image:
        '''simple docstring'''






        _lowercase	       =do_resize if do_resize is not None else self.do_resize
        _lowercase	       =size if size is not None else self.size
        _lowercase	       =get_size_dict(lowerCAmelCase					)
        _lowercase	       =keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
        _lowercase	       =ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
        _lowercase	       =resample if resample is not None else self.resample
        _lowercase	       =do_rescale if do_rescale is not None else self.do_rescale
        _lowercase	       =rescale_factor if rescale_factor is not None else self.rescale_factor
        _lowercase	       =do_normalize if do_normalize is not None else self.do_normalize
        _lowercase	       =image_mean if image_mean is not None else self.image_mean
        _lowercase	       =image_std if image_std is not None else self.image_std

        _lowercase	       =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 or resample is None:
              raise ValueError('Size and resample 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_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.
        _lowercase	       =[to_numpy_array(lowerCAmelCase					) for image in images]

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

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

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

        _lowercase	       =[to_channel_dimension_format(lowerCAmelCase							,   lowerCAmelCase					) for image in images]

        _lowercase	       ={'pixel_values': images}
        return BatchFeature(data=lowerCAmelCase							,   tensor_type=lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None					)  -> List[Any]:
        '''simple docstring'''






        _lowercase	       =outputs.logits

        # Resize logits and compute semantic segmentation maps
        if target_sizes is not None:
              if len(lowerCAmelCase					) != len(lowerCAmelCase					):
                    raise ValueError(
                        'Make sure that you pass in as many target sizes as the batch dimension of the logits'					)

              if is_torch_tensor(lowerCAmelCase					):
                    _lowercase	       =target_sizes.numpy()

              _lowercase	       =[]

              for idx in range(len(lowerCAmelCase					)					):
                    _lowercase	       =torch.nn.functional.interpolate(
                        logits[idx].unsqueeze(dim=0					)							,   size=target_sizes[idx]							,   mode='bilinear'							,   align_corners=lowerCAmelCase					)
                    _lowercase	       =resized_logits[0].argmax(dim=0					)
                    semantic_segmentation.append(lowerCAmelCase					)
        else:
              _lowercase	       =logits.argmax(dim=1					)
              _lowercase	       =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]					)]

        return semantic_segmentation





import os
def      a      (			A__	:   str = "input.txt"     )    ->      int:





      """simple docstring"""



      with open(os.path.join(os.path.dirname(A__     )      ,   A__     )     ) as input_file:
            _lowercase	       =[
                [int(A__     ) for element in line.split(','     )]
                for line in input_file.readlines()
            ]

      _lowercase	       =len(A__     )
      _lowercase	       =len(matrix[0]     )

      _lowercase	       =[[-1 for _ in range(A__     )] for _ in range(A__     )]
      for i in range(A__     ):
            _lowercase	       =matrix[i][0]

      for j in range(1      ,   A__     ):
            for i in range(A__     ):
                  _lowercase	       =minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1      ,   A__     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i - 1][j] + matrix[i][j]     )

            for i in range(rows - 2      ,   -1      ,   -1     ):
                  _lowercase	       =min(
                      minimal_path_sums[i][j]      ,   minimal_path_sums[i + 1][j] + matrix[i][j]     )

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums     )


if __name__ == "__main__":
  print(f"{solution() = }")


