


'''simple docstring'''



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


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):

			def constraint_to_multiple_of(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=0 ,      UpperCamelCase__=None					):
						UpperCAmelCase__  :					Optional[int]        =     round(val / multiple					) * multiple

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

						if x < min_val:
									UpperCAmelCase__  :					List[Any]        =     math.ceil(val / multiple					) * multiple

						return x

			UpperCAmelCase__  :					Any        =     (output_size, output_size) if isinstance(a__ ,      a__					) else output_size

			UpperCAmelCase__   ,       UpperCAmelCase__  :					List[Any]        =     get_image_size(a__					)
			UpperCAmelCase__   ,       UpperCAmelCase__  :					int        =     output_size

			# determine new height and width
			UpperCAmelCase__  :					Any        =     output_height / input_height
			UpperCAmelCase__  :					List[Any]        =     output_width / input_width

			if keep_aspect_ratio:
						# scale as little as possible
						if abs(1 - scale_width					) < abs(1 - scale_height					):
									# fit width
									UpperCAmelCase__  :					Optional[Any]        =     scale_width
						else:
									# fit height
									UpperCAmelCase__  :					Union[str, Any]        =     scale_height

			UpperCAmelCase__  :					Any        =     constraint_to_multiple_of(scale_height * input_height ,      multiple=a__					)
			UpperCAmelCase__  :					Any        =     constraint_to_multiple_of(scale_width * input_width ,      multiple=a__					)

			return (new_height, new_width)







class 		_snake_case					(     a__	):
					lowerCAmelCase      :str   =					['''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							,							):
								super().__init__(**lowercase_)
								UpperCAmelCase__  :					Optional[int]        =     size if size is not None else {"""height""": 384, """width""": 384}
								UpperCAmelCase__  :					List[Any]        =     get_size_dict(lowercase_)
								UpperCAmelCase__  :					str        =     do_resize
								UpperCAmelCase__  :					Tuple        =     size
								UpperCAmelCase__  :					Any        =     keep_aspect_ratio
								UpperCAmelCase__  :					int        =     ensure_multiple_of
								UpperCAmelCase__  :					int        =     resample
								UpperCAmelCase__  :					int        =     do_rescale
								UpperCAmelCase__  :					Dict        =     rescale_factor
								UpperCAmelCase__  :					List[Any]        =     do_normalize
								UpperCAmelCase__  :					Any        =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								UpperCAmelCase__  :					Union[str, Any]        =     image_std if image_std is not None else IMAGENET_STANDARD_STD




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = False							,							_lowerCamelCase = 1							,							_lowerCamelCase = PILImageResampling.BICUBIC							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__  :					Optional[int]        =     get_size_dict(lowercase_)
								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()}''')
								UpperCAmelCase__  :					Optional[int]        =     get_resize_output_image_size(
								    lowercase_							,							output_size=(size["""height"""], size["""width"""])							,							keep_aspect_ratio=lowercase_							,							multiple=lowercase_							,							)
								return resize(lowercase_							,							size=lowercase_							,							resample=lowercase_							,							data_format=lowercase_							,							**lowercase_)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								return rescale(lowercase_							,							scale=lowercase_							,							data_format=lowercase_							,							**lowercase_)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								return normalize(lowercase_							,							mean=lowercase_							,							std=lowercase_							,							data_format=lowercase_							,							**lowercase_)




					def 				snake_case__						(      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							,							):
								UpperCAmelCase__  :					Tuple        =     do_resize if do_resize is not None else self.do_resize
								UpperCAmelCase__  :					List[str]        =     size if size is not None else self.size
								UpperCAmelCase__  :					Optional[Any]        =     get_size_dict(lowercase_)
								UpperCAmelCase__  :					Tuple        =     keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
								UpperCAmelCase__  :					Any        =     ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
								UpperCAmelCase__  :					Any        =     resample if resample is not None else self.resample
								UpperCAmelCase__  :					Optional[int]        =     do_rescale if do_rescale is not None else self.do_rescale
								UpperCAmelCase__  :					Optional[Any]        =     rescale_factor if rescale_factor is not None else self.rescale_factor
								UpperCAmelCase__  :					str        =     do_normalize if do_normalize is not None else self.do_normalize
								UpperCAmelCase__  :					Any        =     image_mean if image_mean is not None else self.image_mean
								UpperCAmelCase__  :					Union[str, Any]        =     image_std if image_std is not None else self.image_std

								UpperCAmelCase__  :					str        =     make_list_of_images(lowercase_)

								if not valid_images(lowercase_):
											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.
								UpperCAmelCase__  :					Dict        =     [to_numpy_array(lowercase_) for image in images]

								if do_resize:
											UpperCAmelCase__  :					Dict        =     [self.resize(image=lowercase_							,							size=lowercase_							,							resample=lowercase_) for image in images]

								if do_rescale:
											UpperCAmelCase__  :					int        =     [self.rescale(image=lowercase_							,							scale=lowercase_) for image in images]

								if do_normalize:
											UpperCAmelCase__  :					Dict        =     [self.normalize(image=lowercase_							,							mean=lowercase_							,							std=lowercase_) for image in images]

								UpperCAmelCase__  :					Optional[Any]        =     [to_channel_dimension_format(lowercase_							,							lowercase_) for image in images]

								UpperCAmelCase__  :					int        =     {"""pixel_values""": images}
								return BatchFeature(data=lowercase_							,							tensor_type=lowercase_)




					def 				snake_case__						(      self							,							_lowerCamelCase							,							_lowerCamelCase = None):
								UpperCAmelCase__  :					Optional[Any]        =     outputs.logits

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

											if is_torch_tensor(lowercase_):
														UpperCAmelCase__  :					int        =     target_sizes.numpy()

											UpperCAmelCase__  :					Optional[int]        =     []

											for idx in range(len(lowercase_)):
														UpperCAmelCase__  :					Dict        =     torch.nn.functional.interpolate(
														    logits[idx].unsqueeze(dim=0)							,							size=target_sizes[idx]							,							mode="""bilinear"""							,							align_corners=lowercase_)
														UpperCAmelCase__  :					Union[str, Any]        =     resized_logits[0].argmax(dim=0)
														semantic_segmentation.append(lowercase_)
								else:
											UpperCAmelCase__  :					Union[str, Any]        =     logits.argmax(dim=1)
											UpperCAmelCase__  :					List[Any]        =     [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

								return semantic_segmentation



'''simple docstring'''



import os







def     _UpperCamelCase      (		UpperCamelCase__ = "input.txt"					):

			with open(os.path.join(os.path.dirname(UpperCamelCase__					) ,      UpperCamelCase__					)					) as input_file:
						UpperCAmelCase__  :					Tuple        =     [
						    [int(UpperCamelCase__					) for element in line.split(""","""					)]
						    for line in input_file.readlines()
						]

			UpperCAmelCase__  :					Optional[Any]        =     len(UpperCamelCase__					)
			UpperCAmelCase__  :					Any        =     len(matrix[0]					)

			UpperCAmelCase__  :					Optional[int]        =     [[-1 for _ in range(UpperCamelCase__					)] for _ in range(UpperCamelCase__					)]
			for i in range(UpperCamelCase__					):
						UpperCAmelCase__  :					Any        =     matrix[i][0]

			for j in range(1 ,      UpperCamelCase__					):
						for i in range(UpperCamelCase__					):
									UpperCAmelCase__  :					Any        =     minimal_path_sums[i][j - 1] + matrix[i][j]

						for i in range(1 ,      UpperCamelCase__					):
									UpperCAmelCase__  :					Dict        =     min(
									    minimal_path_sums[i][j] ,      minimal_path_sums[i - 1][j] + matrix[i][j]					)

						for i in range(rows - 2 ,      -1 ,      -1					):
									UpperCAmelCase__  :					int        =     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() = }""")