



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


lowerCAmelCase__		:					int							=logging.get_logger(__name__)






def      __lowercase       (			a__							,       a__							,       a__							,       a__       )    ->			Optional[Any]:
							def constraint_to_multiple_of(a__							,       a__							,       a__=0							,       a__=None       ):
														__SCREAMING_SNAKE_CASE									= round(val / multiple       ) * multiple

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

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

														return x

							__SCREAMING_SNAKE_CASE									= (output_size, output_size) if isinstance(__a							,       __a       ) else output_size

							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= get_image_size(__a       )
							__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= output_size

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

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

							__SCREAMING_SNAKE_CASE									= constraint_to_multiple_of(scale_height * input_height							,       multiple=__a       )
							__SCREAMING_SNAKE_CASE									= constraint_to_multiple_of(scale_width * input_width							,       multiple=__a       )

							return (new_height, new_width)

class        UpperCAmelCase_  (       __lowercase	):

						'''simple docstring'''




						UpperCamelCase__      :			Tuple      =  ['''pixel_values''']






						def __init__(   self				,							_A = True				,							_A = None				,							_A = PILImageResampling.BILINEAR				,							_A = False				,							_A = 1				,							_A = True				,							_A = 1 / 255				,							_A = True				,							_A = None				,							_A = None				,							**_A				,							):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A				,							_A = False				,							_A = 1				,							_A = PILImageResampling.BICUBIC				,							_A = None				,							**_A				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= get_size_dict(_a		)
													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()}"""		)
													__SCREAMING_SNAKE_CASE									= get_resize_output_image_size(
													    _a				,							output_size=(size['height'], size['width'])				,							keep_aspect_ratio=_a				,							multiple=_a				,							)
													return resize(_a				,							size=_a				,							resample=_a				,							data_format=_a				,							**_a		)






						def 	_A     (   self				,							_A				,							_A				,							_A = None				,							**_A				,							):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A				,							_A				,							_A = None				,							**_A				,							):







													'''simple docstring'''

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






						def 	_A     (   self				,							_A				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = None				,							_A = ChannelDimension.FIRST				,							**_A				,							):







													'''simple docstring'''

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

													__SCREAMING_SNAKE_CASE									= 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_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.
													__SCREAMING_SNAKE_CASE									= [to_numpy_array(_a		) for image in images]

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

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

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

													__SCREAMING_SNAKE_CASE									= [to_channel_dimension_format(_a				,							_a		) for image in images]

													__SCREAMING_SNAKE_CASE									= {'pixel_values': images}
													return BatchFeature(data=_a				,							tensor_type=_a		)






						def 	_A     (   self				,							_A				,							_A = None		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= outputs.logits

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

																				if is_torch_tensor(_a		):
																											__SCREAMING_SNAKE_CASE									= target_sizes.numpy()

																				__SCREAMING_SNAKE_CASE									= []

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

													return semantic_segmentation











import os






def      __lowercase       (			a__ = "input.txt"       )    ->			int:

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

							__SCREAMING_SNAKE_CASE									= len(a__       )
							__SCREAMING_SNAKE_CASE									= len(matrix[0]       )

							__SCREAMING_SNAKE_CASE									= [[-1 for _ in range(a__       )] for _ in range(a__       )]
							for i in range(a__       ):
														__SCREAMING_SNAKE_CASE									= matrix[i][0]

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

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

														for i in range(rows - 2							,       -1							,       -1       ):
																					__SCREAMING_SNAKE_CASE									= 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() = }''')






