'''simple docstring'''




import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor





class     __magic_name__						(    unittest.TestCase):







			def __init__(	self				:  List[str]				,			lowercase_				:  int				,			lowercase_				:  str=7				,			lowercase_				:  int=3				,			lowercase_				:  Any=18				,			lowercase_				:  Optional[int]=30				,			lowercase_				:  Optional[Any]=400				,			lowercase_				:  Dict=True				,			lowercase_				:  Tuple=None				,			lowercase_				:  Any=True				,			lowercase_				:  Any=[0.5, 0.5, 0.5]				,			lowercase_				:  Tuple=[0.5, 0.5, 0.5]				,			):
										lowercase_					:			str										=       size if size is not None else {"""height""": 18, """width""": 18}
										lowercase_					:			Dict										=       parent
										lowercase_					:			Optional[int]										=       batch_size
										lowercase_					:			List[Any]										=       num_channels
										lowercase_					:			str										=       image_size
										lowercase_					:			str										=       min_resolution
										lowercase_					:			Union[str, Any]										=       max_resolution
										lowercase_					:			List[Any]										=       do_resize
										lowercase_					:			Union[str, Any]										=       size
										lowercase_					:			Union[str, Any]										=       do_normalize
										lowercase_					:			Any										=       image_mean
										lowercase_					:			int										=       image_std







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}





@require_torch
@require_vision
class     __magic_name__						(    _UpperCAmelCase,     unittest.TestCase):
			UpperCamelCase__				=       DPTImageProcessor if is_vision_available() else None







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Optional[Any]    ):
										lowercase_					:			int										=       DPTImageProcessingTester(self    )







			@property
			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										return self.image_processor_tester.prepare_image_processor_dict()







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										lowercase_					:			List[Any]										=       self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowercase_				,			"""image_mean"""    )    )
										self.assertTrue(hasattr(lowercase_				,			"""image_std"""    )    )
										self.assertTrue(hasattr(lowercase_				,			"""do_normalize"""    )    )
										self.assertTrue(hasattr(lowercase_				,			"""do_resize"""    )    )
										self.assertTrue(hasattr(lowercase_				,			"""size"""    )    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Dict    ):
										lowercase_					:			List[Any]										=       self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size				,			{"""height""": 18, """width""": 18}    )

										lowercase_					:			Any										=       self.image_processing_class.from_dict(self.image_processor_dict				,			size=42    )
										self.assertEqual(image_processor.size				,			{"""height""": 42, """width""": 42}    )







			def 	SCREAMING_SNAKE_CASE_     (	self				:  List[str]    ):
										# Initialize image_processing
										lowercase_					:			Dict										=       self.image_processing_class(**self.image_processor_dict    )
										# create random PIL images
										lowercase_					:			Any										=       prepare_image_inputs(self.image_processor_tester				,			equal_resolution=lowercase_    )
										for image in image_inputs:
																	self.assertIsInstance(lowercase_				,			Image.Image    )

										# Test not batched input
										lowercase_					:			Tuple										=       image_processing(image_inputs[0]				,			return_tensors="""pt"""    ).pixel_values
										self.assertEqual(
										    encoded_images.shape				,			(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )				,			)

										# Test batched
										lowercase_					:			int										=       image_processing(lowercase_				,			return_tensors="""pt"""    ).pixel_values
										self.assertEqual(
										    encoded_images.shape				,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  Tuple    ):
										# Initialize image_processing
										lowercase_					:			List[Any]										=       self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										lowercase_					:			int										=       prepare_image_inputs(self.image_processor_tester				,			equal_resolution=lowercase_				,			numpify=lowercase_    )
										for image in image_inputs:
																	self.assertIsInstance(lowercase_				,			np.ndarray    )

										# Test not batched input
										lowercase_					:			List[Any]										=       image_processing(image_inputs[0]				,			return_tensors="""pt"""    ).pixel_values
										self.assertEqual(
										    encoded_images.shape				,			(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )				,			)

										# Test batched
										lowercase_					:			Union[str, Any]										=       image_processing(lowercase_				,			return_tensors="""pt"""    ).pixel_values
										self.assertEqual(
										    encoded_images.shape				,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )				,			)







			def 	SCREAMING_SNAKE_CASE_     (	self				:  str    ):
										# Initialize image_processing
										lowercase_					:			List[str]										=       self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										lowercase_					:			str										=       prepare_image_inputs(self.image_processor_tester				,			equal_resolution=lowercase_				,			torchify=lowercase_    )
										for image in image_inputs:
																	self.assertIsInstance(lowercase_				,			torch.Tensor    )

										# Test not batched input
										lowercase_					:			List[str]										=       image_processing(image_inputs[0]				,			return_tensors="""pt"""    ).pixel_values
										self.assertEqual(
										    encoded_images.shape				,			(
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )				,			)

										# Test batched
										lowercase_					:			str										=       image_processing(lowercase_				,			return_tensors="""pt"""    ).pixel_values
										self.assertEqual(
										    encoded_images.shape				,			(
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size["""height"""],
										        self.image_processor_tester.size["""width"""],
										    )				,			)






'''simple docstring'''




import os




def 							lowerCamelCase	(      UpperCAmelCase__       :					str = "input.txt" )			->      int:

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

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

							lowercase_					:			Union[str, Any]										=       [[-1 for _ in range(UpperCAmelCase__ )] for _ in range(UpperCAmelCase__ )]
							for i in range(UpperCAmelCase__ ):
														lowercase_					:			int										=       matrix[i][0]

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

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

														for i in range(rows - 2      ,      -1      ,      -1 ):
																					lowercase_					:			Dict										=       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() = }""")





