






"""simple docstring"""



import unittest

import numpy as np

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

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 ViTImageProcessor





class    _UpperCAmelCase			(					unittest.TestCase				):




					def __init__(							self	:		Any       ,		A	:		List[str]       ,		A	:		Optional[Any]=13       ,		A	:		Optional[Any]=3       ,		A	:		int=2_24       ,		A	:		Tuple=30       ,		A	:		Tuple=4_00       ,		A	:		int=True       ,		A	:		int=None       ,		A	:		str=True       ,		A	:		Optional[int]=[0.5, 0.5, 0.5]       ,		A	:		List[str]=[0.5, 0.5, 0.5]       ,		)    ->    Optional[Any]:
							lowercase_					:					Dict							=  size if size is not None else {'''height''': 18, '''width''': 18}
							lowercase_					:					Any							=  parent
							lowercase_					:					int							=  batch_size
							lowercase_					:					Dict							=  num_channels
							lowercase_					:					Dict							=  image_size
							lowercase_					:					Any							=  min_resolution
							lowercase_					:					Any							=  max_resolution
							lowercase_					:					Tuple							=  do_resize
							lowercase_					:					Optional[int]							=  size
							lowercase_					:					int							=  do_normalize
							lowercase_					:					Tuple							=  image_mean
							lowercase_					:					Tuple							=  image_std






					def     A    (							self	:		Any  )    ->    Dict:
							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    _UpperCAmelCase			(					_A  ,		unittest.TestCase				):
					SCREAMING_SNAKE_CASE_						:							List[str]				       =  ViTImageProcessor if is_vision_available() else None




					def     A    (							self	:		int  )    ->    Optional[Any]:
							lowercase_					:					Dict							=  EfficientFormerImageProcessorTester(self  )




					@property
					def     A    (							self	:		int  )    ->    Union[str, Any]:
							return self.image_proc_tester.prepare_image_processor_dict()




					def     A    (							self	:		Union[str, Any]  )    ->    List[str]:
							lowercase_					:					Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict  )
							self.assertTrue(hasattr(UpperCamelCase_       ,		'''image_mean'''  )  )
							self.assertTrue(hasattr(UpperCamelCase_       ,		'''image_std'''  )  )
							self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_normalize'''  )  )
							self.assertTrue(hasattr(UpperCamelCase_       ,		'''do_resize'''  )  )
							self.assertTrue(hasattr(UpperCamelCase_       ,		'''size'''  )  )




					def     A    (							self	:		Union[str, Any]  )    ->    Tuple:
							pass




					def     A    (							self	:		Tuple  )    ->    Dict:
							# Initialize image_processor
							lowercase_					:					List[str]							=  self.image_processing_class(**self.image_processor_dict  )
							# create random PIL images
							lowercase_					:					List[str]							=  prepare_image_inputs(self.image_proc_tester       ,		equal_resolution=UpperCamelCase_  )
							for image in image_inputs:
									self.assertIsInstance(UpperCamelCase_       ,		Image.Image  )

							# Test not batched input
							lowercase_					:					Optional[int]							=  image_processor(image_inputs[0]       ,		return_tensors='''pt'''  ).pixel_values
							self.assertEqual(
							    encoded_images.shape       ,		(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )       ,		)

							# Test batched
							lowercase_					:					Tuple							=  image_processor(UpperCamelCase_       ,		return_tensors='''pt'''  ).pixel_values
							self.assertEqual(
							    encoded_images.shape       ,		(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )       ,		)




					def     A    (							self	:		List[str]  )    ->    List[str]:
							# Initialize image_processor
							lowercase_					:					Union[str, Any]							=  self.image_processing_class(**self.image_processor_dict  )
							# create random numpy tensors
							lowercase_					:					Optional[int]							=  prepare_image_inputs(self.image_proc_tester       ,		equal_resolution=UpperCamelCase_       ,		numpify=UpperCamelCase_  )
							for image in image_inputs:
									self.assertIsInstance(UpperCamelCase_       ,		np.ndarray  )

							# Test not batched input
							lowercase_					:					int							=  image_processor(image_inputs[0]       ,		return_tensors='''pt'''  ).pixel_values
							self.assertEqual(
							    encoded_images.shape       ,		(
							        1,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )       ,		)

							# Test batched
							lowercase_					:					str							=  image_processor(UpperCamelCase_       ,		return_tensors='''pt'''  ).pixel_values
							self.assertEqual(
							    encoded_images.shape       ,		(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )       ,		)






					def     A    (							self	:		Dict  )    ->    Tuple:
							# Initialize image_processor
							lowercase_					:					int							=  self.image_processing_class(**self.image_processor_dict  )
							# create random PyTorch tensors
							lowercase_					:					Optional[Any]							=  prepare_image_inputs(self.image_proc_tester       ,		equal_resolution=UpperCamelCase_       ,		torchify=UpperCamelCase_  )
							for image in image_inputs:
									self.assertIsInstance(UpperCamelCase_       ,		torch.Tensor  )

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

							# Test batched
							lowercase_					:					Any							=  image_processor(UpperCamelCase_       ,		return_tensors='''pt'''  ).pixel_values
							self.assertEqual(
							    encoded_images.shape       ,		(
							        self.image_proc_tester.batch_size,
							        self.image_proc_tester.num_channels,
							        self.image_proc_tester.size['''height'''],
							        self.image_proc_tester.size['''width'''],
							    )       ,		)










'''simple docstring'''




import os




def 						__lowerCamelCase			(	UpperCAmelCase_ = "input.txt"							)   ->int:

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

	snake_case__	       =   len(UpperCAmelCase_							)
	snake_case__	       =   len(matrix[0]							)

	snake_case__	       =   [[-1 for _ in range(UpperCAmelCase_							)] for _ in range(UpperCAmelCase_							)]
	for i in range(UpperCAmelCase_							):
		snake_case__	       =   matrix[i][0]

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

		for i in range(1					,				UpperCAmelCase_							):
			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							):
			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() = }""")



