



from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
			import torch

if is_torch_tpu_available(check_device=False):
			import torch_xla.core.xla_model as xm


lowerCAmelCase__		:					int							=logging.get_logger(__name__)

@dataclass
class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						UpperCamelCase__      :			Optional[int]      =  [
						    '''no_inference''',
						    '''no_cuda''',
						    '''no_tpu''',
						    '''no_speed''',
						    '''no_memory''',
						    '''no_env_print''',
						    '''no_multi_process''',
						]






						def __init__(   self				,							**_A		):







													'''simple docstring'''

													for deprecated_arg in self.deprecated_args:
																				if deprecated_arg in kwargs:
																											__SCREAMING_SNAKE_CASE									= deprecated_arg[3:]
																											setattr(self				,							_A				,							not kwargs.pop(_A		)		)
																											logger.warning(
																											    f"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
																											    f""" {positive_arg}={kwargs[positive_arg]}"""		)

													__SCREAMING_SNAKE_CASE									= kwargs.pop('torchscript'				,							self.torchscript		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('torch_xla_tpu_print_metrics'				,							self.torch_xla_tpu_print_metrics		)
													__SCREAMING_SNAKE_CASE									= kwargs.pop('fp16_opt_level'				,							self.fpaa_opt_level		)
													super().__init__(**_A		)

						UpperCamelCase__      :			bool      =  field(default=UpperCamelCase_   ,   metadata={'''help''': '''Trace the models using torchscript'''}	)
						UpperCamelCase__      :			bool      =  field(default=UpperCamelCase_   ,   metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}	)
						UpperCamelCase__      :			str      =  field(
						    default='''O1'''   ,   metadata={
						        '''help''': (
						            '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
						            '''See details at https://nvidia.github.io/apex/amp.html'''
						        )
						    }   ,   )






						@cached_property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													logger.info('PyTorch: setting up devices'		)
													if not self.cuda:
																				__SCREAMING_SNAKE_CASE									= torch.device('cpu'		)
																				__SCREAMING_SNAKE_CASE									= 0
													elif is_torch_tpu_available():
																				__SCREAMING_SNAKE_CASE									= xm.xla_device()
																				__SCREAMING_SNAKE_CASE									= 0
													else:
																				__SCREAMING_SNAKE_CASE									= torch.device('cuda' if torch.cuda.is_available() else 'cpu'		)
																				__SCREAMING_SNAKE_CASE									= torch.cuda.device_count()
													return device, n_gpu






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return is_torch_tpu_available() and self.tpu






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													# TODO(PVP): currently only single GPU is supported
													return torch.cuda.current_device()






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													return self._setup_devices[0]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													requires_backends(self				,							['torch']		)
													return self._setup_devices[1]






						@property
						def 	_A     (   self		):







													'''simple docstring'''

													return self.n_gpu > 0











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() = }''')






