



import os

def 			_lowerCAmelCase				(       lowercase_ = "input.txt"						):





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

						UpperCAmelCase					=			len(lowerCAmelCase__						)
						UpperCAmelCase					=			len(matrix[0]						)

						UpperCAmelCase					=			[[-1 for _ in range(lowerCAmelCase__						)] for _ in range(lowerCAmelCase__						)]
						for i in range(lowerCAmelCase__						):
												UpperCAmelCase					=			matrix[i][0]

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

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

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










"""simple docstring"""


import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
				import transformers

def 			_lowerCAmelCase				(       lowercase_						):
						random.seed(lowercase_						)
						np.random.seed(lowercase_						)
						torch.manual_seed(lowercase_						)
						torch.cuda.manual_seed_all(lowercase_						)
						# ^^ safe to call this function even if cuda is not available







class 				A_      :



						"""simple docstring"""






						def __init__( self			:Any       ,  lowercase_			:Iterable[torch.nn.Parameter]       ,  lowercase_			:float = 0.9999       ,  lowercase_			:float = 0.0       ,  lowercase_			:int = 0       ,  lowercase_			:bool = False       ,  lowercase_			:Union[float, int] = 1.0       ,  lowercase_			:Union[float, int] = 2 / 3       ,  lowercase_			:Optional[Any] = None       ,  lowercase_			:Dict[str, Any] = None       ,  **lowercase_			:Dict       ,  )    ->      Optional[int]:

												if isinstance(lowercase_       ,  torch.nn.Module ):
																		UpperCAmelCase					=			(
																		    'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
																		    'Please pass the parameters of the module instead.'
																		)
																		deprecate(
																		    'passing a `torch.nn.Module` to `ExponentialMovingAverage`'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_       ,  )
																		UpperCAmelCase					=			parameters.parameters()

																		# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
																		UpperCAmelCase					=			True

												if kwargs.get('max_value'       ,  lowercase_ ) is not None:
																		UpperCAmelCase					=			'The `max_value` argument is deprecated. Please use `decay` instead.'
																		deprecate('max_value'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			kwargs['max_value']

												if kwargs.get('min_value'       ,  lowercase_ ) is not None:
																		UpperCAmelCase					=			'The `min_value` argument is deprecated. Please use `min_decay` instead.'
																		deprecate('min_value'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		UpperCAmelCase					=			kwargs['min_value']

												UpperCAmelCase					=			list(lowercase_ )
												UpperCAmelCase					=			[p.clone().detach() for p in parameters]

												if kwargs.get('device'       ,  lowercase_ ) is not None:
																		UpperCAmelCase					=			'The `device` argument is deprecated. Please use `to` instead.'
																		deprecate('device'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_ )
																		self.to(device=kwargs['device'] )

												UpperCAmelCase					=			None

												UpperCAmelCase					=			decay
												UpperCAmelCase					=			min_decay
												UpperCAmelCase					=			update_after_step
												UpperCAmelCase					=			use_ema_warmup
												UpperCAmelCase					=			inv_gamma
												UpperCAmelCase					=			power
												UpperCAmelCase					=			0
												UpperCAmelCase					=			None  # set in `step()`

												UpperCAmelCase					=			model_cls
												UpperCAmelCase					=			model_config






						@classmethod
						def      UpperCAmelCase__					( cls			:int       ,  lowercase_			:Union[str, Any]       ,  lowercase_			:Any )    ->      "EMAModel":
												UpperCAmelCase				,   UpperCAmelCase					=			model_cls.load_config(lowercase_       ,  return_unused_kwargs=lowercase_ )
												UpperCAmelCase					=			model_cls.from_pretrained(lowercase_ )

												UpperCAmelCase					=			cls(model.parameters()       ,  model_cls=lowercase_       ,  model_config=model.config )

												ema_model.load_state_dict(lowercase_ )
												return ema_model






						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:List[str] )    ->      int:
												if self.model_cls is None:
																		raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.' )

												if self.model_config is None:
																		raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.' )

												UpperCAmelCase					=			self.model_cls.from_config(self.model_config )
												UpperCAmelCase					=			self.state_dict()
												state_dict.pop('shadow_params'       ,  lowercase_ )

												model.register_to_config(**lowercase_ )
												self.copy_to(model.parameters() )
												model.save_pretrained(lowercase_ )






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:int )    ->      float:
												UpperCAmelCase					=			max(0       ,  optimization_step - self.update_after_step - 1 )

												if step <= 0:
																		return 0.0

												if self.use_ema_warmup:
																		UpperCAmelCase					=			1 - (1 + step / self.inv_gamma) ** -self.power
												else:
																		UpperCAmelCase					=			(1 + step) / (10 + step)

												UpperCAmelCase					=			min(lowercase_       ,  self.decay )
												# make sure decay is not smaller than min_decay
												UpperCAmelCase					=			max(lowercase_       ,  self.min_decay )
												return cur_decay_value






						@torch.no_grad()
						def      UpperCAmelCase__					( self			:List[Any]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      Optional[int]:
												if isinstance(lowercase_       ,  torch.nn.Module ):
																		UpperCAmelCase					=			(
																		    'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
																		    'Please pass the parameters of the module instead.'
																		)
																		deprecate(
																		    'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'       ,  '1.0.0'       ,  lowercase_       ,  standard_warn=lowercase_       ,  )
																		UpperCAmelCase					=			parameters.parameters()

												UpperCAmelCase					=			list(lowercase_ )

												self.optimization_step += 1

												# Compute the decay factor for the exponential moving average.
												UpperCAmelCase					=			self.get_decay(self.optimization_step )
												UpperCAmelCase					=			decay
												UpperCAmelCase					=			1 - decay

												UpperCAmelCase					=			contextlib.nullcontext
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																		import deepspeed

												for s_param, param in zip(self.shadow_params       ,  lowercase_ ):
																		if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																								UpperCAmelCase					=			deepspeed.zero.GatheredParameters(lowercase_       ,  modifier_rank=lowercase_ )

																		with context_manager():
																								if param.requires_grad:
																														s_param.sub_(one_minus_decay * (s_param - param) )
																								else:
																														s_param.copy_(lowercase_ )






						def      UpperCAmelCase__					( self			:Tuple       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												UpperCAmelCase					=			list(lowercase_ )
												for s_param, param in zip(self.shadow_params       ,  lowercase_ ):
																		param.data.copy_(s_param.to(param.device ).data )






						def      UpperCAmelCase__					( self			:Dict       ,  lowercase_			:Tuple=None       ,  lowercase_			:Union[str, Any]=None )    ->      None:
												UpperCAmelCase					=			[
												    p.to(device=lowercase_       ,  dtype=lowercase_ ) if p.is_floating_point() else p.to(device=lowercase_ )
												    for p in self.shadow_params
												]






						def      UpperCAmelCase__					( self			:Union[str, Any] )    ->      dict:
												return {
												    "decay": self.decay,
												    "min_decay": self.min_decay,
												    "optimization_step": self.optimization_step,
												    "update_after_step": self.update_after_step,
												    "use_ema_warmup": self.use_ema_warmup,
												    "inv_gamma": self.inv_gamma,
												    "power": self.power,
												    "shadow_params": self.shadow_params,
												}






						def      UpperCAmelCase__					( self			:Optional[int]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												UpperCAmelCase					=			[param.detach().cpu().clone() for param in parameters]






						def      UpperCAmelCase__					( self			:Optional[Any]       ,  lowercase_			:Iterable[torch.nn.Parameter] )    ->      None:
												if self.temp_stored_params is None:
																		raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`' )
												for c_param, param in zip(self.temp_stored_params       ,  lowercase_ ):
																		param.data.copy_(c_param.data )

												# Better memory-wise.
												UpperCAmelCase					=			None






						def      UpperCAmelCase__					( self			:Union[str, Any]       ,  lowercase_			:dict )    ->      None:
												UpperCAmelCase					=			copy.deepcopy(lowercase_ )

												UpperCAmelCase					=			state_dict.get('decay'       ,  self.decay )
												if self.decay < 0.0 or self.decay > 1.0:
																		raise ValueError('Decay must be between 0 and 1' )

												UpperCAmelCase					=			state_dict.get('min_decay'       ,  self.min_decay )
												if not isinstance(self.min_decay       ,  lowercase_ ):
																		raise ValueError('Invalid min_decay' )

												UpperCAmelCase					=			state_dict.get('optimization_step'       ,  self.optimization_step )
												if not isinstance(self.optimization_step       ,  lowercase_ ):
																		raise ValueError('Invalid optimization_step' )

												UpperCAmelCase					=			state_dict.get('update_after_step'       ,  self.update_after_step )
												if not isinstance(self.update_after_step       ,  lowercase_ ):
																		raise ValueError('Invalid update_after_step' )

												UpperCAmelCase					=			state_dict.get('use_ema_warmup'       ,  self.use_ema_warmup )
												if not isinstance(self.use_ema_warmup       ,  lowercase_ ):
																		raise ValueError('Invalid use_ema_warmup' )

												UpperCAmelCase					=			state_dict.get('inv_gamma'       ,  self.inv_gamma )
												if not isinstance(self.inv_gamma       ,  (float, int) ):
																		raise ValueError('Invalid inv_gamma' )

												UpperCAmelCase					=			state_dict.get('power'       ,  self.power )
												if not isinstance(self.power       ,  (float, int) ):
																		raise ValueError('Invalid power' )

												UpperCAmelCase					=			state_dict.get('shadow_params'       ,  lowercase_ )
												if shadow_params is not None:
																		UpperCAmelCase					=			shadow_params
																		if not isinstance(self.shadow_params       ,  lowercase_ ):
																								raise ValueError('shadow_params must be a list' )
																		if not all(isinstance(lowercase_       ,  torch.Tensor ) for p in self.shadow_params ):
																								raise ValueError('shadow_params must all be Tensors' )





