




"""simple docstring"""







import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowerCamelCase__        =		logging.get_logger(__name__)
def    __lowerCAmelCase    (_UpperCamelCase=None	,  _UpperCamelCase=None		):
	return field(default_factory=lambda: default	,  metadata=_SCREAMING_SNAKE_CASE		)




@dataclass
class 			A__	:
		A_						:	List[str]           =     list_field(
		    default=[]	,					metadata={
		        'help': (
		            'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
		            ' of all available models'
		        )
		    }	,					)

		A_						:	List[int]           =     list_field(
		    default=[8]	,					metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'})

		A_						:	List[int]           =     list_field(
		    default=[8, 3_2, 1_2_8, 5_1_2]	,					metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}	,					)

		A_						:	bool           =     field(
		    default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}	,					)
		A_						:	bool           =     field(
		    default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}	,					)
		A_						:	bool           =     field(
		    default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'})
		A_						:	bool           =     field(default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Use FP16 to accelerate inference.'})
		A_						:	bool           =     field(default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Benchmark training of model'})
		A_						:	bool           =     field(default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Verbose memory tracing'})
		A_						:	bool           =     field(
		    default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}	,					)
		A_						:	bool           =     field(
		    default=__SCREAMING_SNAKE_CASE	,					metadata={
		        'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
		    }	,					)
		A_						:	bool           =     field(default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Trace memory line by line'})
		A_						:	bool           =     field(default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Save result to a CSV file'})
		A_						:	bool           =     field(default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Save all print statements in a log file'})
		A_						:	bool           =     field(default=__SCREAMING_SNAKE_CASE	,					metadata={'help': 'Whether to print environment information'})
		A_						:	bool           =     field(
		    default=__SCREAMING_SNAKE_CASE	,					metadata={
		        'help': (
		            'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
		            ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
		            ' for debugging / testing and on TPU.'
		        )
		    }	,					)
		A_						:	str           =     field(
		    default=f"inference_time_{round(time())}.csv"	,					metadata={'help': 'CSV filename used if saving time results to csv.'}	,					)
		A_						:	str           =     field(
		    default=f"inference_memory_{round(time())}.csv"	,					metadata={'help': 'CSV filename used if saving memory results to csv.'}	,					)
		A_						:	str           =     field(
		    default=f"train_time_{round(time())}.csv"	,					metadata={'help': 'CSV filename used if saving time results to csv for training.'}	,					)
		A_						:	str           =     field(
		    default=f"train_memory_{round(time())}.csv"	,					metadata={'help': 'CSV filename used if saving memory results to csv for training.'}	,					)
		A_						:	str           =     field(
		    default=f"env_info_{round(time())}.csv"	,					metadata={'help': 'CSV filename used if saving environment information.'}	,					)
		A_						:	str           =     field(
		    default=f"log_{round(time())}.csv"	,					metadata={'help': 'Log filename used if print statements are saved in log.'}	,					)
		A_						:	int           =     field(default=3	,					metadata={'help': 'Times an experiment will be run.'})
		A_						:	bool           =     field(
		    default=__SCREAMING_SNAKE_CASE	,					metadata={
		        'help': (
		            'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
		            ' model weights.'
		        )
		    }	,					)

		def 							__lowerCamelCase							(				self			):
			warnings.warn(
			    f"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
			    ' are deprecated in general and it is advised to use external Benchmarking libraries '
			    ' to benchmark Transformer models.'	,		_snake_case	,		)

		def 							__lowerCamelCase							(				self			):
			return json.dumps(dataclasses.asdict(self			)	,		indent=2			)

		@property
		def 							__lowerCamelCase							(				self			):
			if len(self.models			) <= 0:
				raise ValueError(
				    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
				    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].'			)
			return self.models







		@property
		def 							__lowerCamelCase							(				self			):
			if not self.multi_process:
				return False
			elif self.is_tpu:
				logger.info('Multiprocessing is currently not possible on TPU.'			)
				return False
			else:
				return True

"""simple docstring"""







import os
def    __lowerCAmelCase    (_UpperCamelCase = "input.txt"		):

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

	__lowerCAmelCase      :						List[str]						      =				len(_UpperCamelCase		)
	__lowerCAmelCase      :						Tuple						      =				len(matrix[0]		)

	__lowerCAmelCase      :						int						      =				[[-1 for _ in range(_UpperCamelCase		)] for _ in range(_UpperCamelCase		)]
	for i in range(_UpperCamelCase		):
		__lowerCAmelCase      :						Any						      =				matrix[i][0]

	for j in range(1	,  _UpperCamelCase		):
		for i in range(_UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				minimal_path_sums[i][j - 1] + matrix[i][j]

		for i in range(1	,  _UpperCamelCase		):
			__lowerCAmelCase      :						Optional[Any]						      =				min(
			    minimal_path_sums[i][j]	,  minimal_path_sums[i - 1][j] + matrix[i][j]		)

		for i in range(rows - 2	,  -1	,  -1		):
			__lowerCAmelCase      :						List[str]						      =				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() = }')