





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


_a						=   logging.get_logger(__name__)






@dataclass
class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			lowerCAmelCase_							  =		[
			    """no_inference""",
			    """no_cuda""",
			    """no_tpu""",
			    """no_speed""",
			    """no_memory""",
			    """no_env_print""",
			    """no_multi_process""",
			]





			def __init__(					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







				for deprecated_arg in self.deprecated_args:
					if deprecated_arg in kwargs:
						lowerCamelCase__              =	deprecated_arg[3:]
						setattr(self ,						__lowerCAmelCase ,						not kwargs.pop(__lowerCAmelCase							)							)
						logger.warning(
						    F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
						    F' {positive_arg}={kwargs[positive_arg]}'							)

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

			lowerCAmelCase_							  =		field(default=lowerCAmelCase    ,       metadata={"""help""": """Trace the models using torchscript"""}				)
			lowerCAmelCase_							  =		field(default=lowerCAmelCase    ,       metadata={"""help""": """Print Xla/PyTorch tpu metrics"""}				)
			lowerCAmelCase_							  =		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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return is_torch_tpu_available() and self.tpu





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				return self.n_gpu > 0










def lowerCAmelCase__(__snake_case							)       ->   int:







	'''simple docstring'''







	if not grid or not grid[0]:
		raise TypeError('''The grid does not contain the appropriate information'''							)

	for cell_n in range(1 ,len(grid[0]							)							):
		grid[0][cell_n] += grid[0][cell_n - 1]
	lowerCamelCase__              =	grid[0]

	for row_n in range(1 ,len(__snake_case							)							):
		lowerCamelCase__              =	grid[row_n]
		lowerCamelCase__              =	fill_row(__snake_case ,__snake_case							)
		lowerCamelCase__              =	grid[row_n]

	return grid[-1][-1]





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   list:







	'''simple docstring'''







	current_row[0] += row_above[0]
	for cell_n in range(1 ,len(__snake_case							)							):
		current_row[cell_n] += min(current_row[cell_n - 1] ,row_above[cell_n]							)

	return current_row


if __name__ == "__main__":
			import doctest

			doctest.testmod()



