
'''simple docstring'''



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							(				UpperCAmelCase__ ):
			lowerCamelCase							:							Tuple										=     [
			    'no_inference',
			    'no_cuda',
			    'no_tpu',
			    'no_speed',
			    'no_memory',
			    'no_env_print',
			    'no_multi_process',
			]



			def __init__(    self				,   **lowerCamelCase__				)	->						Dict:




									'''simple docstring'''



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

									lowercase__							       =				kwargs.pop("""torchscript"""				,   self.torchscript				)
									lowercase__							       =				kwargs.pop("""torch_xla_tpu_print_metrics"""				,   self.torch_xla_tpu_print_metrics				)
									lowercase__							       =				kwargs.pop("""fp16_opt_level"""				,   self.fpaa_opt_level				)
									super().__init__(**_SCREAMING_SNAKE_CASE				)

			lowerCamelCase							:							bool										=     field(default=UpperCAmelCase__						,  metadata={"""help""": """Trace the models using torchscript"""} )
			lowerCamelCase							:							bool										=     field(default=UpperCAmelCase__						,  metadata={"""help""": """Print Xla/PyTorch tpu metrics"""} )
			lowerCamelCase							:							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				)	->						Tuple["torch.device", int]:




									'''simple docstring'''



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



			@property
			def A__       (    self				)	->						Union[str, Any]:




									'''simple docstring'''



									return is_torch_tpu_available() and self.tpu



			@property
			def A__       (    self				)	->						int:




									'''simple docstring'''



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



			@property
			def A__       (    self				)	->						"torch.device":




									'''simple docstring'''



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



			@property
			def A__       (    self				)	->						Dict:




									'''simple docstring'''



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



			@property
			def A__       (    self				)	->						List[Any]:




									'''simple docstring'''



									return self.n_gpu > 0











"""simple docstring"""
from collections.abc import Callable

import numpy as np

def       _snake_case						(						UpperCamelCase						:		Callable			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float			,		UpperCamelCase						:		float						):
 UpperCAmelCase	:					Any	=	int(np.ceil((x_end - xa) / step_size						)						)
 UpperCAmelCase	:					Optional[Any]	=	np.zeros((n + 1,)						)
 UpperCAmelCase	:					Optional[int]	=	ya
 UpperCAmelCase	:					int	=	xa

 for k in range(UpperCamelCase						):
  UpperCAmelCase	:					Optional[int]	=	y[k] + step_size * ode_func(UpperCamelCase			,		y[k]						)
  UpperCAmelCase	:					Optional[int]	=	y[k] + (
      (step_size / 2) * (ode_func(UpperCamelCase			,		y[k]						) + ode_func(x + step_size			,		UpperCamelCase						))
  )
  x += step_size

 return y


if __name__ == "__main__":
      import doctest

      doctest.testmod()
