



'''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


_lowerCAmelCase      						=	logging.get_logger(__name__)






@dataclass
class        lowerCAmelCase_(				SCREAMING_SNAKE_CASE_     ):
  '''simple docstring'''




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




  def __init__(    self  ,**__UpperCAmelCase      )    ->		str:
      for deprecated_arg in self.deprecated_args:
          if deprecated_arg in kwargs:
              lowerCAmelCase__ :  Union[str, Any]							      =					deprecated_arg[3:]
              setattr(self  ,__UpperCAmelCase  ,not kwargs.pop(__UpperCAmelCase      )      )
              logger.warning(
                  F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
                  F""" {positive_arg}={kwargs[positive_arg]}"""      )

      lowerCAmelCase__ :  Union[str, Any]							      =					kwargs.pop("""torchscript"""  ,self.torchscript      )
      lowerCAmelCase__ :  Any							      =					kwargs.pop("""torch_xla_tpu_print_metrics"""  ,self.torch_xla_tpu_print_metrics      )
      lowerCAmelCase__ :  Optional[Any]							      =					kwargs.pop("""fp16_opt_level"""  ,self.fpaa_opt_level      )
      super().__init__(**__UpperCAmelCase      )

  __lowercase							:      bool				=      field(default=SCREAMING_SNAKE_CASE_						, metadata={'''help''': '''Trace the models using torchscript'''}     )
  __lowercase							:      bool				=      field(default=SCREAMING_SNAKE_CASE_						, metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}     )
  __lowercase							:      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 							UpperCAmelCase_	(    self      )    ->		Tuple["torch.device", int]:
      requires_backends(self  ,["""torch"""]      )
      logger.info("""PyTorch: setting up devices"""      )
      if not self.cuda:
          lowerCAmelCase__ :  str							      =					torch.device("""cpu"""      )
          lowerCAmelCase__ :  List[str]							      =					0
      elif is_torch_tpu_available():
          lowerCAmelCase__ :  Union[str, Any]							      =					xm.xla_device()
          lowerCAmelCase__ :  Dict							      =					0
      else:
          lowerCAmelCase__ :  str							      =					torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""      )
          lowerCAmelCase__ :  Union[str, Any]							      =					torch.cuda.device_count()
      return device, n_gpu




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Optional[int]:
      return is_torch_tpu_available() and self.tpu




  @property
  def 							UpperCAmelCase_	(    self      )    ->		int:
      requires_backends(self  ,["""torch"""]      )
      # TODO(PVP): currently only single GPU is supported
      return torch.cuda.current_device()




  @property
  def 							UpperCAmelCase_	(    self      )    ->		"torch.device":
      requires_backends(self  ,["""torch"""]      )
      return self._setup_devices[0]




  @property
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      requires_backends(self  ,["""torch"""]      )
      return self._setup_devices[1]






  @property
  def 							UpperCAmelCase_	(    self      )    ->		Dict:
      return self.n_gpu > 0









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

import numpy as np



def      __lowerCamelCase   (							a_     :		Callable ,   a_     :		float ,   a_     :		float ,   a_     :		float ,   a_     :		float							)			->      np.ndarray:
      __SCREAMING_SNAKE_CASE   :List[Any]						=  int(np.ceil((x_end - xa) / step_size							)							)
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  np.zeros((n + 1,)							)
      __SCREAMING_SNAKE_CASE   :int						=  ya
      __SCREAMING_SNAKE_CASE   :str						=  xa

      for k in range(a_							):
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  y[k] + step_size * ode_func(a_ ,   y[k]							)
            x += step_size

      return y


if __name__ == "__main__":
  import doctest

  doctest.testmod()