

from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
       import tensorflow as tf


_SCREAMING_SNAKE_CASE  :						List[Any]         =    logging.get_logger(__name__)






@dataclass
class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


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






  def __init__(							self		:	List[Any]			,     **__lowerCamelCase		:	Optional[Any]      )	->							int:
       for deprecated_arg in self.deprecated_args:
            if deprecated_arg in kwargs:
                 SCREAMING_SNAKE_CASE__					=					deprecated_arg[3:]
                 SCREAMING_SNAKE_CASE__					=					not kwargs.pop(__lowerCamelCase      )
                 logger.warning(
                     f'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                     f''' {positive_arg}={kwargs[positive_arg]}'''      )
       SCREAMING_SNAKE_CASE__					=					kwargs.pop('''tpu_name'''			,     self.tpu_name      )
       SCREAMING_SNAKE_CASE__					=					kwargs.pop('''device_idx'''			,     self.device_idx      )
       SCREAMING_SNAKE_CASE__					=					kwargs.pop('''eager_mode'''			,     self.eager_mode      )
       SCREAMING_SNAKE_CASE__					=					kwargs.pop('''use_xla'''			,     self.use_xla      )
       super().__init__(**__lowerCamelCase      )

  a     =      field(
      default=A__		,  metadata={"help": "Name of TPU"}		,  )
  a     =      field(
      default=0		,  metadata={"help": "CPU / GPU device index. Defaults to 0."}		,  )
  a     =      field(default=A__		,  metadata={"help": "Benchmark models in eager model."} )
  a     =      field(
      default=A__		,  metadata={
          "help": "Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."
      }		,  )






  @cached_property
  def    lowercase_	(							self		:	Tuple      )	->							Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:
       requires_backends(self			,     ['''tf''']      )
       SCREAMING_SNAKE_CASE__					=					None
       if self.tpu:
            try:
                 if self.tpu_name:
                      SCREAMING_SNAKE_CASE__					=					tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name      )
                 else:
                      SCREAMING_SNAKE_CASE__					=					tf.distribute.cluster_resolver.TPUClusterResolver()
            except ValueError:
                 SCREAMING_SNAKE_CASE__					=					None
       return tpu






  @cached_property
  def    lowercase_	(							self		:	Optional[int]      )	->							Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:
       requires_backends(self			,     ['''tf''']      )
       if self.is_tpu:
            tf.config.experimental_connect_to_cluster(self._setup_tpu      )
            tf.tpu.experimental.initialize_tpu_system(self._setup_tpu      )

            SCREAMING_SNAKE_CASE__					=					tf.distribute.TPUStrategy(self._setup_tpu      )
       else:
            # currently no multi gpu is allowed
            if self.is_gpu:
                 # TODO: Currently only single GPU is supported
                 tf.config.set_visible_devices(self.gpu_list[self.device_idx]			,     '''GPU'''      )
                 SCREAMING_SNAKE_CASE__					=					tf.distribute.OneDeviceStrategy(device=f'''/gpu:{self.device_idx}'''      )
            else:
                 tf.config.set_visible_devices([]			,     '''GPU'''      )  # disable GPU
                 SCREAMING_SNAKE_CASE__					=					tf.distribute.OneDeviceStrategy(device=f'''/cpu:{self.device_idx}'''      )

       return strategy






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							bool:
       requires_backends(self			,     ['''tf''']      )
       return self._setup_tpu is not None






  @property
  def    lowercase_	(							self		:	Dict      )	->							"tf.distribute.Strategy":
       requires_backends(self			,     ['''tf''']      )
       return self._setup_strategy






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							Tuple:
       requires_backends(self			,     ['''tf''']      )
       return tf.config.list_physical_devices('''GPU'''      )






  @property
  def    lowercase_	(							self		:	List[Any]      )	->							int:
       requires_backends(self			,     ['''tf''']      )
       if self.cuda:
            return len(self.gpu_list      )
       return 0







  @property
  def    lowercase_	(							self		:	Tuple      )	->							bool:
       return self.n_gpu > 0





from collections.abc import Callable

import numpy as np
def        UpperCAmelCase_     (		_A   ,	_A   ,	_A   ,	_A   ,	_A       ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__					=					int(np.ceil((x_end - xa) / step_size       )       )
     SCREAMING_SNAKE_CASE__					=					np.zeros((n + 1,)       )
     SCREAMING_SNAKE_CASE__					=					ya
     SCREAMING_SNAKE_CASE__					=					xa

     for k in range(_A       ):
          SCREAMING_SNAKE_CASE__					=					y[k] + step_size * ode_func(_A   ,	y[k]       )
          SCREAMING_SNAKE_CASE__					=					y[k] + (
              (step_size / 2) * (ode_func(_A   ,	y[k]       ) + ode_func(x + step_size   ,	_A       ))
          )
          x += step_size

     return y


if __name__ == "__main__":
       import doctest

       doctest.testmod()


