


'''simple docstring'''
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


__snake_case							   =  logging.get_logger(__name__)
@dataclass
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       [
		    'no_inference',
		    'no_cuda',
		    'no_tpu',
		    'no_speed',
		    'no_memory',
		    'no_env_print',
		    'no_multi_process',
		]




		def __init__(	self					,     **UpperCamelCase_							):

							'''simple docstring'''


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

		_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 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							requires_backends(self					,     ['''tf''']							)
							UpperCamelCase__     :Optional[int]							      =				None
							if self.tpu:
												try:
																	if self.tpu_name:
																						UpperCamelCase__     :Optional[int]							      =				tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name							)
																	else:
																						UpperCamelCase__     :Tuple							      =				tf.distribute.cluster_resolver.TPUClusterResolver()
												except ValueError:
																	UpperCamelCase__     :str							      =				None
							return tpu




		@cached_property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							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							)

												UpperCamelCase__     :Optional[Any]							      =				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'''							)
																	UpperCamelCase__     :Optional[int]							      =				tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''							)
												else:
																	tf.config.set_visible_devices([]					,     '''GPU'''							)  # disable GPU
																	UpperCamelCase__     :Dict							      =				tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''							)

							return strategy




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							requires_backends(self					,     ['''tf''']							)
							return self._setup_tpu is not None




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							requires_backends(self					,     ['''tf''']							)
							return self._setup_strategy




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							requires_backends(self					,     ['''tf''']							)
							return tf.config.list_physical_devices('''GPU'''							)




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							requires_backends(self					,     ['''tf''']							)
							if self.cuda:
												return len(self.gpu_list							)
							return 0




		@property
		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return self.n_gpu > 0



'''simple docstring'''
import numpy as np


def     a   (    __a				,      __a				,      __a				,      __a				,      __a     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :Tuple							      =				int(np.ceil((x_end - xa) / h     )     )
					UpperCamelCase__     :Optional[int]							      =				np.zeros((n + 1,)     )
					UpperCamelCase__     :List[str]							      =				ya
					UpperCamelCase__     :Tuple							      =				xa

					for k in range(__a     ):
										UpperCamelCase__     :Dict							      =				f(__a				,      y[k]     )
										UpperCamelCase__     :List[Any]							      =				f(x + 0.5 * h				,      y[k] + 0.5 * h * ka     )
										UpperCamelCase__     :Dict							      =				f(x + 0.5 * h				,      y[k] + 0.5 * h * ka     )
										UpperCamelCase__     :List[Any]							      =				f(x + h				,      y[k] + h * ka     )
										UpperCamelCase__     :List[str]							      =				y[k] + (1 / 6) * h * (ka + 2 * ka + 2 * ka + ka)
										x += h

					return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()