
'''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


lowercase					:							List[str]   =      logging.get_logger(__name__)


@dataclass
class 		__UpperCAmelCase   (      _lowerCamelCase		):
							__lowercase   	= [
							    """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:
										_snake_case     		=	deprecated_arg[3:]
										_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]}'						)
								_snake_case     		=	kwargs.pop('tpu_name'					,     self.tpu_name						)
								_snake_case     		=	kwargs.pop('device_idx'					,     self.device_idx						)
								_snake_case     		=	kwargs.pop('eager_mode'					,     self.eager_mode						)
								_snake_case     		=	kwargs.pop('use_xla'					,     self.use_xla						)
								super().__init__(**lowerCAmelCase_						)

							__lowercase   	= field(
							    default=_lowerCamelCase		,     metadata={"""help""": """Name of TPU"""}		,     )
							__lowercase   	= field(
							    default=0		,     metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}		,     )
							__lowercase   	= field(default=_lowerCamelCase		,     metadata={"""help""": """Benchmark models in eager model."""}		)
							__lowercase   	= field(
							    default=_lowerCamelCase		,     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']						)
								_snake_case     		=	None
								if self.tpu:
									try:
										if self.tpu_name:
											_snake_case     		=	tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name						)
										else:
											_snake_case     		=	tf.distribute.cluster_resolver.TPUClusterResolver()
									except ValueError:
										_snake_case     		=	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						)

									_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'						)
										_snake_case     		=	tf.distribute.OneDeviceStrategy(device=F'/gpu:{self.device_idx}'						)
									else:
										tf.config.set_visible_devices([]					,     'GPU'						)  # disable GPU
										_snake_case     		=	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'''







from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def        SCREAMING_SNAKE_CASE__			(     __A    ,       __A    ,       __A = "x"    ,       __A = 10**-10    ,       __A = 1    ,       ) ->			complex:
	_snake_case     		=	symbols(__A			)
	_snake_case     		=	lambdify(__A    ,       __A			)
	_snake_case     		=	lambdify(__A    ,       diff(__A    ,       __A			)			)

	_snake_case     		=	starting_point

	while True:
		if diff_function(__A			) != 0:
			_snake_case     		=	prev_guess - multiplicity * func(__A			) / diff_function(
			    __A			)
		else:
			raise ZeroDivisionError('Could not find root'			) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess			) < precision:
			return next_guess

		_snake_case     		=	next_guess


# Let's Execute
if __name__ == "__main__":
					# Find root of trigonometric function
					# Find value of pi
					print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')

					# Find root of polynomial
					# Find fourth Root of 5
					print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}''')

					# Find value of e
					print(
					    "The root of log(y) - 1 = 0 is ",
					    F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
					)

					# Exponential Roots
					print(
					    "The root of exp(x) - 1 = 0 is",
					    F'''{newton_raphson("exp(x) - 1", 10, precision=0.005)}''',
					)

					# Find root of cos(x)
					print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')

