




"""simple docstring"""




import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
							import torch

							from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
							import pyanvml.pyanvml as nvml


a_            =   logging.get_logger(__name__)

class        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									=42
		UpperCamelCase									=42
		UpperCamelCase									="PyTorch"
		@property
		def 					_lowerCamelCase    (		self   )		->   Optional[int]:
					return torch.__version__
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   float:
					__lowercase	:       Optional[int]				=   self._prepare_inference_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_speed(_inference   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   [Memory, Optional[MemorySummary]]:
					__lowercase	:       str				=   self._prepare_inference_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_memory(_inference   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   float:
					__lowercase	:       Any				=   self._prepare_train_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_speed(_train   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   [Memory, Optional[MemorySummary]]:
					__lowercase	:       int				=   self._prepare_train_func(UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )
					return self._measure_memory(_train   )
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Callable[[], None]:
					__lowercase	:       List[Any]				=   self.config_dict[model_name]

					if self.args.torchscript:
								__lowercase	:       Tuple				=   True

					__lowercase	:       List[str]				=   (
					    hasattr(UpperCamelCase_							,		'''architectures'''   )
					    and isinstance(config.architectures							,		UpperCamelCase_   )
					    and len(config.architectures   ) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											__lowercase	:       Dict				=   config.architectures[0]
											__lowercase	:       Union[str, Any]				=   __import__('''transformers'''							,		fromlist=[model_class]   )
											__lowercase	:       Union[str, Any]				=   getattr(UpperCamelCase_							,		UpperCamelCase_   )
											__lowercase	:       List[str]				=   model_cls(UpperCamelCase_   )
								except ImportError:
											raise ImportError(
											    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
											    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''   )
					else:
								__lowercase	:       Union[str, Any]				=   MODEL_MAPPING[config.__class__](UpperCamelCase_   )

					model.eval()
					model.to(self.args.device   )

					# encoder-decoder has vocab size saved differently
					__lowercase	:       Dict				=   config.vocab_size if hasattr(UpperCamelCase_							,		'''vocab_size'''   ) else config.encoder.vocab_size
					__lowercase	:       str				=   torch.randint(UpperCamelCase_							,		(batch_size, sequence_length)							,		dtype=torch.long							,		device=self.args.device   )

					if self.args.fpaa:
								logger.info('''Running training in Mixed Precision...'''   )
								if not self.args.is_gpu:
											raise ValueError('''Mixed precision is possible only for GPU.'''   )
								# amp seems to have memory leaks so that memory usage
								# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
								model.half()

					if self.args.torchscript:
								with torch.no_grad():
											__lowercase	:       Tuple				=   torch.jit.trace(UpperCamelCase_							,		UpperCamelCase_   )
					else:
								__lowercase	:       int				=   model

					def encoder_decoder_forward():
								with torch.no_grad():
											__lowercase	:       List[Any]				=   inference_model(UpperCamelCase_							,		decoder_input_ids=UpperCamelCase_   )
								return outputs

					def encoder_forward():
								with torch.no_grad():
											__lowercase	:       Dict				=   inference_model(UpperCamelCase_   )
								return outputs

					__lowercase	:       Tuple				=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
					return _forward
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_							,		UpperCamelCase_							,		UpperCamelCase_   )		->   Callable[[], None]:
					__lowercase	:       Optional[int]				=   self.config_dict[model_name]

					__lowercase	:       List[str]				=   (
					    hasattr(UpperCamelCase_							,		'''architectures'''   )
					    and isinstance(config.architectures							,		UpperCamelCase_   )
					    and len(config.architectures   ) > 0
					)
					if not self.args.only_pretrain_model and has_model_class_in_config:
								try:
											__lowercase	:       Optional[int]				=   config.architectures[0]
											__lowercase	:       List[Any]				=   __import__('''transformers'''							,		fromlist=[model_class]   )
											__lowercase	:       int				=   getattr(UpperCamelCase_							,		UpperCamelCase_   )
											__lowercase	:       Dict				=   model_cls(UpperCamelCase_   )
								except ImportError:
											raise ImportError(
											    F"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
											    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''   )
					else:
								__lowercase	:       Tuple				=   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase_   )

					if self.args.torchscript:
								raise NotImplementedError('''Training for torchscript is currently not implemented'''   )
					else:
								__lowercase	:       Any				=   model

					model.train()
					model.to(self.args.device   )

					# encoder-decoder has vocab size saved differently
					__lowercase	:       Optional[Any]				=   config.vocab_size if hasattr(UpperCamelCase_							,		'''vocab_size'''   ) else config.encoder.vocab_size
					__lowercase	:       Union[str, Any]				=   torch.randint(UpperCamelCase_							,		(batch_size, sequence_length)							,		dtype=torch.long							,		device=self.args.device   )

					if self.args.fpaa:
								logger.info('''Running training in Mixed Precision...'''   )
								if not self.args.is_gpu:
											raise ValueError('''Mixed precision is possible only for GPU.'''   )

								# amp seems to have memory leaks so that memory usage
								# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
								model.half()

					def compute_loss_and_backprob_encoder():
								__lowercase	:       str				=   train_model(UpperCamelCase_							,		labels=UpperCamelCase_   )[0]
								loss.backward()
								return loss

					def compute_loss_and_backprob_encoder_decoder():
								__lowercase	:       List[Any]				=   train_model(UpperCamelCase_							,		decoder_input_ids=UpperCamelCase_							,		labels=UpperCamelCase_   )[0]
								loss.backward()
								return loss

					__lowercase	:       Optional[Any]				=   (
					    compute_loss_and_backprob_encoder_decoder
					    if config.is_encoder_decoder
					    else compute_loss_and_backprob_encoder
					)
					return _train
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   float:
					try:
								if self.args.is_tpu or self.args.torchscript:
											# run additional 10 times to stabilize compilation for tpu and torchscript
											logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''   )
											timeit.repeat(
											    UpperCamelCase_							,		repeat=1							,		number=5							,		)

								# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
								__lowercase	:       List[str]				=   timeit.repeat(
								    UpperCamelCase_							,		repeat=self.args.repeat							,		number=10							,		)

								if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
											import torch_xla.debug.metrics as met

											self.print_fn(met.metrics_report()   )

								return min(UpperCamelCase_   ) / 10.0
					except RuntimeError as e:
								self.print_fn(F"""Doesn't fit on GPU. {e}"""   )
								return "N/A"
		def 					_lowerCamelCase    (		self							,		UpperCamelCase_   )		->   [Memory, MemorySummary]:
					try:
								if self.args.trace_memory_line_by_line:
											__lowercase	:       List[Any]				=   start_memory_tracing('''transformers'''   )

								if self.args.is_tpu:
											# tpu
											raise NotImplementedError(
											    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
											    ''' `--no-memory` or `args.memory=False`'''   )
								elif self.args.is_gpu:
											if not is_pyanvml_available():
														logger.warning(
														    '''py3nvml not installed, we won\'t log GPU memory usage. '''
														    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''   )
														__lowercase	:       List[str]				=   '''N/A'''
											else:
														logger.info(
														    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
														    ''' on the same GPU.'''   )
														# init nvml
														nvml.nvmlInit()
														func()
														__lowercase	:       Optional[int]				=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
														__lowercase	:       Any				=   nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase_   )
														__lowercase	:       Any				=   meminfo.used
														__lowercase	:       str				=   Memory(UpperCamelCase_   )
														# shutdown nvml
														nvml.nvmlShutdown()
								else:
											# cpu
											__lowercase	:       Optional[Any]				=   measure_peak_memory_cpu(UpperCamelCase_   )
											__lowercase	:       List[str]				=   Memory(UpperCamelCase_   ) if isinstance(UpperCamelCase_							,		UpperCamelCase_   ) else memory_bytes

								if self.args.trace_memory_line_by_line:
											__lowercase	:       Any				=   stop_memory_tracing(UpperCamelCase_   )
								else:
											__lowercase	:       int				=   None

								return memory, summary
					except RuntimeError as e:
								self.print_fn(F"""Doesn't fit on GPU. {e}"""   )
								return "N/A", None









"""simple docstring"""




from collections.abc import Callable

a_            =   Callable[[float], float]  # type alias for a real -> real function







def       __UpperCAmelCase      (				__UpperCamelCase					,							__UpperCamelCase					,							__UpperCamelCase					,							):
			__lowercase	:       str				=   float(__UpperCamelCase					)
			while True:
						try:
									__lowercase	:       Dict				=   prev_guess - function(__UpperCamelCase					) / derivative(__UpperCamelCase					)
						except ZeroDivisionError:
									raise ZeroDivisionError('''Could not find root'''					) from None
						if abs(prev_guess - next_guess					) < 10**-5:
									return next_guess
						__lowercase	:       List[Any]				=   next_guess







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return (x**3) - (2 * x) - 5







def       __UpperCAmelCase      (				__UpperCamelCase					):
			return 3 * (x**2) - 2


if __name__ == "__main__":
			print(newton(f, fa, 3))



