"""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


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							42
				lowerCamelCase__							=							42
				lowerCamelCase__							=							"PyTorch"

				@property
				def 							A			(  self							:     List[str]  ):


										'''simple docstring'''
										return torch.__version__

				def 							A			(  self							:     Dict							,     _lowercase							:     str							,     _lowercase							:     int							,     _lowercase							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self._prepare_inference_func(_lowercase							,     _lowercase							,     _lowercase  )
										return self._measure_speed(_inference  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     str							,     _lowercase							:     int							,     _lowercase							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self._prepare_inference_func(_lowercase							,     _lowercase							,     _lowercase  )
										return self._measure_memory(_inference  )

				def 							A			(  self							:     List[Any]							,     _lowercase							:     str							,     _lowercase							:     int							,     _lowercase							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self._prepare_train_func(_lowercase							,     _lowercase							,     _lowercase  )
										return self._measure_speed(_train  )

				def 							A			(  self							:     Any							,     _lowercase							:     str							,     _lowercase							:     int							,     _lowercase							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self._prepare_train_func(_lowercase							,     _lowercase							,     _lowercase  )
										return self._measure_memory(_train  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     str							,     _lowercase							:     int							,     _lowercase							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.config_dict[model_name]

										if self.args.torchscript:
																UpperCAmelCase__       						=		True

										UpperCAmelCase__       						=		(
										    hasattr(_lowercase							,     """architectures"""  )
										    and isinstance(config.architectures							,     _lowercase  )
										    and len(config.architectures  ) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																						UpperCAmelCase__       						=		config.architectures[0]
																						UpperCAmelCase__       						=		__import__("""transformers"""							,     fromlist=[model_class]  )
																						UpperCAmelCase__       						=		getattr(_lowercase							,     _lowercase  )
																						UpperCAmelCase__       						=		model_cls(_lowercase  )
																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:
																UpperCAmelCase__       						=		MODEL_MAPPING[config.__class__](_lowercase  )

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

										# encoder-decoder has vocab size saved differently
										UpperCAmelCase__       						=		config.vocab_size if hasattr(_lowercase							,     """vocab_size"""  ) else config.encoder.vocab_size
										UpperCAmelCase__       						=		torch.randint(_lowercase							,     (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():
																						UpperCAmelCase__       						=		torch.jit.trace(_lowercase							,     _lowercase  )
										else:
																UpperCAmelCase__       						=		model

										def encoder_decoder_forward():
																with torch.no_grad():
																						UpperCAmelCase__       						=		inference_model(_lowercase							,     decoder_input_ids=_lowercase  )
																return outputs

										def encoder_forward():
																with torch.no_grad():
																						UpperCAmelCase__       						=		inference_model(_lowercase  )
																return outputs

										UpperCAmelCase__       						=		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
										return _forward

				def 							A			(  self							:     Optional[Any]							,     _lowercase							:     str							,     _lowercase							:     int							,     _lowercase							:     int  ):


										'''simple docstring'''
										UpperCAmelCase__       						=		self.config_dict[model_name]

										UpperCAmelCase__       						=		(
										    hasattr(_lowercase							,     """architectures"""  )
										    and isinstance(config.architectures							,     _lowercase  )
										    and len(config.architectures  ) > 0
										)
										if not self.args.only_pretrain_model and has_model_class_in_config:
																try:
																						UpperCAmelCase__       						=		config.architectures[0]
																						UpperCAmelCase__       						=		__import__("""transformers"""							,     fromlist=[model_class]  )
																						UpperCAmelCase__       						=		getattr(_lowercase							,     _lowercase  )
																						UpperCAmelCase__       						=		model_cls(_lowercase  )
																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:
																UpperCAmelCase__       						=		MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowercase  )

										if self.args.torchscript:
																raise NotImplementedError("""Training for torchscript is currently not implemented"""  )
										else:
																UpperCAmelCase__       						=		model

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

										# encoder-decoder has vocab size saved differently
										UpperCAmelCase__       						=		config.vocab_size if hasattr(_lowercase							,     """vocab_size"""  ) else config.encoder.vocab_size
										UpperCAmelCase__       						=		torch.randint(_lowercase							,     (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():
																UpperCAmelCase__       						=		train_model(_lowercase							,     labels=_lowercase  )[0]
																loss.backward()
																return loss

										def compute_loss_and_backprob_encoder_decoder():
																UpperCAmelCase__       						=		train_model(_lowercase							,     decoder_input_ids=_lowercase							,     labels=_lowercase  )[0]
																loss.backward()
																return loss

										UpperCAmelCase__       						=		(
										    compute_loss_and_backprob_encoder_decoder
										    if config.is_encoder_decoder
										    else compute_loss_and_backprob_encoder
										)
										return _train

				def 							A			(  self							:     Union[str, Any]							,     _lowercase							:     str  ):


										'''simple docstring'''
										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(
																						    _lowercase							,     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
																UpperCAmelCase__       						=		timeit.repeat(
																    _lowercase							,     repeat=self.args.repeat							,     number=1_0							,     )

																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(_lowercase  ) / 10.0
										except RuntimeError as e:
																self.print_fn(f'Doesn\'t fit on GPU. {e}'  )
																return "N/A"

				def 							A			(  self							:     Tuple							,     _lowercase							:     Callable[[], None]  ):


										'''simple docstring'''
										try:
																if self.args.trace_memory_line_by_line:
																						UpperCAmelCase__       						=		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."""  )
																												UpperCAmelCase__       						=		"""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()
																												UpperCAmelCase__       						=		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx  )
																												UpperCAmelCase__       						=		nvml.nvmlDeviceGetMemoryInfo(_lowercase  )
																												UpperCAmelCase__       						=		meminfo.used
																												UpperCAmelCase__       						=		Memory(_lowercase  )
																												# shutdown nvml
																												nvml.nvmlShutdown()
																else:
																						# cpu
																						UpperCAmelCase__       						=		measure_peak_memory_cpu(_lowercase  )
																						UpperCAmelCase__       						=		Memory(_lowercase  ) if isinstance(_lowercase							,     _lowercase  ) else memory_bytes

																if self.args.trace_memory_line_by_line:
																						UpperCAmelCase__       						=		stop_memory_tracing(_lowercase  )
																else:
																						UpperCAmelCase__       						=		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 __future__ import annotations


def 	a__   (		UpperCAmelCase__							:      list      )->						int | float:
						UpperCAmelCase__       						=		sorted(UpperCAmelCase__      )
						UpperCAmelCase__       						=		len(UpperCAmelCase__      )
						UpperCAmelCase__       						=		length >> 1
						return (
						    (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
						    if length % 2 == 0
						    else sorted_list[mid_index]
						)


def 	a__   (		)->						Dict:
						import doctest

						doctest.testmod()


if __name__ == "__main__":
						main()

