code: 
  
  
  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
  
  
  lowerCAmelCase					 =	logging.get_logger(__name__)
  
  
  
  
  
  class    A					(     _UpperCAmelCase				):
  							UpperCamelCase_     :							Dict =42
  							UpperCamelCase_     :							Dict =42
  							UpperCamelCase_     :							List[str] ='''PyTorch'''
  
  
  							@property
  							def     _A (self      ):
  									return torch.__version__
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    self._prepare_inference_func(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									return self._measure_speed(_inference      )
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    self._prepare_inference_func(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									return self._measure_memory(_inference      )
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    self._prepare_train_func(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									return self._measure_speed(_train      )
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    self._prepare_train_func(lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      )
  									return self._measure_memory(_train      )
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    self.config_dict[model_name]
  
  									if self.args.torchscript:
  											__lowercase=    True
  
  									__lowercase=    (
  									    hasattr(lowerCAmelCase   ,       'architectures'      )
  									    and isinstance(config.architectures   ,       lowerCAmelCase      )
  									    and len(config.architectures      ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  											try:
  													__lowercase=    config.architectures[0]
  													__lowercase=    __import__('transformers'   ,       fromlist=[model_class]      )
  													__lowercase=    getattr(lowerCAmelCase   ,       lowerCAmelCase      )
  													__lowercase=    model_cls(lowerCAmelCase      )
  											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=    MODEL_MAPPING[config.__class__](lowerCAmelCase      )
  
  									model.eval()
  									model.to(self.args.device      )
  
  									# encoder-decoder has vocab size saved differently
  									__lowercase=    config.vocab_size if hasattr(lowerCAmelCase   ,       'vocab_size'      ) else config.encoder.vocab_size
  									__lowercase=    torch.randint(lowerCAmelCase   ,       (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=    torch.jit.trace(lowerCAmelCase   ,       lowerCAmelCase      )
  									else:
  											__lowercase=    model
  
  									def encoder_decoder_forward():
  											with torch.no_grad():
  													__lowercase=    inference_model(lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase      )
  											return outputs
  
  									def encoder_forward():
  											with torch.no_grad():
  													__lowercase=    inference_model(lowerCAmelCase      )
  											return outputs
  
  									__lowercase=    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
  									return _forward
  
  
  							def     _A (self   ,       lowerCAmelCase   ,       lowerCAmelCase   ,       lowerCAmelCase      ):
  									__lowercase=    self.config_dict[model_name]
  
  									__lowercase=    (
  									    hasattr(lowerCAmelCase   ,       'architectures'      )
  									    and isinstance(config.architectures   ,       lowerCAmelCase      )
  									    and len(config.architectures      ) > 0
  									)
  									if not self.args.only_pretrain_model and has_model_class_in_config:
  											try:
  													__lowercase=    config.architectures[0]
  													__lowercase=    __import__('transformers'   ,       fromlist=[model_class]      )
  													__lowercase=    getattr(lowerCAmelCase   ,       lowerCAmelCase      )
  													__lowercase=    model_cls(lowerCAmelCase      )
  											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=    MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase      )
  
  									if self.args.torchscript:
  											raise NotImplementedError('Training for torchscript is currently not implemented'      )
  									else:
  											__lowercase=    model
  
  									model.train()
  									model.to(self.args.device      )
  
  									# encoder-decoder has vocab size saved differently
  									__lowercase=    config.vocab_size if hasattr(lowerCAmelCase   ,       'vocab_size'      ) else config.encoder.vocab_size
  									__lowercase=    torch.randint(lowerCAmelCase   ,       (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=    train_model(lowerCAmelCase   ,       labels=lowerCAmelCase      )[0]
  											loss.backward()
  											return loss
  
  									def compute_loss_and_backprob_encoder_decoder():
  											__lowercase=    train_model(lowerCAmelCase   ,       decoder_input_ids=lowerCAmelCase   ,       labels=lowerCAmelCase      )[0]
  											loss.backward()
  											return loss
  
  									__lowercase=    (
  									    compute_loss_and_backprob_encoder_decoder
  									    if config.is_encoder_decoder
  									    else compute_loss_and_backprob_encoder
  									)
  									return _train
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  
  
  
  
  
  
  									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(
  													    lowerCAmelCase   ,       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=    timeit.repeat(
  											    lowerCAmelCase   ,       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(lowerCAmelCase      ) / 10.0
  									except RuntimeError as e:
  											self.print_fn(f'Doesn\'t fit on GPU. {e}'      )
  											return "N/A"
  
  
  
  
  
  
  
  							def     _A (self   ,       lowerCAmelCase      ):
  
  
  
  
  
  
  									try:
  											if self.args.trace_memory_line_by_line:
  													__lowercase=    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=    '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=    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
  															__lowercase=    nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase      )
  															__lowercase=    meminfo.used
  															__lowercase=    Memory(lowerCAmelCase      )
  															# shutdown nvml
  															nvml.nvmlShutdown()
  											else:
  													# cpu
  													__lowercase=    measure_peak_memory_cpu(lowerCAmelCase      )
  													__lowercase=    Memory(lowerCAmelCase      ) if isinstance(lowerCAmelCase   ,       lowerCAmelCase      ) else memory_bytes
  
  											if self.args.trace_memory_line_by_line:
  													__lowercase=    stop_memory_tracing(lowerCAmelCase      )
  											else:
  													__lowercase=    None
  
  											return memory, summary
  									except RuntimeError as e:
  											self.print_fn(f'Doesn\'t fit on GPU. {e}'      )
  											return "N/A", None
  
  
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  from enum import Enum
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							List[Any] =24
  							UpperCamelCase_     :							List[Any] =21
  							UpperCamelCase_     :							int =18
  							UpperCamelCase_     :							Optional[int] =15
  							UpperCamelCase_     :							Tuple =12
  							UpperCamelCase_     :							List[str] =9
  							UpperCamelCase_     :							Any =6
  							UpperCamelCase_     :							List[str] =3
  							UpperCamelCase_     :							str =2
  							UpperCamelCase_     :							str =1
  							UpperCamelCase_     :							Optional[int] =-1
  							UpperCamelCase_     :							List[Any] =-2
  							UpperCamelCase_     :							str =-3
  							UpperCamelCase_     :							Tuple =-6
  							UpperCamelCase_     :							Tuple =-9
  							UpperCamelCase_     :							Optional[int] =-12
  							UpperCamelCase_     :							str =-15
  							UpperCamelCase_     :							Optional[Any] =-18
  							UpperCamelCase_     :							Any =-21
  							UpperCamelCase_     :							List[str] =-24
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							str =8
  							UpperCamelCase_     :							Optional[Any] =7
  							UpperCamelCase_     :							Tuple =6
  							UpperCamelCase_     :							List[Any] =5
  							UpperCamelCase_     :							Optional[Any] =4
  							UpperCamelCase_     :							Union[str, Any] =3
  							UpperCamelCase_     :							Union[str, Any] =2
  							UpperCamelCase_     :							int =1
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    SIUnit[known_prefix.lower()]
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    SIUnit[unknown_prefix.lower()]
  		__lowercase=    known_amount * (
  		    1_0 ** (known_prefix.value - unknown_prefix.value)
  		)
  		return unknown_amount
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    BinaryUnit[known_prefix.lower()]
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    BinaryUnit[unknown_prefix.lower()]
  		__lowercase=    known_amount * (
  		    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
  		)
  		return unknown_amount
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  