code: 
  '''simple docstring'''
  
  
  
  import csv
  from collections import defaultdict
  from dataclasses import dataclass, field
  from typing import List, Optional
  
  import matplotlib.pyplot as plt
  import numpy as np
  from matplotlib.ticker import ScalarFormatter
  
  from transformers import HfArgumentParser
  
  
  
  
  
  def  _A		(							lowercase__=None		,		lowercase__=None	):
  						return field(default_factory=lambda: default		,		metadata=lowercase__	)
  
  
  
  
  
  @dataclass
  class 			A							:
  			lowerCamelCase							:							str       		=  field(
  			    metadata={"""help""": """The csv file to plot."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Disable logarithmic scale when plotting"""}		,						)
  			lowerCamelCase							:							bool       		=  field(
  			    default=__UpperCAmelCase		,						metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }		,						)
  			lowerCamelCase							:							Optional[str]       		=  field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}		,						)
  			lowerCamelCase							:							Optional[List[str]]       		=  list_field(
  			    default=__UpperCAmelCase		,						metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}				)
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												int(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  def  _A		(							lowercase__	):
  						try:
  												float(lowercase__	)
  												return True
  						except ValueError:
  												return False
  
  
  
  
  
  class 			A							:
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       )			->		int:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       args
  									lowercase__							 =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}       )
  
  									with open(self.args.csv_file       ,     newline=""""""       ) as csv_file:
  															lowercase__							 =       csv.DictReader(lowerCamelCase__       )
  															for row in reader:
  																					lowercase__							 =       row["""model"""]
  																					self.result_dict[model_name]["bsz"].append(int(row["""batch_size"""]       )       )
  																					self.result_dict[model_name]["seq_len"].append(int(row["""sequence_length"""]       )       )
  																					if can_convert_to_int(row["""result"""]       ):
  																											# value is not None
  																											lowercase__							 =       int(row["""result"""]       )
  																					elif can_convert_to_float(row["""result"""]       ):
  																											# value is not None
  																											lowercase__							 =       float(row["""result"""]       )
  
  
  
  			def A__ (					self       )			->		Tuple:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__ ,						lowercase__							 =       plt.subplots()
  									lowercase__							 =       """Time usage""" if self.args.is_time else """Memory usage"""
  									lowercase__							 =       title_str + """ for training""" if self.args.is_train else title_str + """ for inference"""
  
  									if not self.args.no_log_scale:
  															# set logarithm scales
  															ax.set_xscale("""log"""       )
  															ax.set_yscale("""log"""       )
  
  									for axis in [ax.xaxis, ax.yaxis]:
  															axis.set_major_formatter(ScalarFormatter()       )
  
  									for model_name_idx, model_name in enumerate(self.result_dict.keys()       ):
  															lowercase__							 =       sorted(set(self.result_dict[model_name]["""bsz"""]       )       )
  															lowercase__							 =       sorted(set(self.result_dict[model_name]["""seq_len"""]       )       )
  															lowercase__							 =       self.result_dict[model_name]["""result"""]
  
  															((lowercase__) ,						(lowercase__))							 =       (
  															    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  															)
  
  															lowercase__							 =       (
  															    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
  															)
  
  															for inner_loop_value in inner_loop_array:
  																					if self.args.plot_along_batch:
  																											lowercase__							 =       np.asarray(
  																											    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]       ,     dtype=lowerCamelCase__       ,     )
  																					else:
  																											lowercase__							 =       np.asarray(
  																											    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]       ,     dtype=np.floataa       ,     )
  
  																					((lowercase__) ,						(lowercase__))							 =       (
  																					    ("""batch_size""", """len""") if self.args.plot_along_batch else ("""in #tokens""", """bsz""")
  																					)
  
  																					lowercase__							 =       np.asarray(lowerCamelCase__       ,     lowerCamelCase__       )[: len(lowerCamelCase__       )]
  																					plt.scatter(
  																					    lowerCamelCase__       ,     lowerCamelCase__       ,     label=F'''{label_model_name} - {inner_loop_label}: {inner_loop_value}'''       )
  																					plt.plot(lowerCamelCase__       ,     lowerCamelCase__       ,     """--"""       )
  
  															title_str += F''' {label_model_name} vs.'''
  
  									lowercase__							 =       title_str[:-4]
  									lowercase__							 =       """Time in s""" if self.args.is_time else """Memory in MB"""
  
  									# plot
  									plt.title(lowerCamelCase__       )
  									plt.xlabel(lowerCamelCase__       )
  									plt.ylabel(lowerCamelCase__       )
  									plt.legend()
  
  									if self.args.figure_png_file is not None:
  															plt.savefig(self.args.figure_png_file       )
  									else:
  															plt.show()
  
  
  
  
  
  def  _A		(							):
  						lowercase__							 =       HfArgumentParser(lowercase__	)
  						lowercase__							 =       parser.parse_args_into_dataclasses()[0]
  						lowercase__							 =       Plot(args=lowercase__	)
  						plot.plot()
  
  
  if __name__ == "__main__":
  				main()
  
  
  
  
  
  
  
style_context: 
  '''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 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							PyTorchBenchmarkArguments
  			lowerCamelCase							:							PretrainedConfig
  			lowerCamelCase							:							str       		=  "PyTorch"
  
  
  
  			@property
  			def A__ (					self       )			->		Union[str, Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return torch.__version__
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_inference_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_inference       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		float:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_speed(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		[Memory, Optional[MemorySummary]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       self._prepare_train_func(lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )
  									return self._measure_memory(_train       )
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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__       )			->		Callable[[], None]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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__       )			->		float:
  
  
  
  
  									'''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(
  																					    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=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(lowerCamelCase__       ) / 10.0
  									except RuntimeError as e:
  															self.print_fn(F'''Doesn\'t fit on GPU. {e}'''       )
  															return "N/A"
  
  
  
  			def A__ (					self       ,     lowerCamelCase__       )			->		[Memory, MemorySummary]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									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
  
  
  
  
  
  
  