code: 
  
  
  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 							UpperCAmelCase				(     UpperCAmelCase=None				,UpperCAmelCase=None      )->		Any:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					return field(default_factory=lambda: default				,metadata=__lowerCAmelCase      )
  
  @dataclass
  class 						snake_case			:
  			'''simple docstring'''
  
  			UpperCAmelCase       :       str       = field(
  			    metadata={"""help""": """The csv file to plot."""}	, )
  			UpperCAmelCase       :       bool       = field(
  			    default=A__	, metadata={"""help""": """Whether to plot along batch size or sequence length. Defaults to sequence length."""}	, )
  			UpperCAmelCase       :       bool       = field(
  			    default=A__	, metadata={"""help""": """Whether the csv file has time results or memory results. Defaults to memory results."""}	, )
  			UpperCAmelCase       :       bool       = field(
  			    default=A__	, metadata={"""help""": """Disable logarithmic scale when plotting"""}	, )
  			UpperCAmelCase       :       bool       = field(
  			    default=A__	, metadata={
  			        """help""": """Whether the csv file has training results or inference results. Defaults to inference results."""
  			    }	, )
  			UpperCAmelCase       :       Optional[str]       = field(
  			    default=A__	, metadata={"""help""": """Filename under which the plot will be saved. If unused no plot is saved."""}	, )
  			UpperCAmelCase       :       Optional[List[str]]       = list_field(
  			    default=A__	, metadata={"""help""": """List of model names that are used instead of the ones in the csv file."""}	)
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		List[str]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					try:
  										int(__lowerCAmelCase      )
  										return True
  					except ValueError:
  										return False
  
  
  
  
  
  
  
  def 							UpperCAmelCase				(     UpperCAmelCase      )->		Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					try:
  										float(__lowerCAmelCase      )
  										return True
  					except ValueError:
  										return False
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     Tuple							,							lowerCAmelCase_     :     str					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       args
  								SCREAMING_SNAKE_CASE_	      =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}					)
  
  								with open(self.args.csv_file							,							newline=''''''					) as csv_file:
  													SCREAMING_SNAKE_CASE_	      =       csv.DictReader(lowerCAmelCase_					)
  													for row in reader:
  																		SCREAMING_SNAKE_CASE_	      =       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
  																							SCREAMING_SNAKE_CASE_	      =       int(row['''result''']					)
  																		elif can_convert_to_float(row['''result''']					):
  																							# value is not None
  																							SCREAMING_SNAKE_CASE_	      =       float(row['''result''']					)
  
  
  
  
  			def _lowercase (	self     :     Optional[int]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       plt.subplots()
  								SCREAMING_SNAKE_CASE_	      =       "Time usage" if self.args.is_time else "Memory usage"
  								SCREAMING_SNAKE_CASE_	      =       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()					):
  													SCREAMING_SNAKE_CASE_	      =       sorted(set(self.result_dict[model_name]['''bsz''']					)					)
  													SCREAMING_SNAKE_CASE_	      =       sorted(set(self.result_dict[model_name]['''seq_len''']					)					)
  													SCREAMING_SNAKE_CASE_	      =       self.result_dict[model_name]["result"]
  
  													(SCREAMING_SNAKE_CASE_)	      =       (
  													    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
  													)
  
  													SCREAMING_SNAKE_CASE_	      =       (
  													    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:
  																							SCREAMING_SNAKE_CASE_	      =       np.asarray(
  																							    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]							,							dtype=lowerCAmelCase_							,							)
  																		else:
  																							SCREAMING_SNAKE_CASE_	      =       np.asarray(
  																							    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]							,							dtype=np.floataa							,							)
  
  																		(SCREAMING_SNAKE_CASE_)	      =       (
  																		    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
  																		)
  
  																		SCREAMING_SNAKE_CASE_	      =       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.'''
  
  								SCREAMING_SNAKE_CASE_	      =       title_str[:-4]
  								SCREAMING_SNAKE_CASE_	      =       "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 							UpperCAmelCase				(     )->		int:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       HfArgumentParser(__lowerCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       parser.parse_args_into_dataclasses()[0]
  					SCREAMING_SNAKE_CASE_	      =       Plot(args=__lowerCAmelCase      )
  					plot.plot()
  
  
  if __name__ == "__main__":
  							main()
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			def __init__(	self     :     int							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       value
  								SCREAMING_SNAKE_CASE_	      =       None
  								SCREAMING_SNAKE_CASE_	      =       None
  
  class 						snake_case			:
  			'''simple docstring'''
  
  			UpperCAmelCase       :       int
  
  			def __init__(	self     :     Union[str, Any]					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       0
  
  			def _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int					)					->					None:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return
  
  								if path_sum == self.target:
  													self.paths += 1
  
  								if node.left:
  													self.depth_first_search(node.left							,							path_sum + node.left.value					)
  								if node.right:
  													self.depth_first_search(node.right							,							path_sum + node.right.value					)
  
  
  
  
  			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     Node | None							,							lowerCAmelCase_     :     int | None = None					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if node is None:
  													return 0
  								if target is not None:
  													SCREAMING_SNAKE_CASE_	      =       target
  
  								self.depth_first_search(lowerCAmelCase_							,							node.value					)
  								self.path_sum(node.left					)
  								self.path_sum(node.right					)
  
  								return self.paths
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  