code: 
  '''simple docstring'''
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						return a + b
  
  
  if __name__ == "__main__":
  				__A          =    5
  				__A          =    6
  				print(F'''The sum of {a} + {b} is {add(a, b)}''')
  
  
  
  
  
  
  
style_context: 
  '''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()
  
  
  
  
  
  
  